Glycobiology Advance Access originally published online on May 4, 2007
Glycobiology 2007 17(8):17C-23C; doi:10.1093/glycob/cwm047
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Published by Oxford University Press 2007
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High-throughput carbohydrate microarray profiling of 27 antibodies demonstrates widespread specificity problems
Laboratory of Medicinal Chemistry, Center for Cancer Research, National Cancer Institute, 376 Boyles Street, Building 376, Room 109, Frederick, MD 21702
1 To whom correspondence should be addressed; Tel: +1 301-846-5699; Fax: +1 301-846-6033; e-mail: gildersleevej{at}ncifcrf.gov
Received on February 7, 2007; revised on April 16, 2007; accepted on April 17, 2007
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Abstract |
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 Top Abstract IntroductionResultsDiscussionMaterials and methodsSupplementary dataConflict of interest statementAcknowledgmentsReferences |
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Progress toward understanding the biological roles of carbohydrates has been remarkably slow, and efforts to exploit this class of biopolymers as diagnostic and therapeutic targets have proven extremely challenging. Both basic and clinical research rely heavily on identifying and monitoring expression levels of carbohydrates. Over the last 30 years, the majority of expression information has been derived from antibody- and lectin-binding studies. Using a carbohydrate microarray containing 80 different glycans and glycoproteins, the specificities of 27 antiglycan antibodies were evaluated, including antibodies to histo-blood group A, B, and H antigens (81FR2.2, CLCP-19B, B389, 92FR-A2, B480, B460, B376, and B393), Lewis antigens (7LE, 15C02, 28, ZC-18C, 121SLE, CA199.02, PR.5C5, 2-25LE, BR55, T174, T218, F3, A70-C/C8, FR4A5, and K21), and other tumor-associated antigens (B389, 1A4, B1.1, and 5B5). In total, evaluation of over 2000 individual carbohydrate–protein interactions was carried out. More than half of the antibodies considered to be specific for their designated antigen were found to cross-react with other glycans. The cross-reactive glycans could be mistaken for the designated antigen in biopsy samples or other biological samples, leading to inaccurate conclusions.
Key words: antibodies / blood group antigens / glycan array / Lewis antigens / tumor-associated carbohydrate antigens
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Introduction |
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TopAbstractIntroductionResultsDiscussionMaterials and methodsSupplementary dataConflict of interest statementAcknowledgmentsReferences |
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Carbohydrates play important roles in a wide range of biological processes. In addition, cells undergo dramatic changes in carbohydrate expression during the onset and progression of diseases such as cancer and rheumatoid arthritis. For example, altered expression of ABH and Lewis histo-blood group antigens, glycosphingolipids, and mucin-related antigens (e.g. TF, Tn, STn) in cancer tissue is well known (Dube and Bertozzi 2005
; Fuster and Esko 2005). Over the last 30 years, there have been numerous studies aimed at understanding the biological roles of carbohydrates and determining how changes in carbohydrate expression contribute to diseases. In some cases, carbohydrate antigens have been found to play key roles in processes such as cell–cell adhesion, inflammation, and metastasis. In most cases, however, the biological effects and the relationships between structure and function are not well understood. Carbohydrate antigens have also become important molecular targets for the development of diagnostic and therapeutic agents (Dube and Bertozzi 2005; Fuster and Esko 2005). For example, a number of carbohydrate-based cancer vaccines (Keding and Danishefsky 2004; Slovin et al. 2005), anticarbohydrate antibodies (Pai et al. 1996; Posey et al. 2002), and lectins (Valdimarsson 2003; Schoffski et al. 2005) are currently in clinical trials. Although there is tremendous potential, development of effective diagnostic and therapeutic agents has proven to be very challenging. With carbohydrate-based cancer vaccines, for example, one frequently observes good clinical responses in a subset of patients but a modest overall response for the entire patient group. When monitoring carbohydrate expression for diagnostic–prognostic purposes, different studies frequently report conflicting results. As a result, only a small number of carbohydrate antigens are used clinically as biomarkers. One of the most fundamental requirements for both basic and translational research is determining when and where a carbohydrate is expressed. To understand the biological roles of a particular carbohydrate or to evaluate a carbohydrate antigen as a disease biomarker, one must first locate the antigen. Reliable information is also critical for clinical trials. For example, the successful application of carbohydrate-based cancer vaccines requires identification of patients with antigen-positive tumors. Unfortunately, carbohydrate expression levels are extremely difficult to measure using direct detection methods, to infer from gene expression levels, or to gauge from protein expression patterns. As a result, expression levels have primarily been monitored indirectly by probing the binding of anticarbohydrate antibodies and/or lectins using techniques such as immunohistochemistry and western blotting. The quality of the information obtained from antibody and lectin-binding studies depends largely on the specificity of these proteins. Consequently, specificity has been studied at length over the years. Most lectins are known to have broad specificity. As a result, they have primarily been used to monitor general changes of carbohydrate expression and to track families of antigens (e.g. various structures containing a terminal fucose). In contrast, anticarbohydrate antibodies typically have much better specificity, and many are reported to be completely specific for their designated antigen. Therefore, antibody binding has been used extensively to monitor expression of individual carbohydrate antigens (e.g. Lewis Y).
