LECTINOMICS:A highway to biomedical/clinical diagnostics

Abstract

The review assesses current status and attempts to forecast trends in the development of lectin biorecognition technology. The progressive trend is characterized scientometrically and reflects the current transient situation, when standard low-throughput lectin-based techniques are being replaced by a novel microarray-based techniques offering high-throughput of detection. The technology is still in its infancy (validation phase), but already shows promise as an efficient tool to decipher the enormous complexity of the glycocode that influences physiological status of the cell. Further enhancement in robustness and flexibility of lectin microarrays is predicted by using recombinant and artificial lectins that will render production of lectin microarrays cost-effective and more affordable. Mass spectrometry is expected to play an important role to characterize the binding profile of new lectins. Differences in glycan recognition by lectins and anti-carbohydrate antibodies are given on a molecular basis, and strong and weak points of both biorecognition molecules in diagnosis are briefly discussed.

Keywords: Lectinology; Biomedical diagnostics; Lectin microarrays; Anti-carbohydrate antibodies; Glycan microarrays; Recombinant/artificial lectins; Mass spectrometry; Modelling of glycan–protein interactions
Article Outline

1. Introduction
2. Lectinomics

2.1. Endogenous and exogenous lectins

3. Scientometry of the literature on the use of lectins in biomedical and clinical diagnostics
4. Lectin biorecognition technology
5. Lectin microarrays
6. Carbohydrate/glycan microarrays
7. Mass spectrometry and related techniques
8. Recombinant and artificial lectins

8.1. Expression of recombinant plant lectins in heterologous systems
8.2. Production and application of recombinant lectins
8.3. Artificial lectins

9. Anti-carbohydrate antibodies in biomedical and clinical diagnostics
10. Scientometry for anti-carbohydrate antibodies in biomedical and clinical diagnostics
11. Carbohydrate–protein interaction profiles and binding affinities
12. Conclusions
Notes added in proof
Acknowledgements
References

1. Introduction
Interest in lectins as reagents for biomedical diagnostics has significantly increased in the new millennium. However, the exact data on whether and how lectins are applied in the routine clinical practice have been as yet very limited. This fact is also corroborated by an absence of sufficiently elaborated reviews on this topic, which is at present limited to a sole monograph by Caron and Sève (2000). This might lead to certain doubts whether lectins are of any practical importance for biomedical diagnostics. There is a long-standing dogma that use of lectins in biomedical diagnostics is outdated and preponderance of the immunodiagnostic tools is definitive (Ferencik et al., 2005).

At the same time, lectins have a very long, more than 140-year history of use in the biomedical diagnostics ([Mitchell, 1860] and [Stillmark, 1888]). As described in recent reviews ([Ambrosi et al., 2005], [Gabius et al., 2004] and [Sharon and Lis, 2004]) and book (Sharon and Lis, 2003), lectins have come a long way since their first detection in plants as hemagglutinins to their present status as ubiquitous recognition molecules with myriad of exciting functions and applications. Ricin and abrin were early made commercially available by E. Merck, which prompted Paul Ehrlich to employ them as model antigens for immunological studies and already in the 1890s, using them, he established several principles of immunology ([Sharon and Lis, 2003] and [Sharon and Lis, 2004]). It seems as if lectins and antibodies were inseparably associated mutually substitutable diagnostic tools for biomedicine.

Since the beginning of the 1980s, the number of purified lectins also started to grow quickly, largely thanks to the advent of recombinant techniques (Sharon and Lis, 2004). At present, the number of identified lectins is estimated to be approximately 1000. Sharon (2007) in his overview of the evolution of lectinology concludes that recognition by lectins in (animal) tissues is undoubtedly one of the major development in glycobiology during the last half of the 20th century. It is predicted that the time of wide application of lectins as diagnostic tools and therapeutic means is yet to come ([Ambrosi et al., 2005] and [Gabius et al., 2004]). The potential of lectins for the diagnosis of a broad spectrum of diseases is based on their ability to decipher the glycocode. For many years, plant lectins were used in attempts to reach this goal ([Gabius et al., 2004], [Sharon, 2007], [Sharon and Lis, 2003] and [Sharon and Lis, 2004]).

In our previous review (Mislovicova et al., submitted for publication), we summarized the most often utilized bioanalytical techniques based on plant lectins used as diagnostic tools to reveal disease-related alterations of glycoconjugates (glycome) described in literature within the past 20 years. The main focus was to show uniqueness of lectins used in diagnosis of the most important diseases, when lectins could not be replaced by other biorecognition molecules. A database thus constructed can be useful at the present swift development of the novel lectin-based (bio)analytical technologies (Nilsson, 2007) or for the preparation of recombinant and new, artificial lectins (Summit Glycoresearch, 2006).

The main aim of the present review is to give an overview about a recently launched powerful detection platform using lectin microarrays with a potential to revolutionize the use of lectins in biomedical diagnosis and glycomics in general. Recent advances of the lectin microarray technology together with further possibilities of future development in the field using recombinant and artificial lectins are discussed in details. Two analytical techniques expected to play important role to characterize binding profile of new lectins i.e. mass spectrometry and glycan microarrays are described in brief. Moreover, strong and weak points of two different biorecognition molecules (lectins vs. anti-carbohydrate antibodies) currently used in biomedical diagnosis are shown with conclusions drawn from molecular modelling of a biorecognition event.
2. Lectinomics

The foregoing discussion implies that lectins possess the ability to act as recognition molecules inside cells, on cell surfaces, and in physiological fluids (Reuter and Gabius, 1999). Lectin–carbohydrate interactions are extensively studied in different scientific disciplines, from basic to applied natural and clinical sciences. Such inter- and multidisciplinarity corroborates the importance of development of the new methodology for the study of lectin–saccharide interactions for the potential applications in clinical diagnostics.

