Proteomics in clinical interventions: Achievements and limitations in biomarker development

Life Sciences 80 (2007) 1345 – 1354 www.elsevier.com/locate/lifescie

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Proteomics in clinical interventions: Achievements and limitations in biomarker development Ashima Sinha, Chetna Singh, Devendra Parmar, Mahendra Pratap Singh ⁎ Industrial Toxicology Research Centre (ITRC), Lucknow-226 001, India Received 14 August 2006; accepted 12 December 2006

Abstract Development of toxicological and clinical biomarkers for disease diagnosis, quantification of toxicant/drug responses and rapid patient care are major concerns in modern biology. Even after human genome sequencing, identification of specific molecular signatures for unambiguous correlation with toxicity and clinical interventions is a challenging task. Differential protein expression patterns and protein–protein interaction studies have started unraveling rigorous molecular explanation of multi-factorial and toxicant borne diseases. Proteome profiling is extensively used to investigate etiology of diseases, develop predictive biomarkers for toxicity and therapeutic interventions and potential strategies for treatment of complex and toxicant mediated diseases. In this review, achievements and limitations of proteomics in developing predictive biomarkers for toxicological and clinical interventions have been discussed. © 2007 Elsevier Inc. All rights reserved. Keywords: Proteomics; Predictive biomarkers; Toxicity biomarkers; Clinical biomarkers

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . Biomarkers for toxicity assessment . . . . . . . . . . Gastrointestinal toxicity biomarkers . . . . . . . Reproductive toxicity biomarkers . . . . . . . . . Renal toxicity biomarkers. . . . . . . . . . . . . Cardiovascular toxicity biomarkers . . . . . . . . Skeletal toxicity biomarkers . . . . . . . . . . . Neuronal toxicity biomarkers . . . . . . . . . . . Clinical biomarkers . . . . . . . . . . . . . . . . . . Biomarkers for cancer . . . . . . . . . . . . . . Biomarkers for diagnosis . . . . . . . . . . . . . Biomarkers for classification . . . . . . . . . . . Biomarkers for therapeutic intervention. . . . . . Neuronal biomarkers . . . . . . . . . . . . . . . Cardiovascular biomarkers . . . . . . . . . . . . Limitations of proteomics in biomarker development Concluding remarks . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . .

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⁎ Corresponding author. Industrial Toxicology Research Centre (ITRC), M. G. Marg, Post Box-80, Lucknow-226 001, UP, India. Tel.: +91 522 2613618x337; fax: +91 522 2628227. E-mail address: [email protected] (M.P. Singh). 0024-3205/$ - see front matter © 2007 Elsevier Inc. All rights reserved. doi:10.1016/j.lfs.2006.12.005

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Introduction Recent molecular tools that include proteomics have erased current distinctions among fields of pathology, toxicology and molecular genetics (Aardema and MacGregor, 2002). Following human genome sequencing, biologists initiated multidimensional approaches to identify complete protein sets to understand differential expression, post-translational modifications, protein–protein interactions and structural aspects. Technological innovations in proteome diagnostics have improved toxicity assessment of drugs, chemicals and environmental toxicants. Proteomics offers analysis of complete proteome profiling to understand the molecular mechanism of complex multi-factorial and toxicant borne diseases. High throughput methods, including two-dimensional gel electrophoresis, mass spectrometry and bio-informatics are being used to provide molecular explanation of diseases and disorders, ranging from cancer to neuronal diseases and organ failure to organ transplantation (Clarke et al., 2003). Protein microarrays, on the other hand, are unraveling unique biomarkers for toxicity assessments and clinical interventions. These technologies simultaneously and comprehensively examine changes in large number of proteins and provide solutions to difficult clinical and toxicological interventions (Clarke et al., 2003). Proteome profiling is extensively used for identification and monitoring of specific biomarkers in biological fluids viz. blood, cerebrospinal fluid, saliva, sputum and urine and revolutionized the way of diagnosis, treatment and prevention of multi-factorial and toxicant borne diseases. Development of biomarkers and search for novel drug targets became easier after advent of modern proteome profiling tools since disease progression leads to alteration in protein expression patterns. Analysis of disease and toxicity specific proteome profiles allows better understanding for disease progression and identification of novel therapeutic targets. Proteome based diagnostic techniques are used to develop therapeutic strategies for toxicity assessment and therapeutic interventions. Biomarker development includes identification, prioritization and validation in pre-clinical models to examine their usefulness and applications. Despite all of its potential, proteomics based experiments must be carefully designed in order to differentiate true clinical and toxicological variation in protein expression patterns from differences those originated during sample collection, experimentation and normal biological variability (Clarke et al., 2003). In this review, an emphasis on achievements and limitations of proteomics in developing clinical and toxicity biomarkers has been given. Biomarkers for toxicity assessment Identification of toxicant responsive proteins provides better means of toxicity assessment, toxicant classification and exposure monitoring than current indicators (Merrick and Bruno, 2004). Proteomics based techniques are used for the development and validation of toxicity biomarkers for heavy

