The Potential Of Protein-Detecting MicroArrays For Clinical Diagnostics

ADVANCES IN CLINICAL CHEMISTRY, VOL.

38

THE POTENTIAL OF PROTEIN-DETECTING MICROARRAYS FOR CLINICAL DIAGNOSTICS Alexandra H. Smith, Jennifer M. Vrtis, and Thomas Kodadek Department of Internal Medicine and Molecular Biology, Center for Biomedical Inventions, University of Texas Southwestern Medical Center, Dallas, Texas 75390

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Diagnostic Signatures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Signature Discovery Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. Signature-Detecting Platforms in the Clinic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Protein-Detecting Microarrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Recent Advances in Protein Ligand Isolation . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Recent Applications of Protein-Detecting Microarrays . . . . . . . . . . . . . . . . . . . 4. Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

217 218 219 223 225 227 232 234 234

1. Introduction The early and accurate detection of disease is an important issue in the development of new medical technology and is crucial for eVective disease treatment. For instance, early stage detection of cancer is crucial for a favorable outcome. In the United States, 72% of lung cancers, 57% of colorectal, and 34% of breast cancers have already metastasized by the time they are detected (G2). Unfortunately, most therapeutics are limited in their eVectiveness once a tumor has invaded surrounding tissue and metastasized throughout the body. Another unsolved problem is to develop diagnostic assays which distinguish diseases with similar symptoms but diVerent pathogenic mechanisms, such as Alzheimer’s disease, Leury-body disease, Creuzfeldt-Jakob disease, frontotemporal dementia (G1), and prognostic subgroups in cancers (J1). 217 Copyright 2004, Elsevier Inc. All rights reserved. 0065-2423/04 $35.00

218

SMITH ET AL.

Diseases are often diagnosed by measuring an endogenous substance or parameter indicative of the disease process. This substance or parameter, which is present in the blood or some other readily available fluid or tissue, is known as a biomarker of disease. Biomarkers are used to diagnose disease, measure disease progression, and monitor the eVects of treatment. In clinical practice, these biomarkers are generally proteins in blood measured by enzyme-linked immunosorbent assay (ELISA), a highly sensitive method to quantify biomarkers using specific antibodies. A well-known biomarker is prostate-specific antigen, which is elevated in the serum of prostate cancer patients (K5) and is used as a diagnostic tool to detect and monitor the treatment of prostate cancer. Currently, most diagnostic assays detect single biomarkers or a small number of genes or proteins strongly induced in response to disease stimuli, such as cytokines. These biomarkers often lack specificity, which precludes an unequivocal diagnosis. Diseases, even at their early stages, elicit many combinations of slight, but significant changes in protein expression and/or activity (W5). Therefore, measuring a combination of biomarkers (hereafter referred to as a diagnostic signature) should be more eVective than a single biomarker. In order to increase the arsenal of diagnostic assays available in the clinic, two main research approaches are required. First, clinically useful diagnostic signatures for specific diseases should be identified and, second, clinically useful platforms must be designed to measure these signatures.

2. Diagnostic Signatures For diagnostic signatures to be relevant in a clinical setting, a number of factors need to be considered. Ideally, clinically useful diagnostic signatures should be measurable in a readily accessible body fluid such as serum, urine, or saliva, making diagnosis noninvasive. A recognizable signature should be evident prior to the onset of clinical symptoms. Early diagnostic signatures would be valuable for monitoring patients for postoperative infection and for population-screening, as prostate-specific antigen is used to screen for prostate cancer. Furthermore, diagnostically useful signatures should be specific for a given disease. Certain signatures may be common to diseases such as cancers or infections, but useful signatures should distinguish between tumor classes and pathogen types. Lastly, signatures are present in a dynamic biological system and normal variation among healthy individuals must be taken into account. Genetic factors, age, gender, time of day, or environmental conditions such as diet and stress all contribute to variation. This issue presents a serious challenge in recognizing diagnostically useful signatures.

PROTEIN-DETECTING MICROARRAYS

219

Researchers must first discover diagnostic signatures which meet these criteria. However, identifying diagnostic signatures will only be useful to the clinician if they can be detected routinely and eYciently. Therefore, an ideal diagnostic signature-detecting system for the clinician’s oYce should allow for on-site analysis for immediate detection or confirmation of a specific disease state, thereby allowing for the necessary targeted treatment. The assay should be sensitive, accurate, and reproducible to prevent false negatives and positives. The system should be inexpensive and easy to use, requiring minimal technical expertise and sample processing to prevent any additional variance. We will focus on progress in detecting clinically useful diagnostic signatures and the development of protein-detecting microarrays to measure these signatures in the clinician’s oYce. 2.1. SIGNATURE DISCOVERY TOOLS The availability of whole genome sequences of organisms starting with a virus (S1), the first bacterium (T4), and the much heralded human genome (L3, V1) has contributed significantly to the understanding of human disease. Analysis of the genome has led to the identification of genetically based diseases and gene variants or polymorphisms that render individuals more susceptible to certain diseases. Depending on developmental stage, age, organ, and environmental factors, a subset of genes is transcribed into messenger RNA (mRNA) that could then be translated into proteins, which are critical for the functional state of a cell. Functional genomics is the study of the transcriptome, i.e., all the genes transcribed into mRNA, while proteomics is the analysis of the proteins expressed under a specific condition, such as disease. Genetic, functional genomic, and proteomic analysis have all contributed to determination of molecular changes related to disease in order to elucidate the cause and develop targeted therapy. In addition, these studies have identified biomarkers/diagnostic signatures that will improve diagnostic accuracy. Diagnostic signatures can be obtained by measuring either mRNA or protein levels in a given sample. It is preferable to measure protein levels, which more accurately describe the conditions in a biological system. Furthermore, mRNA levels are not necessarily correlative of protein levels and activity. In a study comparing mRNA and protein expression in lung carcinomas, only a subset of the proteins studied (17%) exhibited a significant correlation with mRNA levels (C4). Another subset of proteins had a negative correlation, and various protein isoforms had diVerent protein/ mRNA correlations. In addition to measuring protein expression levels, it would be useful to analyze proteins that have undergone post-translational

220

SMITH ET AL.