Plant lectins (and also carbohydrate-specific monoclonal antibodies) found equally valuable application to reliably prove the presence of specific carbohydrate structures on cells and tissue sections (Reuter and Gabius, 1999). By the end of 1990s, evidence has been gathered that the cellular protein glycosylation pattern is influenced by several physiological changes, such as occurrence of a disease ([Gabius et al., 2004] and [Kelly et al., 2007]) and that the altered glycoform population of a given glycoprotein might be used for diagnostics of the disease responsible for the alteration itself ([Ambrosi et al., 2005] and [Kelly et al., 2007]). Both quantitative and qualitative lectin-binding differences were observed for numerous target glycoproteins. Thus, lectins are unique cell-recognition markers for carbohydrates and, in a simplified model known as Fischer's “key-and-lock” mechanism (Fischer, 1894), they represent a key, or “the bunch of keys” (Hardy, 1997) to the sugar code ([Ambrosi et al., 2005], [Gabius et al., 2002] and [Gabius et al., 2004]). In other words, whilst sugars are able to carry the biological information, lectins are capable of deciphering their code (the glycocode), thus, they are “decipherers” (Kuno et al., 2005).
2.1. Endogenous and exogenous lectins

According to the nature of the recognition of specific carbohydrates (oligosaccharides) by cognate receptors, two general categories can be distinguished: self recognition involving lectin receptors within the same organism and non-self recognition, in which the receptors are mainly of plant, microbial or parasitic origin (hemagglutinins, adhesins, toxins, etc.) (Gagneux and Varki, 1999). Different mechanisms of action of both endogenous and exogenous lectins are recognized (Gagneux and Varki, 1999). Utilization of the hitherto favoured exogenous, plant lectins in biomedical diagnostics is currently stagnating, whereas big prospects are forecast for endogenous ones ([Gabius et al., 2004], [Sharon, 2007] and [Sharon and Lis, 2003]).
3. Scientometry of the literature on the use of lectins in biomedical and clinical diagnostics

During the years 1989–2003, more than 10,000 papers and 20 books have been published on lectins as a major subject, while more than twice this number of publications appeared, in which lectins were a secondary subject (Sharon and Lis, 2003). Out of this number, 1294 papers dealing with lectins as biomedical diagnostics have been published in the period from 1986 till 2006 including 655 scientific publications and 639 patents (Mullican, 2007a) (Fig. 1). It can be presumed that the growing trend should be retained in both academic (basic) research and its applications.


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Fig. 1. Patent and publication number bar chart for lectins used as diagnostics within period of 1986–2006 (Mullican, 2007a).

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4. Lectin biorecognition technology

With few exceptions (e.g. Helix pomatia and Limex flavus lectins) lectins used for bioanalytical purposes are derived from plants. For limited number of applications, e.g., those based on cell agglutination (for example blood typing or cell separation), or on mitogenic stimulation (assessing the immunocompetence of patients or chromosome mapping), native (non-labelled) lectins will suffice. For most purposes, however, such as detection and assay of glycoconjugates in tissues, on cells or subcellular organelles, and on gels, blots, thin layer chromatograms and microtiter plates, labelled lectins are preferred, many of which are available commercially (Sharon and Lis, 2003). The labelled lectins are usually applied directly to the samples examined and are revealed by appropriate means (Sharon and Lis, 2003). Alternatively, the preparations are treated with an unmodified lectin that is then visualized by a second reagent, for instance horseradish peroxidase or a labelled anti-lectin antibody (Sharon and Lis, 2003). Methods and instrumentations that have been routinely used were summarized recently (Mislovicova et al., submitted for publication). All abovementioned methods have a common drawback — they are not able to provide results in a high throughput fashion needed to crack the complex nature of the glycocode.

Carbohydrate moieties of glycosylated biomolecules are difficult target for analysis due to the branched structure and diverse modifications of saccharidic monomers. Compared with about 40,000 genes in the human genome, the human proteome is predicted to be much larger (Melton, 2004). No one can accurately predict enormous diversity of the glycome at this stage, but one assumption is already clear: the glycome is expected to be more complex than a proteome due to few factors: 1) variability in the sequence of the glycan structure involving various monosaccharides, 2) individual linkage points leading to linear or branched structures, 3) different anomeric configuration of glycosidic linkages and 4) further modification by addition of substituents ([Gabius et al., 2004] and [Turnbull and Field, 2007]). Moreover, presence and structure of glycans can not be read from the genome since this structure is the product of multiple glycosyltransferases and other modifying enzymes. Thus, it is really hard to predict a molecular context associated with a particular glycan modification. In order to crack the glycocode, which represents a significant challenge for current analytical chemistry a diverse range of technologies and strategies are coming into play (Fig. 2).



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Fig. 2. Cracking the glycocode: emerging technologies underpinning glycomics. A range of technologies are now being exploited to undertake large-scale analyses of the structure–function relationships of the glycome. These approaches hold the potential to decode the glycome, leading to new insights and biomedical applications. Reproduced with small modifications from Turnbull and Field (2007) with kind permission from Oxford University Press.

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The emerging analytical tools like lectin microarrays, glycan microarrays and advanced MS techniques have to be supported by generation of large libraries of glycans recognizing proteins produced in large quantities and with high reliability (e. g. by recombinant techniques), production of synthetic glycan receptors, progress in data handling and processing, bioinformatics and molecular modelling of glycan-receptor interactions. Moreover, the use of a complementary biorecognition platform using anti-carbohydrate antibodies might be advantageous in cases where higher specificity and affinity of binding is required.
5. Lectin microarrays

A new emerging technology based on lectin microarray with high-throughput and flexibility of assays might fulfil all criteria needed for rapid and multiplexed monitoring of carbohydrate alterations. The use of lectins as a glycoprofiling tool has become much more sophisticated with the introduction of microarrays, in which panels of lectins are immobilized on a single chip for glycan analysis. The lectin microarrays were introduced in 2005, when 5 independent papers were published ([Angeloni et al., 2005], [Carlsson et al., 2005], [Kuno et al., 2005], [Pilobello et al., 2005] and [Zheng et al., 2005]). So far, the technology was only partly reviewed (Pilobello and Mahal, 2007).

The direct assay format (Fig. 3A) employs lectins immobilized on a chip with subsequent monitoring of a binding event ([Angeloni et al., 2005], [Carlsson et al., 2005], [Fromell et al., 2005], [Kuno et al., 2005], [Pilobello et al., 2005] and [Uchiyama et al., 2006]). A planar surface is modified with lectins either by simple adsorption on nitrocellulose ([Angeloni et al., 2005] and [Rosenfeld et al., 2007]) or preferentially by covalent bond binding to a modified glass ([Hsu and Mahal, 2006], [Hsu et al., 2006], [Kuno et al., 2005], [Lee et al., 2006], [Pilobello et al., 2005] and [Uchiyama et al., 2006]) or a gold surface ([Carlsson et al., 2005], [Mecklenburg et al., 2002] and [Zheng et al., 2005]). Another immobilization techniques are based on a bioaffinity coupling (e.g. biotin–avidin) ([Angeloni et al., 2005], [Carlsson et al., 2005] and [Mecklenburg et al., 2002]) or retention of lectins in a supramolecular hydrogel matrix (Koshi et al., 2006). DNA-driven immobilization of lectins on polystyrene latex particles (d = 230 nm) offers minimal steric hindrance to glycoprotein/cell binding (Fromell et al., 2005).