metals, polycyclic aromatic hydrocarbons and other environmental pollutants and toxicants. Proteomic based approaches developed new families of biomarkers that permitted characterization and efficient monitoring of cellular perturbations, provided an increased understanding of the influence of genetic variation on toxicological outcomes and allowed definition of environmental causes of genetic alterations and their relationship to human disease (Aardema and MacGregor, 2002). Assessment of toxicity of drugs and environmental chemicals in target tissue are being done using several tools ranging from 2D-PAGE to protein microarrays. Proteome profiling predicts toxicity response and resistance of several anticancer, cardiovascular and anti-inflammatory drugs. Proteome profiling is also used to develop biomarkers for chemical toxicity (Merrick and Bruno, 2004). A plethora of biomarkers are reported for diagnosis of tissue damage such as aspartate aminotransferase and alanine aminotransferase for liver and heart, collagen for joints, amyloid plaque and peptide for neurodegeneration (Collins et al., 2006). The major applications of proteomics in toxicological and clinical interventions are summarized in Fig. 1. Integration of transcriptomics, proteomics and toxicology datasets permitted in silico biomarker and signature pattern discovery for specific chemical toxicants affecting target organs for specific disease models and for unique chemical-protein adducts underlying cellular injury (Merrick and Bruno, 2004). Birth defects systems manager, an open resource system, facilitated analysis and discovery in developmental biology, delineated pathways and biological regulatory networks for specific chemicals or classes of developmental toxicants, developed novel biomarkers indicative of exposure and/or prediction of adverse effects and integrated modern computing and information technology with data obtained from molecular biology (Knudsen et al., 2005). Gastrointestinal toxicity biomarkers Proteomic tools are being applied for identification and confirmation of peripheral biomarkers for altered liver function following toxicant exposure (Amacher et al., 2005). Methapyrilene, cyproterone acetate and dexamethasone-like model hepatotoxins induce distinct changes in rat liver proteome profile, that are used to classify treatment strategies and utilize them as molecular signature for the exploration of molecular mechanisms of toxicity (Man et al., 2002). Up-regulation of pyruvate dehydrogenase, phenylalanine hydroxylase and 2oxoisovalerate dehydrogenase and down-regulation of sulfite oxidase, chaperone-like protein, glucose-regulated protein 78, serum paraoxonase, serum albumin, and peroxiredoxin IV were correlated with clinical and histological data before and after onset of biochemical changes (Meneses-Lorente et al., 2004). The study clearly suggested the use of protein patterns as predictive biomarkers for compounds with a propensity to induce liver steatosis (Meneses-Lorente et al., 2004). LPStreated mouse liver displayed a time-dependent alteration in liver injury and repair related protein expression patterns (Liu et al., 2004). Vitamin D-binding protein, paraoxonase, cellular

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Fig. 1. Summary of role played by proteomics based biomarkers in toxicology and clinical interventions. (Merrick and Bruno, 2004; Amacher et al., 2005; Rockett et al., 2004; Dare et al., 2002; Higenbottam et al., 2004; Kennedy, 2002; Patrick, 2006).