modifications, which are crucial for protein activity and function. Clearly, protein diagnostic signatures are significantly diVerent from DNA signatures and would be more specific if certain post-translationally modified and protein isoforms are important disease indicators. Investigations on RNAbased diagnostic signatures are currently more prevalent due to the availability of DNA microarrays. Protein signatures require specialized techniques for the separation and detection of proteins and the technology is less developed for large-scale high-throughput analysis. 2.1.1. DNA Diagnostic Signatures Microarrays represent a new technology that has been used extensively since the first DNA microarray study on diVerential expression of 45 Arabidopsis genes published in 1995 (S2). Many reports have demonstrated the use of DNA microarrays for the investigation of diVerential gene expression in diseased versus healthy tissue. These studies support the idea that obtaining diagnostic signatures for specific disease states is possible. However, very few studies fulfill the criteria outlined for clinically useful diagnostic signatures. Typically, mRNA is isolated from tissue samples rather than readily accessible body fluids, such as urine, plasma, cerebrospinal fluid, and saliva. Most studies compare healthy and diseased tissues rather than comparing diseases to determine whether signatures are specific. Normal variation is rarely reported and most study groups are too small to take the variation of the normal population into account. One study illustrated that blood genomic signatures could be used to distinguish among experimentally induced disease states in rats. The gene expression patterns for 8740 genes in leukocytes was determined on an AVymetrix GeneChip1 24 hours after adult rats were subjected to ischemic strokes, hemorrhagic strokes, sham surgeries, kainite-induced seizures, hypoxia, or insulin-induced hypoglycemia (T3). There were overall similarities in the response patterns in the six experimental conditions compared to the controls, but each disease condition could be identified by unique gene expression patterns. Animal studies are not directly comparable to humans as there are multiple environmental factors and genetic diversity of the human population to take into account. However, this is a significant study as a proof of principle that disease states can be detected and diVerentiated in readily accessible body fluids. Other DNA microarray studies indicate that presymptomatic diagnostic signatures are obtainable. DNA microarray analysis of mRNA samples from a chimpanzee’s liver during acute resolving Hepatitis C virus infection was performed. The study provided insight into the liver response to viral infection. Although the study was not developed to determine a diagnostic

PROTEIN-DETECTING MICROARRAYS

221

signature, changes in gene expression could be noticed as early as day 2 post-infection (B4). Early changes in gene expression in a murine model for allogenic bone marrow transplant indicated that acute graft-versus-host disease could be detected before the development of histological changes in the liver (I1). These animal studies in controlled settings indicate that early diagnosis is possible. 2.1.2. Protein Diagnostic Signatures Various methodologies are being developed to detect and quantify the specific combinations of proteins associated with a particular disease. There are significant issues that must be taken into consideration to achieve this goal. The methodology should detect proteins from complex biological samples and should be sensitive to detect low-abundant proteins, which are potentially important diagnostic markers. In addition, other challenges include the solubility of the protein (i.e., membrane proteins have low solubility in aqueous media) and diVerent isoforms and post-translationally modified proteins must be identified. Numerous technologies have been developed to undertake this daunting task, but we focus on reports that are clinically relevant. Mass spectrometry (MS) methodologies have most commonly been used to detect proteins (biomarkers) associated with a particular disease (P2, P5, P7, W5). The most well-established technique for determination of protein biomarkers is two-dimensional polyacrylamide gel electrophoresis coupled with mass spectrometry (2D/MS) (G3, H1). Several thousand proteins can be separated simultaneously according to their charge and molecular mass by 2D electrophoresis and visualized by silver staining. Subsequently, the protein spots of interest are excised from the gel, trypsinized, and analyzed by either matrix-assisted laser desorption/ionization time-of-flight (MALDITOF) MS or electrospray ionization (ESI) MS. Overall, 2D/MS has proven to be a reliable tool to diVerentiate between proteins expressed under diVerent cellular conditions and to detect proteins with various isoforms or posttranslational modifications. Although low abundance proteins, proteins of very low or high molecular weight, and less soluble proteins are not easily detected; thus, information is lost for potentially important diagnostic markers. This methodology has been employed for the identification of potential protein diagnostic signatures. By comparing the protein expression in lung adenocarcinoma tissue samples and uninvolved lung samples by 2D/MS, nine proteins were identified to have increased expression levels (1.4- to 10.6-fold) in lung adenocarcinoma tissue samples (C3). Furthermore, multiple protein isoforms were upregulated for a number of these proteins, but one

222

SMITH ET AL.

protein isoform for cytosolic inorganic pyrophosphate (P4HB) was significantly overexpressed while another isoform was unchanged relative to normal lung tissue. Thus, this study suggests that 2D/MS is a powerful tool to identify potential biomarkers, including specific isoforms with diagnostic potential. The cited study was done using tissue samples, but potential biomarkers for hepatitis B virus (HBV) infection were determined using serum samples (H2). Although there are a number of biomarkers available for HBV infection, no single serological test can unequivocally diagnose the infection. Therefore, an assay able to detect multiple biomarkers should be more accurate to diagnose HBV. The expression levels of seven proteins were significantly changed in HBV-infected sera as determined by 2D/MS. This protein profile suggests that these serum proteins may be useful in diagnosis, but additional investigations are needed to determine specificity of the pattern with regard to other types of infection and liver inflammation. Inflammatory response markers have been detected with 2D/MS in the urine of stroke-prone rats at least 4 weeks before a stroke occurred and before the appearance of anomalous features could be detected in the brain by MRI (S4). The specificity of inflammatory response markers still needs to be determined; however, this study suggests that proteins in a readily obtainable body fluid can be used as early diagnostic markers. An alternative mass spectroscopy technique that is rapidly gaining recognition for its potential in clinical proteomics is surface-enhanced laser desorption ionization time-of-flight (SELDI-TOF) (I3, I4, W4). Using the SELDI-TOF technology, only a small amount of serum sample (one microliter) is required to provide a diagnostic signature for a particular disease in a relatively short time and therefore a potentially high-throughput clinical proteomic tool. A critical aspect to this technique is that proteins from a serum sample are bound to a ProteinChip1 (Ciphergen Biosystems Inc., Fremont, CA, USA) based upon common physicochemical properties such as charge and hydrophobicity or, more specifically, adhered to the surface via a specific antibody or ligand, while the rest of the sample is washed away. Next, the adhered proteins are ionized and analyzed similar to MALDI-TOF. Since a low-end mass spectrometer lacking MS/MS capabilities is used in SELDI-TOF, the proteins or peptides in the sample are not individually identified; instead, profiles specific to serum samples are compiled using highly sophisticated bioinformatics. Diseased and healthy tissues have been diVerentiated by MALDI-TOF, as illustrated in the MS protein profile for tumor and normal lung tissue samples with the discriminatory peaks in the spectrum marked by an asterisk