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Fig. 3. Different ways of preparing lectin microarrays. A) Direct detection — lectins are immobilized on the surface and labelled sample is injected over the surface, B) Reverse phase sample blot — sample is attached to the surface and labelled lectin is then injected, C) Sandwich detection — antibody recognizing sample is immobilized followed by injection of sample and finally by injection of labelled lectin. Legend: L — lectins, S — sample (e.g. proteins or cells), Ab — antibody, F — fluorescent dye.

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Reverse-phase dot-blot lectin array is formed by printing of samples containing glycoproteins on a chip surface, which is then analyzed by addition of lectins over the chip surface ([Angeloni et al., 2005], [Patwa et al., 2006] and [Zhao et al., 2007]) (Fig. 3B). A sandwich format of assay has been shown, when glycan-binding antibody is immobilized first, followed by injection of a sample of glycoproteins and finally by detection with lectins (Fig. 3C) (Chen et al., 2007a).

A typical lectin microarray contains from 6 to 43 lectins immobilized on the surface ([Angeloni et al., 2005], [Kuno et al., 2005], [Tateno et al., 2007] and [Uchiyama et al., 2006]). Dominant detection platform is fluorescence ([Angeloni et al., 2005], [Fromell et al., 2005], [Koshi et al., 2006], [Pilobello et al., 2005] and [Zheng et al., 2005]) or various modified techniques using fluorescence such as evanescent-field fluorescence ([Kuno et al., 2005], [Tateno et al., 2007] and [Uchiyama et al., 2006]) and modified FRET (Fluorescence resonance energy transfer) analysis (Koshi et al., 2006). The glycan containing sample has to be labelled prior to the analysis. This requires an additional labelling step with unwanted variability, because not all biomolecules are labelled with the same efficiency. Moreover, it was observed that dead cells exhibit stronger fluorescence after binding to lectin panel than living ones (Lee et al., 2006) and pre-incubation of CD4+ human T-cells with a dye affected cell binding to carbohydrates on the carbohydrate arrays (Nimrichter et al., 2004). There are some other detection platforms that overcome disadvantages with labelling step by working in a label-free mode e.g. scanning ellipsometry (Carlsson et al., 2005) and surface plasmon resonance (SPR) assays (Mecklenburg et al., 2002). Moreover, it is possible to use a simple optical microscopy to assay cells bound to the lectin panel ([Chen et al., 2007b] and [Zheng et al., 2005]).

The microarray platform of detection is really quick – it takes 4–6 h (Rosenfeld et al., 2007) or 8 h (Kuno et al., 2005) to complete the assays, including the time needed for printing an array with a very low consumption of lectins per spot [(0.5 pg (Angeloni et al., 2005) or 125 pg (Patwa et al., 2006)]. Reproducibility of spotting is in the range of 10–20% (Kuno et al., 2005) and assay reproducibility around 10% (Patwa et al., 2006), what makes this technology very powerful and robust allowing to acquire highly reliable data.

The technology is still in a validation phase, when immobilized lectins are probed with oligosaccharides and glycoproteins ([Angeloni et al., 2005], [Fromell et al., 2005], [Koshi et al., 2006], [Kuno et al., 2005], [Pilobello et al., 2005], [Rosenfeld et al., 2007], [Uchiyama et al., 2006] and [Zheng et al., 2005]); pure culture of microbial (including pathogens) ([Hsu and Mahal, 2006], [Hsu et al., 2006], [Kuno et al., 2005] and [Uchiyama et al., 2006]) or mammalian cells ([Chen et al., 2007b], [Ebe et al., 2006], [Pilobello et al., 2007] and [Tateno et al., 2007]), all having well-known lectin-binding patterns.

Lectin microarrays have provided interesting results so far. They were able to detect differences in cell glycosylation phenotype of Caco-2 intestinal cells with increasing culture time (Angeloni et al., 2005); to detect reduced adherence of enteropathogenic E. coli during cultivation, which is associated with lower glycan expression levels (Angeloni et al., 2005); to distinguish between healthy and cancer mammalian cells (Zheng et al., 2005); to recognize closely related E. coli strains, which is not possible to attain using traditional hemagglutination assay ([Hsu and Mahal, 2006] and [Hsu et al., 2006]). It was also revealed that pathogenic bacterial strains had distinct binding signals and different patterns from those of non-pathogenic origin — providing a fingerprint of bacteria ([Hsu and Mahal, 2006] and [Hsu et al., 2006]). Significant differences in carbohydrate expression were observed on normal and tumorigenic human breast cell lines, as well as on sublines differing in their tendency to “home” to different tissues during metastasis (Chen et al., 2007b) and different glycan profiles were observed between normal Chinese hamster ovary cells and their glycosylation-defective Lec mutants ([Ebe et al., 2006] and [Tateno et al., 2007]). The most recent study showed a different profiling of splenocytes prepared from wild type and (1-3)-N-acetyl-β-d-glucosaminyltransferase II knockout mouse (Tateno et al., 2007). The authors demonstrated the suitability of lectin microarray for profiling dynamic changes in cell-surface glycome during chemically induced differentiation of K562 cells.

A study performed using a lectin panel clearly showed that it was possible to distinguish between patients having bacterial infection (8 serum samples) and healthy individuals (8 serum samples) (Mecklenburg et al., 2002). The authors claim that extended lectin panels have the potential to even distinguish between types of bacterial infection and identify specific disease stages. Another study confirmed differences in glycan patterns between healthy patients and patients having chronic pancreatitis and pancreatic cancer (Zhao et al., 2007). Moreover, it was observed that the glycan expression of chronic pancreatitis serum glycoproteins was more similar to that of the glycoproteins from the normal sera than to glycoproteins from the cancer sera. Sialylation and fucosylation were the major glycosylation differences observed with the progression of a pancreatic cancer (Zhao et al., 2007).

The technology was recently launched on the market using an array of 6 lectins commercially available from the company Qiagen (http://www1.qiagen.com) or by using panel of 45 lectins from the company Moritex (http://www.pgx-glyco.com/eng/glyco/index.html).