retinol-binding protein, malate dehydrogenase, F-protein and purine nucleoside phosphorylase were identified as serum biomarkers for hepatic effects in drug-treated rats (Amacher et al., 2005). BMS-PTX-265 and BMS-PTX-837 proteins were found reproducibly and significantly increased in cells treated with toxicants in conditioned media that supported use of preclinical in vitro methods in identifying and validating new biomarkers for liver toxicity (Gao et al., 2004). Reproductive toxicity biomarkers Surrogate tissue analyses incorporating contemporary genomic and proteomic tools are used for determination of toxicant exposure and effect, disease state and identification of target tissues at early clinical stage (Rockett et al., 2004). Effects of male reproductive toxicants on protein expression patterns in rat testis identified potential biomarker candidates for reproductive toxicants such as ethylene glycol monomethyl ether, cyclophosphamide, sulfasalazine and 2,5-hexanedione (Yamamoto et al., 2005). Reproductive toxicants showed differential expression of several proteins, including glutathione S-transferase, testis-specific heat shock protein 70-2, glyceraldehyde3-phosphate dehydrogenase and phosphatidylethanolaminebinding protein that could potentially serve as biomarkers to evaluate male reproductive toxicity at an early stage of drug discovery and development since protein alteration could be detected before appearance of histopathological changes (Yamamoto et al., 2005).

Renal toxicity biomarkers Several observations highlighted identification of new biomarkers and provided new unprecedented insights into complex biological mechanisms (Bandara et al., 2003). 4Aminophenol (4-AP) and D-serine led to an expression of fumarylacetoacetate hydrolase (FAH), a toxicity-associated plasma protein, required for tyrosine metabolism. FAH can be used as a biomarker for 4-AP and D-serine induced kidney injury (Bandara et al., 2003). Metabolomic and proteomic biomarkers for III–V semiconductors clearly suggested the use of urinary proteins as potential early biomarkers for renal damage as compared with intracellular proteotoxicity markers in kidney tubule cells (Fowler et al., 2005). Cardiovascular toxicity biomarkers Proteomic approaches are used to evaluate cumulative and synergistic toxicity assessments in complex conditions such as chemical exposure. Sensitive biomarkers are developed for cadmium and arsenic co-exposure induced renal dysfunction that revealed how renal damage is more pronounced in coexposed condition as compared with individual exposure (Nordberg et al., 2005). The up-regulation of eight proteins and down-regulation of haptoglobin in automobile emission inspectors and waste incineration workers were also reported (Kim et al., 2004). Additionally, serum paraoxonase was found in plasma of waste incineration workers (Kim et al., 2004).

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Table 1 A list of proteins developed as biomarkers either alone or in combination with other proteins for assessment of toxicity of environmental chemicals/drugs Biomarkers

Toxicants

References

Glial fibrillary acid protein

Neurotoxins

Transthyretin, sarcolectin and haptoglobin Glutathione S-transferase, testis-specific heat shock protein 70-2, glyceraldehyde phosphate dehydrogenase and phosphotidylethanolamine-binding protein Glucose and lipid metabolizing enzymes and oxidative stress related proteins Fumarylacetoacetate hydrolase, a toxicity-associated plasma protein Pyruvate dehydrogenase, phenylalanine hydroxylase and 2-oxoisovalerate dehydrogenase, down regulation of sulfite oxidase, chaperone-like protein, glucose-regulated protein 78, serum paraoxonase, serum albumin, and peroxiredoxin IV Urinary parvalbumin-alpha

Automobile emission and waste incineration Ethylene glycol monomethyl ether, cyclophosphamide, sulfasalazine and 2,5-hexanedione

Gramsbergen and van den Berg (1994) Kim et al. (2004) Yamamoto et al. (2005)

Hydrazine exposure Aminophenol (4-AP) and D-serine, rodent nephrotoxins Steasis causing hepatotoxins

Kleno et al. (2004) Bandara et al. (2003) Meneses-Lorente et al. (2004)

Skeletal muscle toxicity

Dare et al. (2002)

The table gives a limited selection of biomarkers for the diagnosis of the effect of chemicals.