Although antibody and lectin specificity has been studied frequently (for some examples, see Oriol et al. 1990; Mollicone et al. 1996; Pochechueva et al. 2002), only a tiny fraction of all the natural carbohydrate structures found in nature have been evaluated as potential ligands for any given protein. The two primary obstacles have been a lack of access to structurally defined glycans for analysis and a lack of high-throughput methods to evaluate binding. Advances in the fields of combinatorial carbohydrate synthesis and automated carbohydrate synthesis are rapidly expanding the number of glycans available. However, traditional methods for analyzing carbohydrate–protein interactions, such as X-ray crystallography, NMR, isothermal calorimetry, ELISA, and mono- or oligosaccharide inhibition studies can be slow, require large amounts of material, and are not well suited to analysis of thousands of potential carbohydrate–protein interactions.
Carbohydrate microarrays are a new technology being developed by a number of groups for high-throughput evaluation of carbohydrate–macromolecule interactions (Paulson et al. 2006). Carbohydrate microarrays contain many different glycans immobilized on a solid support in a miniaturized format. One can simultaneously evaluate binding of proteins, viruses, or cells to every component on the array under identical conditions. In addition, the microarray format has the capacity to accommodate thousands of unique glycans, although consuming only tiny amounts of precious materials. Our group has developed a carbohydrate microarray and highly sensitive assay (Manimala et al. 2005, 2006). The array contains 80 different components including 61 structurally defined synthetic carbohydrates [in the form of bovine serum albumin (BSA)/human serum albumin (HSA) conjugates], 15 natural glycoproteins, and 4 controls (see Table I for a detailed list). The glycans are printed on glass microscope slides using a robotic microarrayer. To maximize throughput, a 16-well slide format is utilized, with an entire array printed in each well. Therefore, 16 independent array experiments can be conveniently carried out on each slide.
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In this paper, carbohydrate microarray profiling of 27 carbohydrate-binding antibodies is described. In addition to providing key information for scientists using these antibodies or using the information derived from the antibodies, the results have broad implications for the field.
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Results |
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TopAbstractIntroductionResultsDiscussionMaterials and methodsSupplementary dataConflict of interest statementAcknowledgmentsReferences |
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The 27 antibodies chosen for this study are listed in Table II. All the antibodies are known to bind antigens with altered expression in tumors. These included 8 antibodies to ABH histo-blood group antigens, 15 antibodies to Lewis antigens, and 4 antibodies to Tn, TF, and Gal
1-4Galß1-4Glc-HSA (Gb3) (Table II). The panel consisted of 21 IgM antibodies and 6 IgG antibodies, reflecting the higher proportion of IgM antibodies available. All the antibodies are reported to be specific for their listed antigen by the companies that sell them. However, four of these have previously been reported to have some cross-reactivity.
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Antibodies were evaluated using an ELISA format adapted for the carbohydrate microarray (Manimala et al. 2005
, 2006). Briefly, mouse monoclonal antibodies were incubated in wells at eight or more different concentrations. Next, wells were incubated with goat antimouse horseradish peroxidase (HRP) conjugate, followed by incubation with a Cy3-labeled tyramide substrate. Fluorescence intensities for each spot were measured using a DNA microarray scanner, and the antibody titers were determined for each component on the array. For this study, the titer was set as the largest dilution that produced a signal 5 times higher than the background. This cutoff was typically around 1000 and was chosen to avoid possible concerns related to low signal-to-noise ratios. Throughout the assay, the slides are washed extensively. Therefore, any signal observed on a slide arises from a fairly strong binding event. In total, more than 2000 potential protein–carbohydrate interactions were evaluated in the study.