Approximately 60 lectins are commercially available with ability to recognize only a fraction of glycan structures present either on the surface or inside the microbial or mammalian cells (Pilobello and Mahal, 2007). There is a clear need for further characterization of lectins (and generally any glycan-binding proteins), currently being undertaken by several laboratories using both carbohydrate microarrays and mass spectrometry. Such studies will ultimately lead to more informative lectin microarray analysis of the glycome. In order to make lectin microarray technology more robust and affordable, large quantity of lectins has to be produced in a reliable way using recombinant techniques. To make glycan profiling high-throughput and more versatile, discovery of new biomarkers is needed. An initial effort to fulfil this task using a blot lectin array has been already launched ([Patwa et al., 2006] and [Zhao et al., 2007]). When a specific biomarker signalling development of a disease will be known, use of anti-carbohydrate antibodies with much higher affinity and specificity might do a better job to recognize the disease at initial phase. Detailed knowledge of the nature of intermolecular forces involved in recognition of glycan structures is essential for bioengineering of lectins or anti-carbohydrate antibodies for their bioanalytical applications. All those aspects will be discussed in the following chapters.
6. Carbohydrate/glycan microarrays

Glycan microarrays, prepared by attachment of glycan biomolecules to a surface in a spatially discrete pattern, provide a low-cost and high-throughput methodology for screening glycan interactions. The chip-based format offers important advantages over classical methods, such as the ability to screen up to several thousand binding events (e.g. DNA microarrays) on a single glass slide and the miniscule amounts of both analyte and ligand required for one experiment.

The technique introduced in 2002 opened the way for discovering new carbohydrate-recognizing proteins including lectins ([Fukui et al., 2002], [Houseman and Mrksich, 2002], [Park and Shin, 2002] and [Wang et al., 2002]). The initial achievements and importance of the new technology were immediately recognized and covered in two review papers ([Love and Seeberger, 2002] and [Mellet and Fernandez, 2002]). The technology is heavily used for mapping the repertoire of carbohydrate recognition structures in the glycome. Progress in the field of high throughput carbohydrate detection was more difficult to achieve compared to advancements in the fields of multiplexed detection of two other important biomolecules — DNA and protein (with DNA microarrays introduced in 1995 (Schena et al., 1995) and protein microarrays launched in 2000 (MacBeath and Schreiber, 2000)). This is because oligosaccharides cannot be readily cloned — they are produced by the action of multiple glycosyltransferases and other modifying enzymes. The other reason is the amount of individual oligosaccharides that can be isolated is usually limited.

Oligosaccharides for preparation of microarrays may be accessed by synthetic approaches (chemically or enzymatically) and by isolation from natural sources ([Feizi et al., 2003] and [Seeberger and Werz, 2007]). The development of a fully automated oligosaccharide synthesis is still difficult due to the complexity of carbohydrates, the large number of monomers needed and possible multiple connections between monosaccharide units (Seeberger and Werz, 2007). Isolation of various oligosaccharides from natural sources will be an important method to discover unknown oligosaccharide sequences in glycome and to reveal the role oligosaccharides play in the nature (Feizi and Chai, 2004).

Immobilization of oligosaccharides on the surface can be done in many different ways including 1) covalent immobilization on a gold surface, 2) covalent immobilization on a glass slide, 3) adsorption on nitrocellulose coated glass slides, 4) adsorption on modified black polystyrene slides, 5) adsorption of neoglycolipids (oligosaccharides linked to lipid) onto a nitrocellulose membrane, 6) adsorption of hydrophobic glycans (linked to a 14-carbon aliphatic chain) in polystyrene wells and 7) immobilization of biotinylated oligosaccharides on streptavidin-coated surfaces ([Culf et al., 2006], [Feizi and Chai, 2004], [Feizi et al., 2003] and [Paulson et al., 2006]). Immobilization should be performed in a way oligosaccharide chain is fully accessible for the interaction from the solution phase (Disney and Seeberger, 2004a).

Fluorescence is still a preferred means of detection followed by a colorimetric reading of the product accumulated as a result of the enzyme used as a tag (Culf et al., 2006). A disadvantage of using a label for detection is discussed in more detail in chapter Lectin microarrays. Only few papers reported the use of label-free approach for signal reading, namely SPR ([Culf et al., 2006], [Karamanska et al., 2008] and [Smith et al., 2003]) which not only allows reading in a microarray format, but also enables to acquire kinetic and affinity constants of interaction between glycans and its ligands.

Carbohydrate arrays, in addition to being applied to study biomolecular interactions, have begun to be used in diagnostics and to conduct epidemiological studies. The arrays have remarkable utility for profiling the specificity of a diverse range of RNA, glycan-binding proteins, including C-type lectins, siglecs, galectins, anti-carbohydrate antibodies, lectins from plants and microbes, and intact viruses including avian influenza viruses ([Blixt et al., 2004], [Disney and Seeberger, 2004b], [Horlacher and Seeberger, 2006], [Stevens et al., 2006a] and [Stevens et al., 2006b]). Hundreds of human sera have been screened for antibodies against the malaria toxin and the technology has been successfully used for the search for serological markers of autoimmune diseases (Seeberger and Werz, 2007). Glycan microarrays are usually prepared by using standard robotic microarray printing technology to couple amino-functionalized glycans to an amino-reactive glass slide (Blixt et al., 2004). The largest glycan array might comprise more than 400 different synthetic and natural glycan sequences representing major glycan structures of glycoproteins and glycolipids developed by the Consortium for Functional Glycomics.

There are still some issues to be resolved including the importance of the underlying peptide sequence on binding affinity of glycans to their binding partners (Adams et al., 2004). An initial study using chemical tags for precise attachment of multiple glycans to a bare protein scaffold was launched with a potential to prepare functional mimics of glycoproteins (van Kasteren et al., 2007). An advantage of using oligosaccharides still attached on the carrier macromolecules is a lower risk of the loss of a binding conformation that could arise during the release of oligosaccharides from their parent molecules (Horan et al., 1999). The other very important issue besides oriented immobilization of oligosaccharides on the surface is the density of the glycans in the array, because an unexpected change in the binding selectivity of a plant lectin on high-density carbohydrate array was found (Horan et al., 1999). Glycan microarrays prepared by using self-assembled monolayers (SAMs) from thiolated glycans can be an elegant solution to control both orientation and density of recognition elements on the surface needed for robust and reliable characterization of binding pattern of glycan-binding molecules (Ratner et al., 2004). Controlled density of glycans on the surface has been achieved using DNA–glycan conjugates hybridized onto a DNA chip, as well (Chevolot et al., 2007).