Transthyretin, sarcolectin and haptoglobin could serve as biological monitoring markers for automobile emission and waste incineration exposure (Kim et al., 2004). A single dose of hydrazine causes alterations in proteins related to glucose metabolism, lipid metabolism and oxidative stress and proteomic tools helped in the development of potential biomarkers for hydrazine-induced toxicity (Kleno et al., 2004). Skeletal toxicity biomarkers Proteomics offered development of biomarkers for skeletal toxicity assessments. Compound-induced changes in urine samples using surface enhanced laser desorption ionization time of flight (SELDI-TOF) demonstrated urinary parvalbuminalpha as a specific, potential, noninvasive and easily detectable biomarker for skeletal muscle toxicity in rats (Dare et al., 2002). Neuronal toxicity biomarkers

and cerebrospinal fluid biomarkers. Protein microarrays also represent a powerful tool to identify and characterize group of proteins involved in tumor progression. Proteomics also offers potential for tracing mechanism of complex diseases, differential diagnosis, classification of diseases and therapeutic monitoring. Proteomics in combination with functional imaging, biosensors and computational biology generated an unprecedented impact on discovery and development of RNA-based gene expression profiles for prediction of response, resistance and toxicity of both new and existing anticancer, cardiovascular, and anti-inflammatory drugs (Ross et al., 2005). Protein microarrays detect different binding levels among the patient classes reflected inflammation (high C-reactive protein, alpha-1 anti-trypsin, serum amyloid A), immune response (high immunoglobulin A), leakage of cell breakdown products (low plasma gelsolin) and possibly altered vitamin K usage or glucose regulation (high protein induced vitamin K antagonistII) were reviewed (Omenn, 2006). Biomarkers for cancer

Proteins involved in neurodegeneration, neuroplasticity, metabolism and energy transfer viz. gap-43/neuromodulin, stathmin, alpha-enolase, gamma-enolase, ATP synthase, H+ transporting mitochondrial F1 complex, beta subunit (Atp5b) and alpha-synuclein were found altered in striatum and hippocampus following 2,2′,4,4′,5-pentabromodiphenyl ether (PBDE-99) exposure. Early exposure to PBDE-99 contributed persistent neurotoxic effects and led to the identification of potential biomarkers for developmental neurotoxicity caused by organohalogen compounds (Alm et al., 2006). Several oxidative stress and metabolism related protein biomarkers are developed, however comprehensive analysis of defined proteins is necessary to develop more sensitive toxicity biomarker (Alm et al., 2006). Some biomarkers developed in toxicology using proteomics based approaches are summarized in Table 1.

The diagnosis of cancer at an early stage is difficult due to complex structure and frequent modification in participating proteins in response to environmental and other stresses and proteomics helped a lot in this direction. Marker proteins are released into blood and other body fluids from cancerous tissues. Model systems are extensively used to find out novel proteins in blood and other body fluids associated with target tissues e.g., ovarian cancer (Rockett et al., 2004). Biomarkers are developed in blood, body fluids, tissues and target organs for cancer diagnosis, classification and therapeutic interventions. Proteomic tools are used to identify tumors at an early stage and therefore, patients safely avoid surgery or radiation therapy (Coombes et al., 2005).

Clinical biomarkers

Biomarkers for diagnosis

2D gel electrophoresis in combination with mass spectrometry allowed identification of cancer, cardiovascular, neurodegenerative and other complex disease-specific plasma/serum

Proteomics provided enormous data-gathering capabilities by developing several clinical biomarkers in serum, plasma, urine, tissues and other biological samples. Serum proteome