Overall, the antibodies were far more selective than most lectins. However, over half displayed inappropriate binding relative to the listed specificity (Table II). Fourteen of the antibodies had measurable cross-reactivity with at least one other glycan on the array; 10 of these were regarded as having substantial cross-reactivity (i.e. 
20-fold selectivity for the designated antigen; antibodies T174, T218, F3, A70-C/C8, 92FR-A2, B386, FR4A5, B480, 1A4, and K21). Two antibodies did not bind any glycan on the array (antibodies B460 and 5B5). Finally, one antibody recognized only a specific form of its designated antigen.
Of the eight antibodies to blood group A, B, and H antigens, only 81FR2.2 displayed the expected specificity on the array. B480, also known as 3-3A, cross-reacted with blood group B, whereas CLCP-19B cross-reacted weakly with blood group H1. 92FR-A2 was found to bind both blood group H2 and Fuc1-2Galß1-4[Fuc1-3)GlcNac-HSA (Ley). These results are in agreement with a previous publication (Mollicone et al. 1996) but not with information provided by the company supplying the antibody. B389, also known as A70-A/A9, was found to bind Ley. This antibody is listed as binding blood group H2, Ley, and Fuc1-2Galß1-3[Fuc1-4)GlcNacß1-3Galß1-4Glcß-BSA (Leb) by the manufacturer. In contrast, a recent report showed strong binding to Ley, 30-fold weaker binding to Leb, and no binding to blood group H2 (Christensen et al. 2007). Antibodies B376 (also known as A63-D/B12) and B393 (also known as A46-B/B10) from Biomeda (Foster City, CA) did not show any binding on the array. Two different batches of antibodies yielded identical results. However, the same antibodies obtained from Glycotope (Berlin, Germany) showed substantial binding on the array. As an interesting note, Biomeda lists these antigens as specific for blood group H2, whereas Glycotope lists them as cross-reacting with Ley. On our array, they were found to bind both blood group H2 and Ley. Antibody B460 did not show any binding on our array even after concentrating the antibodies. The lack of binding is not due to the absence of the glycans on the array since results from this study and previous studies verify that every glycan on the array is present and accessible for binding (Manimala et al. 2006). The lack of binding could be due to a production/manufacturing and/or shipping problem of the supplier. Alternatively, the antibody may only recognize the antigen in a specific context. Carbohydrate antigens can be presented on N-linked oligosaccharides, O-linked oligosaccharides, or glycolipids. Differences in presentation can lead to differences in accessibility, spacing of epitopes, and orientation of epitopes. In this case, there are at least some contexts that are not recognized by these antibodies. As a result, they can produce false-negative results (i.e. failure to detect expression of the antigen).
Of the 15 Lewis antigen-binding antibodies, 6 (7LE, 121SLE, CA199.02, 15C02, 28, and ZC-18C) displayed excellent specificity within the context of the array. Of the Galß1-3[Fuc1-4)GlcNAcß1-3Galß1-4Glcß-BSA (Lea)-binding antibodies, T174 cross-reacted with sialyl Lea, whereas PR.5C5 cross-reacted weakly with Gal1-4Gal and rhamnose. Of the Leb-binding antibodies, 2-25LE displayed weak cross-reactivity with Lea, an observation that has also been seen previously (Good et al. 1992; Amano et al. 1997), whereas T218 cross-reacted significantly with both Ley and blood group H1. One of the Galß1-4[Fuc1-3)GlcNAc-HSA (Lex) antibodies, FR4A5, cross-reacted with Glc1-6Glc1-4Glc, Tn, blood group H1, and maltose. All three Ley-binding antibodies showed at least some cross-reactivity to other antigens. F3 bound well to blood group H2 and Lex. A70-C/C8 was found to cross-react substantially with blood group B. Previous studies on the specificity of this antibody had found it to be specific for Ley (Cao et al. 2001; Pochechueva et al. 2002). BR55 displayed the best specificity but still showed weak cross-reactivity to Lex and DiLex, a feature that is not uncommon for Lex-binding antibodies. K21, a Galß1-3GlcNAcß-BSA (Lec)-binding antibody, bound well to Glc1-6Glc1-4Glc, maltose, and rhamnose but did not bind the Lec disaccharide on the array.