The major limitation is still the preparation and supply of large number of glycans using more efficient automated synthesis for these complex carbohydrates and by implementation of new purification protocols allowing to isolate glycans from natural sources with high yield and efficiency (de Paz and Seeberger, 2006). A key development will be to couple the microarrays with the mass spectrometry and bioinformatics to decipher the oligosaccharide sequences that are arrayed. An interface of glycan microarray with mass spectrometry will allow the discovery of new and unknown oligosaccharide ligands. When combined with modern protein-expression strategies, this technology is well suited to the systematic identification of carbohydrate-binding proteins in proteomes and their ligands in glycome (Feizi and Chai, 2004).

It is not a trivial task to overcome all current limitations and to address all challenges of the technology, such a task requires collaboration of experts from different complementary fields. This is why several large consortia have been launched such as the Consortium for Functional Glycomics (USA), Collaborative Glycomics Initiative (Europe), Human Disease Glycomics/Proteomics Initiative (Japan), Complex Carbohydrate Research (USA) to make this technology widely accessible to life science researchers. Further information about web resources for glycomics can be found in a recent review paper (Raman et al., 2005).
7. Mass spectrometry and related techniques

The major disadvantage of lectin microarrays is the fact that the lectin is unable to recognize more than 3–5 saccharidic units and therefore the same lectin will bind several types of glycoproteins having structurally similar glycosidic decoration. Naturally, much depends on the content and variability of different glycoproteins in the biological material, since it may often happen that many glycoproteins bound by lectins have negligible informative value for diagnostic purposes. In spite of this, lectin microarrays find successful application in clinical diagnostics, which is mainly due to a prevalent occurrence of the glycoproteins of similar type. However, such elevated concentration of marker glycoproteins often develops only at the later stage of a disease. For this reason, in order to allow for early diagnosis of the disease, when only a low level of biomarkers is present in patient's samples, fast and reliable detection of these biomarkers is required.

Mass spectrometry (MS) techniques seem to be especially suitable for such detection because of the small sample amount needed, high sensitivity, reproducibility, as well as the highly automated procedure of sample preparation and evaluation of the mass spectra by means of available databases. However, it is necessary to emphasize that although mass spectrometry is a very efficient tool, especially for biomarker discovery, it is an extremely sophisticated and costly technique requiring very skilled operators. Although some high-throughput applications have been reported, MS is presently not suitable for clinical diagnosis. Also it appears that the detection is significantly improved if the analyzed samples are beforehand transformed to per-O-methylated saccharides, which however means additional time delay. A correct quantifying of glycan profile also poses a problem. Despite all mentioned drawbacks, MS includes an array of various techniques, which complement each other and, if combined, can render a reliable solution. For this reason, multi-institutional study was performed with a purpose of comparison of the appropriateness of the MALDI-TOF and LC/ESI MS/MS techniques for glycan profiling and an efficient method for identification and quantification of oligosaccharides in glycomic studies was recommended (Wada et al., 2007). It was found that MALDI MS of per-O-methylated oligosaccharides was as reliable as chromatographic methods, and for underivatized oligosaccharide alditols, graphitized carbon-liquid chromatography LC/ESI MS in the negative ion mode provided acceptable quantitation. Another study was carried out to compare the properties of quadrupole ion-trap, a triple quadrupole, and a quadrupole time-of-flight mass spectrometry in profiling the glycopeptide composition (Jiang et al., 2004). While the qTOF mass spectrometry required the lowest sample consumption, all three instruments were useful in profiling the glycopeptide composition.

From the data published, it is evident that for disease diagnosis a combination of lectin microarrays with high throughput quantitative profiling of serum by nanoESI MS/MS is the most suitable (Fig. 4).



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Fig. 4. Bottom-up (A) versus top-down (B) approaches in clinical proteomics and their possible combination with lectin microarray (C).

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Analogously, capture of the intact glycoproteins by lectin microarray followed by trypsin digestion on the spot and subsequent MS analyses would bring fingerprints as well as the structure information of generated glycopeptides. All steps can be fully automated using robotics. Last but not least, deglycosylation of glycopeptides with Protein N-Glycanase F can be performed and identities of these peptides, as well as of the released glycans can be determined by tandem mass spectrometry ([Jiang et al., 2004] and [Wright et al., 2005]), MALDI-TOF/TOF or Fourier-transform ion-cyclotron resonance (FT-ICR) mass spectrometry ([Itoh et al., 2006] and [Peterman and Mulholland, 2006]).

Finally, a fluorescent tag can be also used for detection of the bound glycoproteins by lectin microarray technique. If prior to analysis, samples taken from healthy and sick persons are mixed in an equal ratio, each bearing a tag with a specific mass, the MS techniques would be able to determine the content of specific glycoproteins, glycopeptides, or peptides. The named methods, together with the developed nanotechnology applications (Fromell et al., 2005), would contribute to the design of the efficient clinical assays.
8. Recombinant and artificial lectins

Purification of the lectins from raw material is, in general, a time-consuming process demanding large amount of starting biological material, with exception of many plant lectins, particularly leguminous ones. In addition, the lectin yield is low and purified lectin fractions can be contaminated with diverse range of biomolecules. Additional problems may arise from heterogeneity of lectin-binding properties as a result of “batch to batch” variation of the source of the lectin is isolated from. Thus, isoforms of the same lectin with different binding affinities and/or specificities will introduce additional unwanted variability when used as a diagnostic tool.

An elegant solution to solve the problem of tedious production of lectins from natural sources is a recombinant expression and production offering higher degree of control of final properties of lectins. Moreover, the process of production is more effective and lectins can be prepared with higher purity. The earliest publication on recombinant lectin technology is focused on the study of the relationship between the structure and function of the lectin based on cloning and expression system used, especially when the molecular mechanism of the sugar binding specificity is elucidated, rather than on its application in a clinical practice (Nagahora et al., 1992). The enormous boost of recombinant production of plant lectins in the recent time has been due to access to the complete cDNA libraries of a large number of these proteins (Streicher and Sharon, 2003). Yim et al. (Yim et al., 2001) developed a novel lectin library (genetically engineered lectins) with a variety of specificities distinguishing reliably between different cell types (e.g. human and domestic animal erythrocyte agglutination).
8.1. Expression of recombinant plant lectins in heterologous systems

With the progress of recombinant DNA technology, numerous expression systems have been used for the heterologous production of lectins. Expression in heterologous host systems has clarified the influence of various posttranslational modifications on folding and targeting of carbohydrate-binding properties of plant proteins, ranging from bacteria (Escherichia coli), yeasts (Pichia pastoris, Saccharomyces cerevisiae) to plant (e.g. tobacco) or mammalian cells (e.g. monkey kidney). Mainly Escherichia coli and Pichia pastoris have been successfully employed for the large-scale production of heterologous properly folded proteins used in the industry due to their well-known genetics, fast high-density cultivations and the large number of compatible biotechnological tools available ([Bretthauer and Castellino, 1999] and [Menéndez et al., 2005]). Unicellular eukaryotes such as yeasts are related more closely to plants than to bacteria and are more suitable than bacteria for the expression of mammalian or plant proteins, mainly to overcome problems with low yield and absence of a posttranslational modification such as glycosylation (Streicher and Sharon, 2003).