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profile is used to monitor the development and progression of ovarian and colon cancers (Rockett et al., 2004; Albrethsen et al., 2005). In addition to proteome profiles, proteolytic degradation patterns in serum peptidome detect cancer and distinguish indolent and aggressive tumors (Villanueva et al., 2006). A reproducible mass-spectrometry approach to identify tumor-specific peptidome patterns in serum samples was developed along with an automated procedure for simultaneous measurement of peptides in serum (Villanueva et al., 2006; Kristine, 2006). A total of 61 cancer and cancer type specific signature breakdown products were measured for biomarker development and prostate-specific antigen and cancer antigen125 were developed as biomarkers for cancer (Villanueva et al., 2006). Several biomarkers associated with different types of cancers were identified that include psoriasin for bladder squammous cell carcinoma, prostate cancer antigen-1 and calgranulin B/MRP-14 for prostate cancer, combination of five novel proteins and seven protein clusters for transitional cell carcinoma, defensin for bladder cancer and matrix metalloproteinases-2,-4, fibronectin and their fragments for general cancers (Gonzalez-Buitrago et al., 2007). Human neutrophil peptides-1, -2 and -3 (HNP 1–3), also known as alfa-defensin-1, -2 and -3, found elevated in serum of colon cancer patients and are used as blood markers for colon cancer in combination with other diagnostic tools (Albrethsen et al., 2005). Peripheral blood lymphocytes (PBLs) are used as a source of genetic biomarkers to monitor radiation exposure. Gene expression analysis of PBLs and hair follicles for monitoring impact of toxicants on inaccessible organs and characterization of disease-associated gene signatures in peripheral blood mononuclear cells (PBMCs) of renal cell carcinoma (RCC) patients are already reported (Rockett et al., 2004). Computational methods can predict function of gene products based on expression patterns, analysis of gene promoter sequence and protein–protein interactions (Troyanskaya et al., 2003). Immuno-1 ERBB2 assay was used to analyse serum samples and a significant difference in serum ERBB2 concentrations in patients with and without metastasis (Osman et al., 2005). The risk of cause-specific death was associated with serum ERBB2 and test was used to identify potential candidates for antiERBB2 therapy (Osman et al., 2005). Gene expression profiling also identified few differentially expressed genes in leukaemic subtypes with poor prognosis and decreased tendency to undergo apoptosis (Holleman et al., 2006). Proteomic analyses of carcinogen exposed liver showed significant up-regulation of cellular stress responsive proteins that include, superoxide dismutase, heat shock protein 60 and peroxiredoxin (Fella et al., 2005). Caspase-8 precursor, vimentin, Rho GDP dissociation inhibitor were identified in rat liver bearing malignant, transformed cells following 18 weeks after carcinogen withdrawal (Fella et al., 2005). Annexin A5 and fructose-1, 6-bisphosphatase were deregulated after three weeks of exposure indicating their potential usefulness as early predictive biomarkers for liver carcinogenesis (Fella et al., 2005). 1D-myo-inositol 1,4,5-trisphosphate 3-kinase A (ITPKA) acts as potential biomarker for cellular differentiation and intracel-

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lular Ca2+ homeostasis in human oral squamous cell carcinomas (Kato et al., 2006). Daunorubicin (DRC), a cytotoxic agent, upregulated the expression of some proteins in a dose-dependent manner in pancreas carcinoma (Moller et al., 2001). Biomarkers for classification Gene and protein expression patterns are used to classify tumors and cancers into subgroups with prognostic significance (Moyano et al., 2006). An automated analysis based on expression profile demonstrated that combinations of individual apoptosis and differentiation genes could be used to discriminate various tumors and treatment strategies. HER2/NEU is best characterized in patients with metastatic androgenindependent prostate cancer and alpha-B crystallin, a stressresponse protein with oncogenic potential, is used for identification of an aggressive subtype of human basal-like breast tumor (Osman et al., 2005; Moyano et al., 2006). Biomarkers for therapeutic intervention Gene-expression profiling has also discovered potential biomarkers for therapeutic responses (Luthra et al., 2006). An independent cohort of 92 patients treated with same drugs showed BCL2L13 as a single gene, independently associated with treatment outcome (Holleman et al., 2006). BCL2L13 had pro-apoptotic activity in cell lines and was associated with drug resistance (Holleman et al., 2006). BCL2L13 plays a different apoptotic role in primary leukaemic cells and acts as an antiapoptotic splice variant, however, a prospective validation is required to establish BCL2L13 expression as a true prognostic factor (Holleman et al., 2006). Experimental and computational approaches in model systems identified proliferation-signatureproteins in improved anti-cancer treatments (Troyanskaya et al., 2003). Expression of alphaB-crystallin in breast cancer cohorts was used as a biomarker for prognosis or drug response against basal-like breast tumor (Moyano et al., 2006). Expression of alphaB-crystallin also indicated use of MEK inhibitors for treatment of basal-like breast tumors (Moyano et al., 2006). Localized oesophageal carcinoma treated with pre-operative chemoradiotherapy and outcome of intervention was found difficult to predict because of undesirable consequences (Luthra et al., 2006). Similarly, molecular signatures in oesophageal cancer were developed and correlated with treatment response in a small sample; however, validation with larger sample size needs to be performed before arriving at any conclusion (Luthra et al., 2006). Neuronal biomarkers Clinical proteomics, used for discovery of biomarkers in blood and cerebrospinal fluid, reflected central pathogenic processes of Alzheimer's and Parkinson's diseases. Protein biomarkers in serum for Alzheimer's disease (AD), Parkinson's disease (PD) and amyotrophic lateral sclerosis (ALS) using quantitative 2D gel electrophoresis are being developed using proteomics. Proteomics reflected differential expression of