Of the four other tumor antigen-binding antibodies, none displayed binding consistent with the listed specificity. Antibody 1A4 bound well to Gb3 but also cross-reacted with blood group B. 5B5, a different Gb3-binding antibody, did not bind any glycan on the array. Lack of binding to Gb3 has been observed previously (Dorken et al. 1989) and appears to be assay dependent and/or context dependent. Antibody B1.1 is listed as a general Tn binder but was only found to bind the clustered form of the antigen, sequences containing two or more GalNAc1-Ser/Thr (Tn) residues linked consecutively on a peptide chain. Although the antibody displayed good selectivity and could be very useful, previous conclusions drawn from binding data may be inaccurate as a result of the difference between the listed specificity and the actual specificity. B386, also known as A68-B/A11, was found to bind blood group B and blood group H1, but no binding to the TF disaccharide or asialo-glycophorin (a protein known to display the TF antigen) was observed. Previous studies have also seen cross-reactivity to blood group B as well as Galß1-3Gal (Pochechueva et al. 2002).
Several factors confirmed that the observed specificity problems were due to the antibodies and not artifacts arising from the microarray. First, the binding profiles were reproducible and observable over a range of concentrations for each antibody. Second, binding preferences were verified by ELISA with individual carbohydrates to ensure that the observed binding was not a result of a misprinted slide or an artifact (see Supplementary data). Third, results for different antibodies to the same antigen were compared to ensure that unanticipated signals were not due to contamination (Figure 1). For example, BR55, F3, and A70-C/C8 all bind well to Ley on the microarray. However, F3 cross-reacted with BG-H2, and A70-C/C8 cross-reacted with BG-B. If BG-H2 or BG-B was contaminated with Ley, all three antibodies would give a positive signal for the contaminated component. As a second example, T174 gives a positive signal with both Lea and SLea. In contrast, 7LE only binds Lea, and 121SLE only binds SLea, demonstrating that neither of these components is contaminated with the other carbohydrate antigen. Representative microarray images for all the antibodies can be found in the Supplementary data.
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Discussion |
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TopAbstractIntroductionResultsDiscussionMaterials and methodsSupplementary dataConflict of interest statementAcknowledgmentsReferences |
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Antibody binding has been used extensively to monitor the expression of carbohydrate antigens, modulate their biological activity, and target specific glycans for clinical applications. Conclusions drawn from these studies are frequently based on the perception that the antibodies are highly specific for their listed antigen. Using a carbohydrate microarray, we show that many of the antibodies currently in use do not have the expected specificity. More than half the antibodies cross-react with other carbohydrate antigens. With these antibodies, the cross-reacting carbohydrate(s) could easily be mistaken for the listed antigen in a biopsy sample, leading to a false-positive or an overestimation of antigen expression levels. Several other antibodies did not bind their listed antigen at all and, therefore, could produce false-negative results. Even the antibodies that were found to be specific within the context of the array may cross-react with glycans that are not present on the array.
Both false-positive and false-negative results can have a major effect on basic and clinical research. For example, information regarding expression of carbohydrate tumor antigens in different tissues has been used to select patient groups for clinical trials. If that information is incorrect, a suboptimal group of patients may be chosen. When studying the biological roles of carbohydrates, properties or functions attributed to a particular carbohydrate antigen may actually be due to a cross-reactive epitope. Given the importance of this information, one must be extremely cautious drawing conclusions from antibody-binding data and interpreting previous conclusions reported in the literature. To avoid false-positive and false-negative results, verification of conclusions with one or more independent analytical methods would be best. However, analysis of carbohydrate expression with two or more different antibodies that bind the same glycan could also yield more reliable information. The data reported in this paper should be a useful resource for selecting appropriate antibodies for studies and for interpreting experimental results arising from those antibodies. Efforts to re-evaluate previous conclusions regarding carbohydrate expression and to develop more effective and reliable methods for monitoring carbohydrate expression should be a high priority for the field.
Finally, the results presented in this paper illustrate the power of carbohydrate microarray technology. Thousands of potential carbohydrate–protein interactions were rapidly analyzed using only nanograms of each neoglycoconjugate. As access to glycans increases into the thousands, the carbohydrate microarray format can easily accommodate the added diversity and provide the throughput necessary to evaluate large numbers of potential interactions. Moreover, the carbohydrate microarray is a powerful tool for screening new antibodies and lectins to identify proteins with improved specificity.