The recombinant plant lectin (RPL) should have the same specificity and activity as the native protein, but the structure and possible binding properties might be slightly different, because in plants lectins undergo co- and posttranslational processing reactions. Furthermore, lectins expressed in bacteria lack their carbohydrate moieties, whereas glycans of those expressed in yeasts or other cells may carry different glycans compared to lectins isolated from natural source (Streicher and Sharon, 2003). The risk of incorrectly produced glycosylated protein during heterologous production in P. pastoris was demonstrated, when it was found that the Phaseolus vulgaris agglutinin (PHA) was hyper(over-)glycosylated during recombinant production (Raemaekers et al., 1999). This is why it is important to check integrity and binding properties of the lectin after recombinant production.

A commonly used vector for production of unfused plant lectins is pET expression system containing the T7 promoter, which is then transferred to a suitable host, particularly into the E. coli BL21/DE3 strain (Studier et al., 1990). A common problem is the formation of inclusion bodies by the bacteria to overcome the cytotoxic effect of over-expressed heterologous protein and, as a result, a small fraction of RPL is obtained in a soluble form. Often, more than 90% of lectins form insoluble aggregates, but a small fraction refolds in a correct manner and regains a carbohydrate-binding activity. To facilitate the protein purification, it is possible to choose one of many fusion tags attached to the expressed protein in a form of a fusion protein.
8.2. Production and application of recombinant lectins

Recombinant methods, in general, typically provide larger quantities of the protein in a relatively much shorter period of time compared with those of native ones. Altogether, more than 200 lectins from diverse sources have been cloned and expressed in heterologous systems (Sharon and Lis, 2003) offering thus a means to obtain pure protein samples of defined amino acid sequences (Lannoo et al., 2007). We found more than 40 papers dealing with production of recombinant lectins, dated to 2007, of which a representative pattern of 22 was chosen regarding a still increasing demand for their biotechnological application in medicine: 14 of exogenous, 5 of endogenous, and 3 of another natural sources (Table 1).

Table 1.