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proteins in brain under healthy and diseased conditions. Protein arrays also provided a more global picture of biological processes involved in synaptic plasticity that selectively altered in brain of cases at high risk (Ho et al., 2005). Proteomic approach was used to study sequential changes of distinctive gene expression patterns in the brain as a function of the progression of AD dementia (Ho et al., 2005). Protein sequence analysis identified a 13.4-kDa protein species as cystatin C and a 4.8-kDa protein species as a peptic fragment of the neurosecretory protein VGF (Pasinetti et al., 2006). Application of a “three-protein” biomarker model as a current diagnostic criteria provided an objective biomarker pattern to identify patients with ALS (Pasinetti et al., 2006). Detection of new biomarkers in brain proteome further strengthened diagnosis and provided useful information in drug trials in mouse models (Pasinetti et al., 2006). Large-scale identification of protein–protein interactions using automated yeast two-hybrid screening created comprehensive protein interaction maps to characterize proteins and to understand the regulatory processes (Suopanki et al., 2006). Protein–protein interaction network provided clues for early dysfunction in Huntington's disease (HD) (Suopanki et al., 2006). Several uncharacterized proteins and a G proteincoupled receptor kinase interacting protein, GIT1 were functionally annotated. GIT1 present in neuronal inclusions in HD patient's brain suggested its involvement in disease pathogenesis. VCP/p97, carboxyl terminus of Hsp70-interacting protein (CHIP) and amphiphysin II interaction partners using membrane-based human proteome arrays were developed (Grelle et al., 2006). An interaction with ataxin-2 and endophilin in plastin-associated pathways in HD was reported (Ralser et al., 2005). Molecular markers derived from proteomic analysis also offered best prospect for developing molecular diagnostic tests for complex neurodegenerative disorders such as AD (Choe et al., 2002). Cell-based and in vitro drug screening assays were developed for the identification of small molecules that delay aggregate formation (Suopanki et al., 2006). Lead compounds have been identified and tested for activity in different transgenic in vivo model systems (Suopanki et al., 2006). Selected biomarkers were used to address unmet pressing needs in the differential diagnosis of diseases to provide potential avenues for mechanism-based drug targeting and to monitor treatment strategies in large cohorts of targeted patient blood serum samples and complimentary age-matched controls (Sheta et al., 2006). Proteomics has potential for pathway measurement in blood for differential diagnosis, disease burden and therapeutic monitoring (Sheta et al., 2006). Cardiovascular biomarkers Increasing incidence of cardiovascular diseases, specifically atherosclerosis and heart failure, prioritized the search for novel biomarkers. Biomarkers are needed for the diagnosis, prognosis, therapeutic monitoring and risk stratification of acute myocardial infarction (AMI) and heart failure, leading causes of mortality and morbidity (Stanley et al., 2004). Proteomics