PROTEIN-DETECTING MICROARRAYS

223

(Fig. 1) (Y1). SELDI-TOF has also been used to distinguish protein profiles. The first reports of using SELDI-MS to reveal diagnostic signatures in readily accessible body fluids were in nipple aspirate fluid to detect breast cancer (P1) and in serum to detect ovarian cancer (P3). To detect ovarian cancer-specific protein profiles, serum proteins were bound to a C16 hydrophobic interaction ProteinChip1 (P3). The mass spectra patterns from 50 ovarian cancer patients were compared to those from 50 unaVected controls. An iterative searching algorithm process identified a small subset of key values that segregated the cancer patients from the unaVected population. The cluster patterns were distinguishable for 50 ovarian cancer cases, including 18 stage I cases and nearly all control samples. This promising study suggests that a readily attainable serum sample could be used for initial screening of patients for ovarian cancer. The identities of the discriminatory peptides in the ovarian cancer sample were not deduced, which illustrates the limitation of the commercial Ciphergen system. The specificity of the protein profile for stage I ovarian cancer still needs to be determined, since the diagnostic signatures could be attributed to general metabolic changes caused by tumors. Combining profiles from additional subset-specific protein chips increased specificity (P4). The Ciphergen system has also been used to determine serum proteomic patterns in other cancers such as prostate cancer (A1, B2), hepatocellular carcinoma (P6), and non-small cell lung cancer (X1). The protein patterns were determined from a combination of data from more than one capture array, thereby increasing the specificity of the diagnostic signature. Another MS-based technique has been used in an attempt to determine the normal peptides present in bodily fluids. Peptides present in a normal urine sample were separated by high-resolution capillary electrophoresis (CE). A peptide pattern was established by analyzing the mass spectra from 18 samples (W3). The patterns contained ion peaks from 247 peptides (out of more than 1000 detected) that were present in more than 50% of the samples. The data was compared to five samples from patients with renal disease and impaired renal function. An alternative pattern was evident for samples from diseased individuals with additional ion peaks in the spectra and the absence of previously observed peaks. Even though a small number of samples were analyzed, valuable information about biological samples was obtained rapidly, illustrating the significance of MS. 2.2. SIGNATURE-DETECTING PLATFORMS IN THE CLINIC DNA microarrays have been a useful research tool. However, there are only a few examples of DNA microarrays used to identify clinically relevant diagnostic signatures, as outlined earlier. The complexity of the technique hinders

FIG. 1. Representative example of potential protein diagnostic profiles obtained by MALDITOF Mass Spectroscopy (MS) from tumor and normal lung tissue samples shown with the molecular weight calculation (m/z values). Asterisks indicate examples of the MS peaks identified by statistical analyses as optimum discriminatory patterns between normal and tumor. Below: hierarchical cluster analysis of 42 lung tumors and eight normal lung tissues in the training cohort according to the protein expression patterns of 82 MS signals. Each row represents an individual proteomic signal and each column represents an individual sample. The dendrogram at the top shows the similarity in protein expression profiles of the samples. Substantially raised (red) expression of the proteins is noted in individual tumor and normal lung tissue

PROTEIN-DETECTING MICROARRAYS

225

its use by nonexperts. Detecting diVerential gene expression requires a number of sample preparation steps. Furthermore, significant diVerences in measurable gene expression can be introduced by small variations in sample collection and preservation, RNA quality, cDNA amplification methods, probe labeling, hybridization, and washing conditions. DNA microarrays will be useful to identify individuals susceptible to genetic-based diseases. However, clinical use of DNA microarrays will most likely be done in specialized centers due to the cost and technical expertise required for reproducible results. Mass spectrometry techniques are used to determine diVerential patterns of protein expression but cannot be employed for rapid detection and quantification of specific proteins. Although 2D/MS has been proven to be a powerful tool to analyze protein mixtures, there are limitations that prevent it from use in the clinic. Separation of proteins by 2D is tedious, labor intensive, and not amenable to high-throughput strategies. Mass spectrometry techniques used to determine protein profiles (i.e., SELDI-TOF) have the potential for high-throughput strategies and automation. These methodologies do not require the extensive sample processing required by 2D/MS. However, additional SELDI-TOF studies must be carried out to ascertain its accuracy in detecting positives while reducing the incidence of falsepositives, prior to its use as a clinical diagnostic tool. However, the expense of mass spectrometers may limit their use to specialized centers. Currently, most clinical assays detect protein biomarkers by ELISA. Though ELISA is a well-established technique for diagnostic assays, it is limited to detection of single biomarkers. In the future, we envisage that multiplex ELISAs in the form of protein-detecting microarray-based assays will be used in the clinician’s oYce, or even in the home, for rapid detection of multiple proteins in biological samples. Unlike DNA microarrays, proteindetecting arrays would require minimal sample processing, thus reducing the variability and need for experienced individuals to obtain reproducible and accurate results.

3. Protein-Detecting Microarrays For clinical diagnostics, the goal is to develop protein-detecting microarrays with capture agents/ligands that bind specifically to target proteins in complex biological solutions (Figs. 2 and 3) (K2, K3). The eYciency of a samples. AD ¼ adenocarcinoma, SQ ¼ squamous-cell carcinoma, LA ¼ large-cell carcinoma, META ¼ metastases to lung from other sites, REC ¼ recurrent NSCLC, CAR ¼ pulmonary carcinoid, NL ¼ normal lung.1 1

Reprinted with permission from Elsevier (The Lancet, 2003, 362, 433–439).

226

SMITH ET AL.

FIG. 2. A protein-detecting microarray. Each square in the grid represents a diVerent feature of the array that would be impregnated with a particular protein ligand (blue shapes). When the sample is applied to the chip, each ligand will capture its target protein (orange and red coils in blow-up). The amount of target protein bound to each feature of the array would be quantitated with probes such as fluorescently labeled antibodies against the captured proteins. A fluoresence scanner would then measure the intensity of fluorescence (diVerently shaded green squares) at each spot, which would reflect the level of captured protein.2

protein-detecting microarray is dependent on a number of factors. The solid support and surface chemistry should minimize the amount of sample needed and optimize the eYciency of protein detection. Immobilized ligands must be stable and retain activity over extended periods. Ligand-binding capability must be validated to ensure that the working range covers the physiologically relevant concentrations of proteins. In addition, methods for signal detection and quantification should have a large dynamic range 2 Reprinted from Trends in Biochemical Sciences, 27, T. Kodadek, Development of proteindetecting microarrays and related devices, 295–300. Copyright (2002), with permission from Elsevier.