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Materials and methods |
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TopAbstractIntroductionResultsDiscussionMaterials and methodsSupplementary dataConflict of interest statementAcknowledgmentsReferences |
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Materials
81FR2.2 was purchased from DakoCytomation (Carpinteria, CA). CLCP-19B, F3, T218, and T174 were purchased from EMD Biosciences, Inc. (San Diego, CA). B369, B460, B386, B389, B480, B1.1, B393, and B376 were purchased from Biomeda. PR5C5, 28, and ZC-18C were purchased from Chemicon International, Inc. (Temecula, CA). 7LE, 2-25LE, 15C02, 121SLE, and CA199.02 were purchased from Lab Vision Corporation (Fremont, CA). K21 was purchased from Genetex, Inc. (San Antonio, TX) and 5B5 was purchased from BD Biosciences (San Jose, CA). BR55 was purchased from Glycotech (Gaithersburg, MD). A63-D/B12 and A46-B/B10 were purchased from Glycotope. Goat antimouse immunoglobulin (Ig)–HRP (was purchased from SouthernBiotech (Birmingham, AL). The epoxide-derivatized Nunc ArrayCote 16-well microarray slides were purchased from Nalge Nunc International (Rochester, NY), and the arrays were printed by KamTek Inc. (Gaithersburg, MD).
Antibody-binding assay and analysis
The assay is based on previously reported protocols (Manimala et al. 2005, 2006). Briefly, the printed array slides (see Supplementary data for fabrication of the arrays) were incubated with blocking buffer (3% BSA in PBS, 200 µL/well) for 2 h. Serial dilutions of antibodies (50 µL/well, diluted in PBS with 0.3% BSA) were incubated for 2 h in individual wells. Slides were washed 3 times with PBS, incubated for 1 h with goat antimouse Ig (H + L)–HRP (diluted 1:500 in PBS containing 3% BSA), washed 7 times with PBS, and then incubated with 0.1% cyanine 3-tyramide-labeling reagent in PBS containing 1% BSA, 0.01% H2O2, 0.005% Tween 20, and 0.01% polyvinylpyrrolidone for 10 min. Wells were washed with PBS, and the detachable well casings were removed. The slides were incubated in PBS for 5 min and then dried by centrifugation at 900g for 5 min. The slides were scanned on a GenePix 4000B scanner (Molecular Devices Corporation, Union City, CA). The fluorescence was quantified by using GenePix Pro 6.0 software with a GenePix Array List file. The mean values minus the background (typically around 200) for each of the two spots for a particular sample were averaged. Spot-to-spot variability is typically less than 20%. All antibodies were assayed on at least two different slides and data were fully consistent from slide to slide.
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Supplementary data |
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TopAbstractIntroductionResultsDiscussionMaterials and methodsSupplementary dataConflict of interest statementAcknowledgmentsReferences |
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Supplementary data are available at Glycobiology online (http://glycob.oxfordjournals.org/).
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Conflict of interest statement |
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TopAbstractIntroductionResultsDiscussionMaterials and methodsSupplementary dataConflict of interest statementAcknowledgmentsReferences |
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None declared.
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Acknowledgments |
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TopAbstractIntroductionResultsDiscussionMaterials and methodsSupplementary dataConflict of interest statementAcknowledgmentsReferences |
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We thank Jack Simpson (Protein Chemistry Laboratory, NCI) for MALDI MS of BSA conjugates, and Sharanjeet Vedbrat and Amit Jain (KamTek, Inc.) for printing the carbohydrate microarrays. Financial support from the Intramural Research Program of the NIH, NCI, is gratefully acknowledged.
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Abbreviations |
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aBSM, asialo-bovine submaxillary mucin; aGn, asialo-glycophorin; aOSM, asialo-ovine submaxillary mucin; BSA, bovine serum albumin; BSM, bovine submaxillary mucin; deAcBSM, deacetylated-BSM; Gb3, Gal
1-4Galß1-4Glc-HSA; HRP, horseradish peroxidase; HSA, human serum albumin; KLH, keyhole limpet hemocyanin; Lea, Galß1-3[Fuc1-4)GlcNAcß1-3Galß1-4Glcß-BSA; Leb, Fuc1-2Galß1-3[Fuc1-4)GlcNacß1-3Galß1-4Glcß-BSA; Lec, Galß1-3GlcNAcß-BSA; Lex, Galß1-4[Fuc1-3)GlcNAc-HSA; Ley, Fuc1-2Galß1-4[Fuc1-3)GlcNac-HSA |
References |
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 C.-C. Wang, Y.-L. Huang, C.-T. Ren, C.-W. Lin, J.-T. Hung, J.-C. Yu, A. L. Yu, C.-Y. Wu, and C.-H. Wong Glycan microarray of Globo H and related structures for quantitative analysis of breast cancer PNAS, August 19, 2008; 105(33): 11661 - 11666. [Abstract] [Full Text] [PDF]
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