Overview on recombinant lectins
Plant lectins
Recombinant lectin Sugar binding specificity Donor/clone Recipient: producing bacterial/yeast host medium low asteriskDetermination method Production/yield [mg/L]; equipment Reference
rec Pea lectin derived from: Pea lectin (Pisum sativum) seeds Mannose; Glucose Cloned gene: two lectin cDNA sequences isolated Expression of rec Pea lectin in Escherichia coli strain W3110 → rec lectin formation (single polypeptide chain) aSDS/PAGE; Hemagglutination assay 2–5 mg/L (1 L broth) Stubbs et al. (1986)
rec Wheat germ agglutinin isolectin 2 (WGA2) derived from: WGA2 plant lectin GlcNAc; Neu5Ac Genes encoding pre/prepro protein of WGA2 in vitro synthesized Expression of rec lectin in the two yeast Saccharomyces cerevisiae strains (KK4; KS58-2Ddel) and secretion Electrospray ionization mass spectrometry; SDS/PAGE analysis Not reported Nagahora et al. (1992)
rec Lectin (rECorL) derived from: Native legume lectin ECorL (Erythrina corallodendron) plant Gal/GalNAc Mutant cDNAs; sequenced and cloned Expression of rec lectin and refolding of lectin mutants in Escherichia coli lysogenic strain BL21(DE3) Hemagglutination assay; ECorL-Asialofetuin binding assay; SDS/PAGE analysis 3–7 mg/L (1 L culture) Arango et al. (1993)
rec PNA derived from: Peanut agglutinin (Arachis hypogea) seeds Terminal α- and β-galactosides Coding sequence for PNA cloned Expression of rec lectin clone in Escherichia coli NM522 single colonies Western blotting with polyclonal anti-PNA Immunoglobulin G; Hemagglutination assay 200 mg/L (Large-scale) Sharma and Surolia (1994)
rec SBA derived from: Soybean agglutinin GalNAc cDNA of SBA cloned using pET11b vector Expression of plasmid DNAs in Escherichia coli strain BL21(DE3)pLysS Hemagglutination assay; SDS/PAGE analysis; Deglycosylation 1 mg/L (two 12 L fermentors) Adar et al. (1997)
rec Ricin A derived from: Ricin toxin (RT) and Ricin communis agglutinin (RCA) (Ricinus communis) seeds Gal/GalNAc Three full-length ricin-related clones isolated Expression of rec Ricin A in Escherichia coli procaryotes (MC1000 lambda lysogen); pRAL6-transformed E. coli MM294 strain Polyacrylamide gel analysis Not reported (10 L fermentor) Piatak (1998)
rec PHA and rGNA lectins derived from: Agglutinins (Phaseolus vulgaris; Galanthus nivalis) plants Mannose Genomic DNA isolated; b PCR primers cloned and sequenced Expression and secretion of linearized plasmids in Pichia pastoris strains GS115/KM71 SDS/PAGE analysis; Chemiluminescence; Western blotting 1–2 mg/L (50 mL culture) Raemaekers et al. (1999)
rec ML derived from: Mistletoe lectin (Viscum album) plant GalNAc; Neu5Ac Gene sequence encoding the mistletoe lectin chain (rMLA, rMLB) Expression of rec ML in Escherichia coli strain BL21/pT7 Western blot assay; c ELLA test; SDS/PAGE Not reported (2 L flasks) Lentzen et al. (2001)
rec PHA-E derived from: Phytohemagglutinin E- form (Phaseolus vulgaris) kidney bean Mannose pPICZ-PHA-E plasmid linearised with PmeI Expression and secretion of rec lectin in the methylotrophic yeast Pichia pastoris strain X33 d MALDI-TOF MS; SDS/PAGE; Western blot analysis; e ELISA; Amino-terminal sequence analysis; Hemagglutination assay 100 mg/L (2 L; 15 L; 200 L fermentor) Baumgartner et al. (2002)
rec ECL derived from: ECL lectin (Erythrina cristagalli) seeds Galactose Coding sequence for ECL cloned Expression of rec ECL in Escherichia coli lysogenic strain BL21(DE3) SDS/PAGE; Western blot analysis; Isoelectric focussing; Hemagglutination assay 870 mg/L (7 g/8 L fermentor) Stancombe et al. (2003)
rec GNA derived from: Agglutinin (GNA) (Galanthus nivalis) plant bulbs α-d-mannosyl groups pPICZ-GNA plasmid linearised with PmeI Expression of rec GNA in Pichia pastoris methylotrophic strain X33 SDS/PAGE; Western blot analysis; ELISA; Edman degradation; Hemagglutination assay; N-Terminal sequence analysis 80 mg/L (200 L fermentor) Baumgartner et al. (2003)
rec Agglutinin (PTA) derived from: PTA agglutinin (Pinellia ternata); MBL; tuber plant from the Araceae family Mannose cDNA-pQE-30 expression vector clone; Expression plasmid pQE-PTA Expression of rec PTA lectin in Escherichia coli strain M15 (pREP4) SDS/PAGE analysis; Hemagglutination assay; Western blot analysis 10 mg/L (Flask culture) Lin et al. (2003)
rec SALT lectin derived from: SALT agglutinin (Oryza sativa L.) from rice roots Mannose cDNA clone encoding protein (pSaltPet expression plasmid) Expression of rec SALT lectin in Escherichia coli strain BL21(DE3) Hemagglutination assay; Western blot assay; SDS/PAGE analysis; Densitometry 7.30 mg/0.50 L Branco et al. (2004)
rec MAH derived from: Native legume MAH lectin (Maackia Amurensis) hemagglutinin Carbohydrate chains containing Neu5Ac 24 nucleotides cDNA mutated; 16 clones chosen Expression of mutants as glutathione-S-transferase fusion proteins in Escherichia coli strain BL21 Hemagglutination assay; PAGE analysis; Agglutination of human and domestic animal erythrocytes Not reported Yim et al. (2001)
rec Conglutinin derived from: Bovine conglutinin (Calcium-dependent mammalian C-type lectin) Glucose; Lactose; d-Fucose; Mannose Native conglutinin cDNA Expression of rec Conglutinin in Escherichia coli strain JM109 SDS/PAGE; Western blotting; Coomassie Protein Assay Reagent; ELISA; Microtiter plate assay system 2.80 mg/L (Large-scale) Wakamiya (2000)
rec Lectin derived from: Lectin (C-type) (Asterina pectinifera) starfish (Marine invertebrate) Terminal α-GalNAc Recombinant plasmid with a full-length cDNA encoding the lectin Expression of rec lectin (C-type) in Escherichia coli strain JM109 SDS/PAGE; Hemagglutination assay; f TLC; ELISA Not reported Kakiuchi et al. (2002)
rec Human mannan-binding lectin derived from: Natural MBL Mannose MBL/pREP9 plasmid DNA construct Expression of rec human MBL (in vivo/in vitro) in eukaryotic human embryonal kidney cell culture (HEK 293 cell lines); E. coli strain TOPF10 SDS/PAGE analysis; Western blot analysis; TRIFMAg Not reported (Thiel et al., 2004) and (Thiel et al., 2005)
rec Gal-1 derived from: Human Galectin-1 (Bone marrow cells) β-Galactoside cDNA of Gal-1 cloned Expression of rec human Gal-1 in Escherichia coli strain BL21 SDS/PAGE; h HPLC; Mass spectrometry 10 µg/mL (25 mL flasks) Vas et al. (2005)
rec MBL derived from: Mannose-binding lectin (Ficolin; from: human blood plasma) Mannose Gene expression construct with a cDNA sequence; encoding MBL peptide Expression of rec MBL in culture of human embryonal kidney cells (HEK 293 cell lines) SDS/PAGE; Western blotting; ELISA; TRIFMA Not reported (10 L/Large-scale) Matthiesen (2006)
Lectins from other natural sources (fungal; Rhodophyta)
rec AAL lectin derived from: AAL lectin from fruit bodies of (Aleuria aurantia) — an ascomycete mushroom Fucose cDNA and genomic DNA cloned; pPIC9K-AAL plasmid linearized Expression of rec AAL in the methylotrophic yeast Pichia pastoris strain GS115 SDS/PAGE analysis; Hemagglutination assay 67 mg/L (3 L fermentor) Amano et al. (2003)
rec PSL derived from: Lectin (Polyporus squamosus) mushroom Neu5Acα2-6Galβ1-4GlcNAc trisaccharide sequence Protein-coding regions of PSL1a and PSL1b cloned into expression vector pET-43a Expression of rec lectin in Escherichia coli Nova Blue (DE3) strain PAGE; SDS/PAGE; Western blotting; Hemagglutination assay; Hapten inhibition assay; Quantitative precipitation; i CD spectroscopy 4–7 mg/L (1 L culture) Tateno et al. (2004)
rec His-GRFT derived from: low asterisklow asteriskGriffithsin (Griffithia sp.) marine red algae Glu/GlcNAc Recombinant vector with N-terminal hexa histidine-tagged GRFT coding sequence Expression of rec His-GRFT in Escherichia coli strain BL21(DE3) MALDI-TOF MS; j SEC; SDS/PAGE; ELISA 819 mg/L (20 L fermentor) Giomarelli et al. (2006)
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low asteriskDetermination method — used for comparison of native/authentic and recombinant lectins.

low asterisklow asteriskRhodophyta kingdom (Protoctistae) — 4000 species (no animal, plant, fungus).
a SDS/PAGE — Sodium dodecyl sulfate polyacrylamide gel electrophoresis.
b PCR — Polymerase chain reaction.
c ELLA — Enzyme-linked lectinosorbent assay.
d MALDI-TOF MS — Matrix-assisted laser desorption-ionization-time of flight mass spectroscopy.
e ELISA — Enzyme-Linked ImmunoSorbent Assay.
f TLC — Thin-layer chromatography.
g TRIFMA - Time-resolved immunofluorimetric assay.
h HPLC — High performance liquid chromatography.
i CD — Circular dichroism.
j SEC — Size exclusion chromatography.