PROTEIN-DETECTING MICROARRAYS

227

FIG. 3. A bead-based format for the parallel detection of proteins. Each bead displays a diVerent binding agent directed against a specific protein target (blue shapes). Each bead is colorcoded by covalent linkage of two dyes (red and orange shapes) at a characteristic ratio, allowing for uniquely coded beads. Only two beads are shown for clarity. Upon application of the biological sample, the target protein binds to the capture agents. A mixture of secondary binding ligands (in this case, antibodies) conjugated to a fluorescent tag (green) is applied to the mixture of beads. The beads are then passed through a detector where two lasers ‘‘read’’ the ratio (n:m, x:y) of dyes and thus identify the bead, while the fluorescence intensity is read to quantitate the amount of labeled antibodies present (which will reflect the analyte level).2

appropriate for biological samples. Various aspects of protein arrays, from surface chemistry to detection systems, have been reviewed (C6, E1, F2, K2, K3, M1, T1, Z1). We will focus on the progress in ligand isolation, which is the most crucial feature for the development of clinical protein-detecting microarrays. 3.1. RECENT ADVANCES IN PROTEIN LIGAND ISOLATION Currently, antibodies are most often used as protein ligands because of their high specificity and aYnity (KD in the nM range) for a target protein. However, traditional methods of antibody production are not amenable to high-throughput isolation. Generally, the production of polyclonal antibodies takes 2 to 4 months and requires about 0.2 to 2 mg of purified antigen. It will take another 2 to 4 months to then produce a monoclonal antibody for the particular antigen. Moreover, the high-throughput production of purified antigen is challenging because purifying protein antigens is labor intensive and conditions generally need to be optimized for each protein. Recently, a more high-throughput method has been developed to generate polyclonal antibodies in mice (C2), which uses genetic immunization (T2) rather than purified antigen. Genetic immunization involves directly transfecting antigen-presenting cells with genes to express the antigen. Antibody response is enhanced by codon optimization of genes and addition of various elements to enhance antigenicity, such as plasmids encoding genetic adjuvants. Using this method, polyclonal antibodies can be produced within 4 to 8 weeks, even for antigens that failed to produce a response in protein

228

SMITH ET AL.

form. The exact aYnities of the polyclonal antibodies have not been determined, but are thought to be comparable to other antibodies. Antibodies may not be optimal ligands for protein-detecting arrays even with high-throughput antibody production. Most commercially available antibodies were found to be unsuitable for microarray-based analysis of cellular lysates (M1). A crucial drawback is that any type of antibody or folded protein is prone to loss of activity upon immobilization and storage. In contrast, small synthetic ligands are more stable and can be produced and purified economically and eYciently in bulk. These synthetic ligands are typically protein aptamers (antibody mimics), peptides, peptide-mimics, and small organic molecules. Various molecular biology techniques are available to screen for protein aptamer and peptide ligands for specific proteins. Phage display technology, introduced in 1985 (S5), has been used for the isolation of peptide (B5, F1, L5) and antibody fragment (G4) ligands for specific proteins. A 2002 review focuses on the principle of phage display technology and methods for the construction and bio-panning of phage libraries (A3). Libraries are constructed in vitro by inserting foreign DNA into specific locations of the genome of filamentous phage. The encoded protein or peptide is displayed on the surface as a fusion protein with one of the phage coat proteins, generally pIII, which displays five copies. Ligands bind to the protein of interest, which is immobilized on a plate. Bound phage are then eluted and amplified for more stringent rounds of panning. The amino acid sequence of the selected ligand can readily be determined by sequencing inserted DNA in the phage genome. Antibody fragment libraries from immunized and nonimmunized sources can be used in phage display and peptide libraries are commercially available. An alternative technique is to design protein aptamers that consist of a stable protein scaVold on which random peptides are displayed. An example of protein aptamers are aYbodies, which present a library of 13 randomized amino acids on the Z domain of Staphylococcus aureus protein A. Crystal structure studies indicate similarity in the binding of an aYbody to its target to protein–antibody interactions. However, aYbodies have a dissociation constant of approximately 1 M compared to antibody–antigen complexes of 1 nM or less (H3, R1, W1). The larger the library, the greater the probability of selecting rare highaYnity ligands. Phage display libraries typically contain 108–9 peptides with the limiting factor being the transformation eYciency of bacteria (L6). The in vitro techniques, ribosome and mRNA display, overcome this limitation since more complex libraries up to 1013 can be prepared (R2, W2). During in vitro transcription–translation of random DNA libraries, the encoded peptide remains associated with its mRNA. Either a ribosome complex is formed noncovalently by stalling the ribosome or the peptide is covalently linked to the mRNA through puromycin. Additional advantages of these

PROTEIN-DETECTING MICROARRAYS

229

techniques are that binding of the ligand is monovalent and aYnity maturation can be achieved over several rounds of screening by error-prone PCR or DNA shuZing (A3). This technique has been used successfully to isolate ligands that retain their high-aYnity binding properties when immobilized on a protein microarray. An mRNA library of antibody-mimics was prepared by randomizing three exposed loops on a stable, soluble protein, the tenth fibronectin type III domain. After 10 selection rounds, high-aYnity ligands for TNF- were isolated with dissociation constants between 1 and 24 nM. These ligands were further optimized by random mutagenesis to provide a ligand with a KD of 20 pM (X2). Disadvantages of both phage and mRNA display are the requirement for numerous rounds of selection and amplification, as well as the need to express and purify target proteins. Selectively infective phage (J3) and bacterial (H4, J2) and yeast (Y2) two-hybrid methods can overcome these obstacles because they are one-step screening assays with in vivo expressed target proteins. For the selectively infective phage technique, the N-terminal domains of the pIII coat protein is replaced with peptides from a ligand library, resulting in noninfective phage particles. To restore phage infectivity, adaptor molecules consisting of the target protein coupled to the missing N-terminal domains are required. These adaptor molecules can be expressed and exported to the periplasm in E. coli, eliminating the need for purified protein. Interaction between the fused peptide expressed on the phage coat and the adaptor molecule restores infectivity, allowing ligand selection in a single round. Although this method appears to have potential for ligand isolation (I2), few protein ligands are reported in the literature. This technology may be less successful for ligand isolation because of the potential for false positives (I3) or the size restriction of the target or ligand (C1). The yeast two-hybrid system detects protein–protein or protein–peptide interactions in vivo. The target or ‘‘bait’’ protein and the ligand library are fused to either the DNA-binding domain or the transcription activation domain. Yeast cells are transformed with both plasmids and only the transformants expressing the protein–ligand interaction are selected (Y2). The main advantage is the one-step in vivo screening; however, the library size is limited to about 107 because of the transformation eYciency of the cells. A variation on the yeast two-hybrid technique is the bacterial two-hybrid system. In this case, the target protein and the library-encoded peptide are each fused to a monomer of the DNA binding domain. Only if the target and ligand interact will the DNA binding domain form an active dimeric repressor. An activated repressor results in cells immune to phage infection, allowing for one-step selection of immune cells. This method was shown to be

230

SMITH ET AL.