allowed protein identification, characterization, expression and post-translational modification assessment involved in cardiovascular diseases and development of suitable biomarkers (Gallego-Delgado et al., 2005). Genetic markers have enormous potential for identification of cardiovascular disease at an early stage of onset (Gibbons et al., 2004). Functional proteomics of a protein complex-based portrait in cardiac cell signaling examined and briefly evaluated and analyzed multiprotein complex that showed several advantages over classical methods (Vondriska and Ping, 2003). The investigators involved in developing biomarkers for acute cardiac disease have made a remarkable progress. Troponin B-type, natriuretic peptide and C-reactive proteins are increasingly moving towards more productive clinical use in cardiac diseases (Jaffe et al., 2006). Directed proteomics approach identified a set of 177 candidate biomarker proteins associated with cardiovascular diseases. Although more than 1000 proteins were detected in plasma or serum, a paradoxical decline occurred in the number of new protein markers approved for diagnostic use in clinical laboratories (Anderson, 2005). Plasma proteome project pilot phase examined human plasma with distinct proteomic approaches and identified 3020 proteins that were organized into eight groups: markers of inflammation and/or cardiovascular disease, vascular and coagulation, signaling, growth and differentiation, cytoskeletal, transcription factors, channels/receptors and heart failure and remodeling (Berhane et al., 2005). Analysis of the MS/MS data revealed group-specific trends that serve as a resource to interrogate functions of plasma proteins (Berhane et al., 2005). List of cardiovascular-related proteins in plasma constitutes a baseline blueprint for the development of bio-signatures for diseases such as myocardial ischemia and atherosclerosis (Berhane et al., 2005). New potential biological markers of cardiovascular disease were developed using human plaques proteins secreted from pathologic and normal vessel wall and left ventricular hypertrophy (Gallego-Delgado et al., 2005). An albumin binding protein was examined and validated in myocytes/ tissue/serum/plasma using MALDI-TOF MS/MS for development of cardiac biomarker (Stanley et al., 2004). Affinity SELDI was used to monitor the disease-induced posttranslational modification and ternary status of myoctyeoriginating protein, cardiac troponin I in serum (Stanley et al., 2004). Potential markers specific to a disease or to changes in the surrounding tissue were discovered in isolated perfused organ effluent (Koomen et al., 2006). Proteomic analysis of effluent fractions collected from isolated beating rat hearts during reperfusion after brief episodes of ischemia detected clinical markers for myocardial ischemia and validated potential of isolated organ perfusion in diagnostic protein discovery (Koomen et al., 2006). Proteomic approaches were used to serve surrogates that include measures of allograft pump function, intragraft histology, peripheral markers, structural protein markers such as cardiac specific troponins, inflammatory markers, fibrogenic markers such as TGF-beta, fibroblast growth factor and immune markers such as anti-HLA Ab and indirect alloantibodies (Mehra

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Table 2 A list of few protein biomarkers developed for identification of neurological, cardiovascular and cancerous disease and disorders Biomarkers of neurological disease

Biomarkers of cardiovascular disease

Biomarkers of cancer

Sulfatide, amyloid precursor, glycerophosphocholine and Tau proteins in CSF for Alzheimer's disease (Flirski and Sobow, 2005; Andreasen and Blennow, 2005; Walter et al., 2004; Fonteh et al., 2006; Sheta et al., 2006) Cystatin C and peptic fragment of the neurosecretory protein VGF for Alzheimer's disease (Pasinetti et al., 2006) Protein kinase C in red blood cells for Alzheimer’s disease (Janoshazi et al., 2006) CSF cystatin C and matrix metalloproteinases in serum for multiple sclerosis (Bollengier, 1987; Avolio et al., 2003) “Three protein” biomarker for ALS (Pasinetti et al., 2006) C-tau, hyperphosphorylated axonal neuro-filment protein and serum S100B for traumatic brain injury (Zemlan et al., 2002; Shaw et al., 2005; Berger et al., 2002)