capable of selecting a peptide that specifically bound to its target protein with a KD in the micromolar range and inhibited the activity of the target protein in vivo (Z2). Although specific peptide ligands have been selected by both yeast and bacterial two-hybrid methods, there is little documented evidence for the identification of high-aYnity ligands. An alternative to the biological methodologies for screening protein ligands is the synthetic combinatorial library approach. Chemical libraries are prepared on a solid-support, usually on bead or a microarray format, and encompass a variety of synthetic molecules such as peptides, peptide mimics, and small organic molecules. For feasibility reasons, the libraries are usually limited to a size of 105 to 106, which is several-fold less than libraries developed from other techniques. However, an appealing feature is that a synthetic library is not limited to the 20 natural amino acids, thereby allowing for the inclusion of a variety of chemical properties. One such example are peptoids, N-substituted oligoglycines, which are structurally similar to peptides but are resistant to proteolytic cleavage, easily synthesized on resin, and which have diverse chemical side chains on the nitrogen of the peptoid backbone (A2, F3). Synthetic ligands from a combinatorial library are amenable to high-throughput screening and can easily be prepared in large quantities with little variability. Numerous protein ligands and inhibitors have been identified using combinatorial libraries. A comparison of combinatorial peptide library approaches has been outlined in a recent review (L7). The one-bead-onecompound (OBOC) approach entails the synthesis of thousands or millions of random compounds on bead. Small molecules as well as peptide mimetics have been identified as ligands to cellular proteins such as protein kinases and intracellular signaling proteins using an OBOC approach (L2). Protein ligands have also been isolated from biased libraries, which include a structural motif or derivatives of initial leads. By incorporating a consensus sequence into a peptide library, ligands were discovered to bind to the SH3 domain of phosphatidylinositol 3-kinase with modest aYnity (C5). Inhibitors of aspartyl proteases have been isolated from a peptide library which incorporated chemical functional groups known to interact with essential active site residues (L4). Bead-based libraries are most commonly used for the development of protein ligands. However, a combinatorial small-molecule library on a microarray format was screened and included an inhibitor of the transcription factor Hap3p (K4). Both the biological methods and the chemical combinatorial libraries usually yield low to modest aYnity protein-ligands (KD in micromolar range), which are insuYcient to capture low abundance proteins from complex biological mixtures. Rather than designing and synthesizing larger libraries, an alternative approach is to synthesize multivalent ligands. Two

PROTEIN-DETECTING MICROARRAYS

231

modest aYnity ligands can be linked to yield a new ligand with an aYnity that theoretically equals the product aYnity for the two individual molecules. One example of this ‘‘pincer’’ strategy was demonstrated when nuclear magnetic resonance was used to identify small molecules that bind to diVerent surfaces on FK506-binding protein and determine the appropriate linker for the two compounds. The chimeric molecule of the individual FK506binding protein binders had a KD of 19 nM (S3). A potent inhibitor of cSrc kinase was designed using a similar strategy. Initial lead molecules for kinase inhibition were identified from aldehyde-derived oxime compounds. Screening of a small library of chimeric compounds of the initial hits yielded a much more eVective kinase inhibitor with an inhibition constant, KI, of 60 nM. The pincer approach oVers a unique opportunity for creating chemically diverse protein-ligands, though designing optimal linkers for the pincer molecule requires some experimental eVort. Instead of linking two solution binders, protein ligands can be immobilized onto solid support, providing a wide variety of combinations of the two ligands. Appropriately positioned ligands will bind diVerent surfaces of the same protein, increasing the overall aYnity. Therefore, two noncompetitive, modest-aYnity ligands can be synthesized on solid support without a linker to provide a high-aYnity chimeric molecule, also known as the mixed-element capture agent (MECA) (Fig. 4) (B1). To demonstrate this, a MECA of two specific protein ligands was synthesized on resin. Each peptide of the MECA was specific for monomeric protein, either MBP or Mdm2. The MECA was determined to have a slightly higher aYnity for the MBP–Mdm2 fusion protein as compared to the individual peptide ligands in solution, but was a much more eVective capture agent on solid support. Using a similar idea, high-aYnity protein capture agents can be designed simply by immobilizing modest-aYnity ligands for multimeric proteins.

FIG. 4. Schematic representation of the MECA concept. Two noncompeting ligands (red and blue shapes) could be immobilized individually (left) or as a linear fusion (right), allowing for two appropriately positioned molecules to cooperate in the capture of the target protein. Reprinted with permission from (B1). Copyright (2003) American Chemical Society.

232

SMITH ET AL.

Some fractions of these ligands should be appropriately oriented on the surface to promote binding of multiple ligands to one protein (i.e., one ligand bound per monomer). High-aYnity capture agents were created by synthesizing peptide ligands to dimeric proteins on Tentagel resin (N1). These immobilized ligands dramatically increased the half-life of the peptide–target protein complex when compared to random peptide ligands. The creation of high-aYnity capture agents from modest aYnity solution binders suggests that protein-detecting arrays may be more readily available than previously expected. 3.2. RECENT APPLICATIONS OF PROTEIN-DETECTING MICROARRAYS Due to the limited availability of well-characterized ligands, proteindetecting arrays are not ideal as a signature/biomarker discovery tool. However, protein-detecting microarrays are being developed as research tools and for diagnostics (L1). The first generation of protein microarrays are constructed with antibodies as capture agents. Although antibodies are less stable, very few synthetic ligands are currently available. An array of 368 antibodies was developed to identify the proteins present in the tissue of a single case of oral cavity cancer (K1). Antibody suspensions were spotted onto a thin film of nitrocellulose bonded to a glass slide. Protein lysates were biotinylated and bound protein was detected and quantified by an enzymelinked colorimetric assay. The antibody arrays were capable of detecting cancer-related proteins, as three of the eleven proteins detected were previously identified in tissue culture models of oral cavity cancer. Although this is the largest array to date, the assay needs to be validated, since the antibodies were not characterized with respect to aYnities, concentration, and cross-reactivity. Furthermore, the detected proteins were in non-native form. In another study, a screen of potential serum biomarkers of human prostrate cancer identified five proteins with significantly diVerent expression levels between 33 prostate cancer samples and 20 healthy controls (M3). These proteins were detected by antibodies spotted on either microscope slides coated with poly-L-lysine/N-hydroxysuccinimide-4-azidobenzoate (HSAB) or acrylamide-based HydrogelTM-coated slides. One hundred and eighty-four antibodies to target serum proteins and intracellular proteins were spotted in quadruplicate. The sample proteins were labeled directly with fluorescent tags and compared to a reference sample consisting of equal volumes of all serum samples. Labeling the sample eliminates the need for paired antibodies able to detect noncompeting epitopes of a protein, but can lead to bias. To control for labeling bias, the samples and reference were alternately labeled with diVerent fluorescent tags (reverse labeling). Labeling bias due to diVerent fluorophores can be controlled this way, but