Lipoprotein associated phospholipase-A2, intracellular adhesion molecule 1, PARK7 and nucleoside diphosphate kinase-A for stroke (Baird, 2006; Keaney et al., 2004; Allard et al., 2005) Troponin, natriuretic peptide, creatine kinase, myoglobin and fatty acid binding protein for ischemic heart disease (Lindahl et al., 1995; Staub et al., 2006; Stanley et al., 2004; Berhane et al., 2005; Tang et al., 2004) G protein-coupled receptor kinase-2 for congestive heart failure (Iaccarino et al., 2005) Adipocyte-enhancer binding protein, lipid-modified proteins and lipid-phospholipase-A2 for artherosclerotic heart disease (Majdalawieh et al., 2006; Holvoet, 1998; Macphee and Nelson, 2005; Berhane et al., 2005)

HER-2/neu oncoprotein and tumor-specific glycoproteins for breast cancers (Osman et al., 2005) Hypoxia-inducible factor-1 in head and neck cancer, epithelial membrane protein-1 for gefitinib resistance (Shemirani and Crowe, 2002; Jain et al., 2005) Methylation or related biomarkers for drug resistance in cancer (Worm and Guldberg, 2002) Cdk6 and serum CA 15-3 for prognosis in advanced breast cancer (Cover et al., 1998; Al-azawi et al., 2006) Protein kinase C for metastasic breast cancer (Pan et al., 2005; Nagaraja et al., 2006) Receptor protein tyrosine phosphatase-B for gliomas (Akasaki et al., 2006)

et al., 2004). A correlation was established between an increase in H-FABP expression and decrease in PCNA expression that regulates cardiomyocyte growth and differentiation in mouse neonatal hearts (Tang et al., 2004). Proteomic and metabolomic analyses of atherosclerotic vessels from apolipoprotein Edeficient mice revealed alterations and potential associations of immune-inflammatory responses, oxidative stress and energy metabolism (Mayr et al., 2005). Identification of group of proteins present in blood is expected to identify candidates for drug-associated diseases (Higenbottam et al., 2004). A list of some biomarkers developed for toxicity assessment and clinical intervention using proteomics are given in Table 2. Limitations of proteomics in biomarker development Proteomics has opened a path for understanding the underlying mechanism of toxicity and clinical interventions and being used to increase speed and sensitivity of toxicological screening by identifying toxicity biomarkers (Kennedy, 2002). Proteomics provided insights into the mechanism of action of a wide range of substances, from metals to peroxisome proliferators, increased predictability of early drug development and identified non-invasive biomarkers of toxicity or efficacy (Kennedy, 2002). The utilization of safety biomarkers for prediction of compound-related toxicity provided several advantages in drug discovery and development, particularly at an early stage (Yamamoto et al., 2005). Proteomics is being used for understanding and predicting clinical and toxicity responses and therefore has sorted out several major problems in medical sciences and toxicology; however, many obstacles remain to be resolved before biomarkers get widespread practical applications. The major challenge is the discrimination of changes due to interindividual variation, experimental background noise in protein

profiling and post-translational modifications. Despite intensive researches, a very limited number of plasma proteins have been validated as biomarkers for disease (Coombes et al., 2005). Although proteome approaches gave opportunities to define molecular mechanism of toxicity and clinical interventions, but reproducibility in expression depends on experimental conditions across different laboratories, therefore, is still a challenge for proteomics. Despite of extensive efforts in the development of new technologies in proteomics, it is not possible to properly detect hydrophobic proteins and proteins present in very high or minute quantities using proteomics based approaches such as 2D-PAGE, however, fractionating proteins into organelle components increases the probability of detecting low-copy-number proteins. Most of the drug targets are hydrophobic proteins, therefore, application of diagnostic protein expression profiling in a predictive context is still a challenge and needs further investigation. Similarly, increase in automation and development of new techniques in proteomics can control speed of high throughput, another major drawback of proteomics (Kennedy, 2002). Concluding remarks Proteomics created robotic, sophisticated and unified approaches to understand the mechanism of action of drugs, chemicals, toxicants and environmental pollutants and also for biomarker development. Proteomic tools are used to identify and validate drug target, efficacy and toxicity assessments and develop biomarkers in biological fluids. Proteomics based approaches are also being used in clinical trials and protein– protein interaction studies that influence disease/toxicity onset. Despite of some shortcomings, proteomics is still a major choice of research in biomarker development and validation in toxicity and clinical interventions.

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