PROTEIN-DETECTING MICROARRAYS

233

bias will be introduced if the presence of any label interferes with binding of the labeled protein to its specific ligand. Hydrogels were considered superior to the poly-L-lysine/HSAB-coated slides because of the lower background and more antibodies with a measurable signal (78 compared to 23). A number of companies have developed antibody arrays for research purposes. For example, a bead-based assay to screen up to 17 cytokines is available from Bio-Rad Laboratories (Hercules, CA, USA). In addition to the bead-based assays, Zyomyx, Inc. (Hayward, CA, USA) oVers a Human Cytokine Biochip for profiling of 30 biologically relevant cytokines. BD Biosciences Clontech (Palo Alto, CA, USA) has developed the largest commercially available antibody array for research purposes. This array includes over 500 antibodies with aYnity for proteins involved in a range of biological functions such as signal transduction, cancer, cell cycle regulation, cell structure, apoptosis, and neurobiology. Furthermore, the company reports that proteins present in the low pg/ml range can be detected in complex protein mixtures. The first antibody array with diagnostic potential was produced for immunotyping of leukemia (B3). Sixty antibodies were adhered to a film of nitrocellulose bound to a glass slide. Leukocytes from leukemia patients and healthy controls were incubated on arrays and bound leukocytes were visualized by dark-field microscopy. Relative densities of subpopulations of cells with distinct immunophenotypes were determined by eye. Distinctive and reproducible patterns were obtained for five leukemia types, indicating the potential for accurate diagnosis. Flow cytometric analysis of samples from two patients with chronic lymphocytic leukemia correlated closely with the array analyses for antigens expressed at high levels. A comparison of samples from 20 patients with chronic lymphocytic leukemia and 20 healthy controls indicated that leukocyte expression levels for 7 of the 60 cell-surface antigens could discriminate between the two sets. Although the results are only semiquantitative, this study suggests that leukemia types can be diVerentiated rapidly using a simple technique without specialized, expensive equipment. An alternative to the antibody array is the immobilization of proteins and detection of specific antibodies in sera. These antibody-detecting arrays will probably be the first to be routinely available in the clinic for serodiagnosis of autoimmune and infectious diseases. Serum samples from 60 individuals were tested with an array of microbial antigens printed on silanized glass microscope slides (M2). Anti-human IgG and IgM detection antibodies were labeled with fluorophores and quantified using confocal scanning microcopy. Comparison with commercially available ELISAs indicated that the microarray assay could identify positive and negative sera with similar eYciency. In this experiment, only 5 microbial antigens were arrayed.

234

SMITH ET AL.

However, it is conceivable that arrays could be developed with ligands for a range of infectious disease agents and microbial toxins, allowing for rapid serodiagnosis in a clinical setting.

4. Conclusions Protein-detecting microarrays have great clinical diagnostic potential for monitoring the level and molecular state of native proteins in readily accessible body fluids such as serum, urine, and saliva. There are a number of significant hurdles that need to be overcome before these protein-detecting microarrays will be routinely accessible. First, diagnostic signatures specific to certain disease states need to be identified. MS-based signature discovery tools will play an extensive role in identifying the set of useful biomarkers for the development of disease-specific protein-detecting arrays. The ideal protein microarray will be capable of detecting all proteins, isoforms, and post-translationally modified proteins essential to diagnose disease. Even simple protein profiles may consist of hundreds to thousands of proteins. We hypothesize that within complex signatures there are some proteins that are more informative. As a first step toward clinically relevant protein-detecting microarrays, smaller arrays of protein-ligands should be useful to identify diagnostic signatures. Antibody microarrays for research purposes have been developed, but ideally arrays will consist of synthetic capture agents, which are more stable and amenable to highthroughput isolation and production. The next hurdle, therefore, will be the isolation of high specificity and aYnity ligands for clinically relevant proteins and any significant post-translational states and isoforms. REFERENCES A1. Adam, B. L., Qu, Y., Davis, J. W., et al., Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men. Cancer Res. 62, 3609–3614 (2002). A2. Alluri, P. G., Reddy, M. M., Bachhawat-Sikder, K., Olivos, H. J., and Kodadek, T., Isolation of protein ligands from large peptoid libraries. J. Am. Chem. Soc. 125, 13995–14004 (2003). A3. Azzazy, H. M., and Highsmith, W. E., Jr., Phage display technology: Clinical applications and recent innovations. Clin. Biochem. 35, 425–445 (2002). B1. Bachhawat-Sikder, K., and Kodadek, T., Mixed-element capture agents: A simple strategy for the construction of synthetic, high-aYnity protein capture ligands. J. Am. Chem. Soc. 125, 9550–9551 (2003). B2. Banez, L. L., Prasanna, P., Sun, L., et al., Diagnostic potential of serum proteomic patterns in prostate cancer. J. Urol. 170, 442–446 (2003).

PROTEIN-DETECTING MICROARRAYS

235

B3. Belov, L., de la Vega, O., dos Remedios, C. G., Mulligan, S. P., and Christopherson, R. I., Immunophenotyping of leukemias using a cluster of diVerentiation antibody microarray. Cancer Res. 61, 4483–4489 (2001). B4. Bigger, C. B., Brasky, K. M., and Lanford, R. E., DNA microarray analysis of chimpanzee liver during acute resolving hepatitis C virus infection. J. Virol. 75, 7059–7066 (2001). B5. Bottger, V., Bottger, A., Howard, S. F., et al., Identification of novel mdm2 binding peptides by phage display. Oncogene 13, 2141–2147 (1996). C1. Cebe, R., and Geiser, M., Size of the ligand complex between the N-terminal domain of the gene III coat protein and the non-infectious phage strongly influences the usefulness of in vitro selective infective phage technology. Biochem. J. 352(Pt. 3), 841–849 (2000). C2. Chambers, R. S., and Johnston, S. A., High-level generation of polyclonal antibodies by genetic immunization. Nat. Biotechnol. 21, 1088–1092 (2003). C3. Chen, G., Gharib, T. G., Huang, C. C., et al., Proteomic analysis of lung adenocarcinoma: Identification of a highly expressed set of proteins in tumors. Clin. Cancer Res. 8, 2298–2305 (2002). C4. Chen, G., Gharib, T. G., Huang, C. C., et al., Discordant protein and mRNA expression in lung adenocarcinomas. Mol. Cell Proteomics 1, 304–313 (2002). C5. Chen, J. K., Lane, W. S., Brauer, A. W., Tanaka, A., and Schreiber, S. L., Biased combinatorial libraries: Novel ligands for the SH3 domain of phosphatidylinositol 3-kinase. J. Am. Chem. Soc. 115, 12591–12592 (1993). C6. Cutler, P., Protein arrays: The current state-of-the-art. Proteomics 3, 3–18 (2003). E1. Elia, G., Silacci, M., Scheurer, S., Scheuermann, J., and Neri, D., AYnity-capture reagents for protein arrays. Trends Biotechnol. 20(12 Suppl.), S19–22 (2002). F1. Fairbrother, W. J., Christinger, H. W., Cochran, A. G., et al., Novel peptides selected to bind vascular endothelial growth factor target the receptor-binding site. Biochemistry 37, 17754–17764 (1998). F2. Figeys, D., Adapting arrays and lab-on-a-chip technology for proteomics. Proteomics 2, 373–382 (2002). F3. Figliozzi, G. M., Goldsmith, R., Ng, S. C., Banville, S. C., and Zuckermann, R. N., Synthesis of N-substituted glycine peptoid libraries. Meth. Enzymol. 267, 437–447 (1996). G1. Geschwind, D. H., DNA microarrays: Translation of the genome from laboratory to clinic. Lancet Neurol. 2, 275–282 (2003). G2. Gibbs, W. W., Untangling the roots of cancer. Sci. Am. 289, 56–65 (2003). G3. Gorg, A., Obermaier, C., Boguth, G., et al., The current state of two-dimensional electrophoresis with immobilized pH gradients. Electrophoresis 21, 1037–1053 (2000). G4. GriYths, A. D., Williams, S. C., Hartley, O., et al., Isolation of high aYnity human antibodies directly from large synthetic repertoires. EMBO J. 13, 3245–3260 (1994). H1. Hanash, S. M., Biomedical applications of two-dimensional electrophoresis using immobilized pH gradients: Current status. Electrophoresis 21, 1202–1209 (2000). H2. He, Q. Y., Lau, G. K., Zhou, Y., et al., Serum biomarkers of hepatitis B virus infected liver inflammation: A proteomic study. Proteomics 3, 666–674 (2003). H3. Hogbom, M., Eklund, M., Nygren, P. A., and Nordlund, P., Structural basis for recognition by an in vitro evolved aYbody. Proc. Natl. Acad. Sci. USA 100, 3191–3196 (2003). H4. Hu, J. C., Kornacker, M. G., and Hochschild, A., Escherichia coli one- and two-hybrid systems for the analysis and identification of protein–protein interactions. Methods 20, 80–94 (2000).

236

SMITH ET AL.

I1. Ichiba, T., Teshima, T., Kuick, R., et al., Early changes in gene expression profiles of hepatic GVHD uncovered by oligonucleotide microarrays. Blood 102, 763–771 (2003). I2. Ilag, L. L., Rottenberger, C., Liepinsh, E., Wellnhofer, G., Rudert, F., and Otting, G., Selection of a peptide ligand to the p75 neurotrophin receptor death domain and determination of its binding sites by NMR. Biochem. Biophys. Res. Comm. 255, 104–109 (1999). I3. Issaq, H. J., Conrads, T. P., Prieto, D. A., Tirumalai, R., and Veenstra, T. D., SELDITOF MS for diagnostic proteomics. Anal. Chem. 75, 148A–155A (2003). I4. Issaq, H. J., Veenstra, T. D., Conrads, T. P., and Felschow, D., The SELDI-TOF MS approach to proteomics: Protein profiling and biomarker identification. Biochem. Biophys. Res. Comm. 292, 587–592 (2002). J1. Joos, L., Eryuksel, E., and Brutsche, M. H., Functional genomics and gene microarrays—The use in research and clinical medicine. Swiss Med. Wkly. 133, 31–38 (2003). J2. Joung, J. K., Ramm, E. I., and Pabo, C. O., A bacterial two-hybrid selection system for studying protein–DNA and protein–protein interactions. Proc. Natl. Acad. Sci. USA 97, 7382–7387 (2000). J3. Jung, S., Arndt, K. M., Muller, K. M., and Pluckthun, A., Selectively infective phage (SIP) technology: Scope and limitations. J. Immunol. Meth. 231, 93–104 (1999). K1. Knezevic, V., Leethanakul, C., Bichsel, V. E., et al., Proteomic profiling of the cancer microenvironment by antibody arrays. Proteomics 1, 1271–1278 (2001). K2. Kodadek, T., Protein microarrays: Prospects and problems. Chem. Biol. 8, 105–115 (2001). K3. Kodadek, T., Development of protein-detecting microarrays and related devices. Trends Biochem. Sci. 27, 295–300 (2002). K4. Koehler, A. N., Shamji, A. F., and Schreiber, S. L., Discovery of an inhibitor of a transcription factor using small molecule microarrays and diversity-oriented synthesis. J. Am. Chem. Soc. 125, 8420–8421 (2003). K5. Kuriyama, M., Wang, M. C., Papsidero, L. D., et al., Quantitation of prostate-specific antigen in serum by a sensitive enzyme immunoassay. Cancer Res. 40, 4658–4662 (1980). L1. Lal, S. P., Christopherson, R. I., and dos Remedios, C. G., Antibody arrays: An embryonic but rapidly growing technology. Drug Discov. Today 7(18 Suppl.), S143–S149 (2002). L2. Lam, K. S., Liu, R., Miyamoto, S., Lehman, A. L., and Tuscano, J. M., Applications of one-bead one-compound combinatorial libraries and chemical microarrays in signal transduction research. Acc. Chem. Res. 36(6), 370–377 (2003). L3. Lander, E. S., Linton, L. M., Birren, B., et al., Initial sequencing and analysis of the human genome. Nature 409, 860–921 (2001). L4. Lee, C. E., Kick, E. K., and Ellman, J. A., General solid phase synthesis approach to prepare mechanism-based aspartyl protease inhibitor libraries. Identification of potent cathepsin D inhibitors. J. Am. Chem. Soc. 120, 9735–9747 (1998). L5. Li, P., Zhang, M., Peach, M. L., et al., Structural basis for a non-phosphorus-containing cyclic peptide binding to Grb2-SH2 domain with high aYnity. Biochem. Biophys. Res. Comm. 307, 1038–1044 (2003). L6. Lin, H., and Cornish, V. W., Screening and selection methods for large-scale analysis of protein function. Angew. Chem. Int. Ed. Engl. 41, 4402–4425 (2002). L7. Liu, R., Enstrom, A. M., and Lam, K. S., Combinatorial peptide library methods for immunobiology research. Exp. Hematol. 31, 11–30 (2003). M1. MacBeath, G., Protein microarrays and proteomics. Nat. Genet. 32(Suppl.), 526–532 (2002).

PROTEIN-DETECTING MICROARRAYS

237

M2. Mezzasoma, L., Bacarese-Hamilton, T., Di Cristina, M., Rossi, R., Bistoni, F., and Crisanti, A., Antigen microarrays for serodiagnosis of infectious diseases. Clin. Chem. 48, 121–130 (2002). M3. Miller, J. C., Zhou, H., Kwekel, J., et al., Antibody microarray profiling of human prostate cancer sera: Antibody screening and identification of potential biomarkers. Proteomics 3, 56–63 (2003). N1. NaYn, J. L., Han, Y., Olivos, H. J., Reddy, M. M., Sun, T., and Kodadek, T., Immobilized peptides as high-aYnity capture agents for self-associating proteins. Chem. Biol. 10, 251–259 (2003). P1. Paweletz, C. P., Trock, B., Pennanen, M., et al., Proteomic patterns of nipple aspirate fluids obtained by SELDI-TOF: Potential for new biomarkers to aid in the diagnosis of breast cancer. Dis. Markers 17, 301–307 (2001). P2. Petricoin, E. E., Paweletz, C. P., and Liotta, L. A., Clinical applications of proteomics: Proteomic pattern diagnostics. J. Mammary Gland Biol. Neoplasia 7, 433–440 (2002). P3. Petricoin, E. F., Ardekani, A. M., Hitt, B. A., et al., Use of proteomic patterns in serum to identify ovarian cancer. Lancet 359, 572–577 (2002). P4. Petricoin, E. F., Mills, G. B., Kohn, E. C., and Liotta, L. A., Correspondence: Proteomic patterns in serum and identification of ovarian cancer. Lancet 360, 170–171 (2003). P5. Petricoin, E. F., Zoon, K. C., Kohn, E. C., Barrett, J. C., and Liotta, L. A., Clinical proteomics: Translating benchside promise into bedside reality. Nat. Rev. Drug Discov. 1, 683–695 (2002). P6. Poon, T. C., Yip, T. T., Chan, A. T., et al., Comprehensive proteomic profiling identifies serum proteomic signatures for detection of hepatocellular carcinoma and its subtypes. Clin. Chem. 49, 752–760 (2003). P7. Pusch, W., Flocco, M. T., Leung, S. M., Thiele, H., and Kostrzewa, M., Mass spectrometry-based clinical proteomics. Pharmacogenomics 4, 463–476 (2003). R1. Regan, L., Molten globules move into action. Proc. Natl. Acad. Sci. USA 100, 3553–3554 (2003). R2. Roberts, R. W., and Szostak, J. W., RNA-peptide fusions for the in vitro selection of peptides and proteins. Proc. Natl. Acad. Sci. USA 94, 12297–12302 (1997). S1. Sanger, F., Air, G. M., Barrell, B. G., et al., Nucleotide sequence of bacteriophage phi X174 DNA. Nature 265, 687–695 (1977). S2. Schena, M., Shalon, D., Davis, R. W., and Brown, P. O., Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270, 467–470 (1995). S3. Shuker, S. B., Hajduk, P. J., Meadows, R. P., and Fesik, S. W., Discovering highaYnity ligands for proteins: SAR by NMR. Science 274, 1531–1534 (1996). S4. Sironi, L., Tremoli, E., Miller, I., et al., Acute-phase proteins before cerebral ischemia in stroke-prone rats: Identification by proteomics. Stroke 32, 753–760 (2001). S5. Smith, G. P., Filamentous fusion phage: Novel expression vectors that display cloned antigens on the virion surface. Science 228, 1315–1317 (1985). T1. Talapatra, A., Rouse, R., and Hardiman, G., Protein microarrays: Challenges and promises. Pharmacogenomics 3, 527–536 (2002). T2. Tang, D. C., DeVit, M., and Johnston, S. A., Genetic immunization is a simple method for eliciting an immune response. Nature 356, 152–154 (1992). T3. Tang, Y., Lu, A., Aronow, B. J., and Sharp, F. R., Blood genomic responses diVer after stroke, seizures, hypoglycemia, and hypoxia: Blood genomic fingerprints of disease. Ann. Neurol. 50, 699–707 (2001).

238

SMITH ET AL.

T4. Tomb, J. F., White, O., Kerlavage, A. R., et al., The complete genome sequence of the gastric pathogen Helicobacter pylori. Nature 388, 539–547 (1997). V1. Venter, J. C., Adams, M. D., Myers, E. W., et al., The sequence of the human genome. Science 291, 1304–1351 (2001). W1. Wahlberg, E., Lendel, C., Helgstrand, M., et al., An aYbody in complex with a target protein: Structure and coupled folding. Proc. Natl. Acad. Sci. USA 100, 3185–3190 (2003). W2. Wilson, D. S., Keefe, A. D., and Szostak, J. W., The use of mRNA display to select high-aYnity protein-binding peptides. Proc. Natl. Acad. Sci. USA 98, 3750–3755 (2001). W3. Wittke, S., Fliser, D., Haubitz, M., et al., Determination of peptides and proteins in human urine with capillary electrophoresis–mass spectrometry, a suitable tool for the establishment of new diagnostic markers. J. Chromatog. A. 1013, 173–181 (2003). W4. Wright, G. L., Jr., SELDI proteinchip MS: A platform for biomarker discovery and cancer diagnosis. Expert Rev. Mol. Diag. 2, 549–563 (2002). W5. Wulfkuhle, J. D., Liotta, L. A., and Petricoin, E. F., Proteomic applications for the early detection of cancer. Nat. Rev. Cancer 3, 267–275 (2003). X1. Xiao, X. Y., Tang, Y., Wei, X. P., and He, D. C., A preliminary analysis of non-small cell lung cancer biomarkers in serum. Biomed. Environ. Sci. 16, 140–148 (2003). X2. Xu, L., Aha, P., Gu, K., et al., Directed evolution of high-aYnity antibody mimics using mRNA display. Chem. Biol. 9, 933–942 (2002). Y1. Yanagisawa, K., Shyr, Y., Xu, B. J., et al., Proteomic patterns of tumour subsets in nonsmall-cell lung cancer. Lancet 362, 433–439 (2003). Y2. Yang, M., Wu, Z., and Fields, S., Protein–peptide interactions analyzed with the yeast two-hybrid system. Nucl. Acids Res. 23, 1152–1156 (1995). Z1. Zhu, H., and Snyder, M., Protein chip technology. Curr. Opin. Chem. Biol. 7, 55–63 (2003). Z2. Zhu, W., Williams, R. S., and Kodadek, T., A CDC6 protein-binding peptide selected using a bacterial two-hybrid-like system is a cell cycle inhibitor. J. Biol. Chem. 275, 32098–32105 (2000).