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A small/laboratory-scale production of recombinant lectins is illustrated by some authors ([Bezerra et al., 2006], [Lannoo et al., 2007], [Raemaekers et al., 1999] and [Vas et al., 2005]). Lower yield of some recombinant lectins, e.g. recSBA, may be preferentially caused by higher number of subunits (Adar et al., 1997). Milligram quantities of a unique recombinant Polyporus squamosus lectin (PSL), having a carbohydrate-binding specificity for sialylated glycoconjugates, was prepared for biomedical studies (Tateno et al., 2004). Nevertheless, higher yields of some plant lectins of interest from the leguminosae family (20 mg/L) e.g. Dolichus biflorus lectin (DBL) (Chao et al., 1994) or Pisum sativum lectin (PSL) (Prasthofer et al., 1989) were achieved. A 10–20 mg/L yield of Viscum album mistletoe lectin (MLA, MLB) from the loranthaceae family was obtained ([Eck et al., 1999a] and [Eck et al., 1999b]). A functional fungal Aleuria aurantia lectin (AAL) was used for investigation of fucose-containing saccharides in the field of molecular cell biology and clinical diagnosis (Amano et al., 2003). A 2 L flask fermentation process was also successfully applied for preparation of a recombinant mistletoe lectin (RML) with a possibility for therapeutic use in cancer therapy (Lentzen et al., 2001). A nucleocytoplasmic Nicotiana tabacum plant lectin exhibited biological activities such as agglutination activity and interaction with GlcNAc-oligomers and glycoproteins (Lannoo et al., 2007).

A large-scale bioreactor purification scheme for preparation of biologically active plant recombinant Erythrina cristagalli lectin (ECL), functionally equivalent to native ECL, depended on a refolding ratio (1:10; 1:14; 1:18) with a corresponding high yield of either 294 mg/L, 360 mg/L, or final 870 mg/L using an 8 L bioreactor (Stancombe et al., 2003). A large-scale production of other recombinant lectins has also been reported by others ([Baumgartner et al., 2003], [Baumgartner et al., 2002], [Bezerra et al., 2006], [Giomarelli et al., 2006] and [Sharma and Surolia, 1994]).

Exogenous lectins in their native form are, in general, broadly used either in clinical diagnosis of diseases or in the treatment of specific diseases. Recombinant lectins of different origin can be used in clinical trials, as well ([Amano et al., 2003], [Kakiuchi et al., 2002], [Lentzen et al., 2001] and [Tateno et al., 2004]). A practical importance of therapeutic treatment of diseases using lectins either in their native ([Giomarelli et al., 2006], [Lin et al., 2003], [Matthiesen, 2006], [Thiel et al., 2005], [Vas et al., 2005] and [Wakamiya, 2000]) or recombinant forms has also been experimentally verified ([Giomarelli et al., 2006], [Lentzen et al., 2001], [Matthiesen, 2006], [Thiel et al., 2005] and [Wakamiya, 2000]). Recombinant lectins may also be used in plant protection; e.g. a SALT lectin is toxic towards phytopathogenic fungus (Branco et al., 2004).

Recently, an enhanced interest has been shown in the preparation of recombinant lectins using endogenous (animal) lectins as sources ([Kakiuchi et al., 2002], [Matthiesen, 2006], [Thiel et al., 2005], [Vas et al., 2005] and [Wakamiya, 2000]). Biotechnological large-scale production of recombinant lectins to be used preferentially in clinical diagnosis of diseases is still a significant future challenge. Research and development activities for application of animal lectins for pharmaceutical or therapeutic purposes are underway (Irimura et al., 2007).

So-called personalized medicine may be represented e.g. by a plasma-derived or recombinant, mannose/mannan-binding lectin (MBL)-replacement therapy. The safety and efficiency of the MBL therapy has yet to be proven, being potentially useful for treatment of MBL-deficient patients to reduce susceptibility to bacterial infection. Additionally, MBL could be used as a disease-modifying drug to reduce the severity of rheumatoid arthritis and to preserve lung and liver function in cystic fibrosis (Summerfield, 2003).
8.3. Artificial lectins

Large-scale stable production of artificial lectins with new and variable glycan affinities in the future lectin-based diagnostics is anticipated ([Summit Glycoresearch, 2006] and [Irimura et al., 2007]). Further, artificial lectins may be used for cellular quality control and evaluation of equivalence and the development of cultured cells in regenerative medicine, as well. The other field artificial lectins can be used in is a quality control of glycoproteins designed for use as “biopharmaceuticals”. Cell-specific artificial lectins have been applied in flow cytometry examination and a technology to combine cell-specific artificial lectins and magnetic beads is in the development process ([Summit Glycoresearch, 2006] and [Irimura et al., 2007]).

To date, the majority of mutagenesis studies used to prepare mutant plant lectins with point mutations, insertions, or deletions aimed at identifying the role of distinct amino acids essential for lectin activity. The group of plant lectins that has been most widely studied by active site mutagenesis are those isolated mainly from legumes. Recently, preparation of modified gene cassette by mutagenesis and synthesis of both strands of the target cDNA cloned then into an expression vector, has become a commonly used procedure (Streicher and Sharon, 2003). A novel Sia-recognition protein (Sia-binding lectin) was successfully constructed (Yabe et al., 2007) and is expected to contribute as a useful tool in sialoglycomics. The method of preparation is based on the strategy of ‘natural evolution — mimicry’ using an error-prone PCR from a Gal-binding scaffold protein (EW29Ch). One of the evolved mutants, SRC (Sia-recognition EW29Ch), showed a novel affinity for Neu5Acα2-6 (Yabe et al., 2007).
9. Anti-carbohydrate antibodies in biomedical and clinical diagnostics

Antibodies specific to carbohydrates (we use the term anti-carbohydrate antibodies throughout this article) and their potential to complement and/or replace lectins have recently gained attention of the scientific community working in the lectinomics field. Anti-carbohydrate antibodies appear in human serum and tissues in relatively high quantities and a very wide variety. Multiple connections of the anti-carbohydrate antibodies appearing in sera to specific disease conditions have recently been identified. The current status of anti-carbohydrate antibodies will be briefly reviewed here and some examples of practical applications will be given later in this chapter. Current review articles comparing anti-carbohydrate antibodies to lectins are not available. Available are some earlier reviews summarizing the state of anti-carbohydrate antibodies in 1997 (Pazur, 1998), as well as reviews dealing with anti-carbohydrate antibodies indirectly or only briefly ([Ambrosi et al., 2005], [Kirkeby et al., 2004], [Mammen et al., 1998], [Sharon and Lis, 2003] and [Wearne et al., 2006]).

The principal difference between lectins and antibodies are their binding properties: antibodies are generally more specific and binding affinity is higher, e.g. the affinity of murine monoclonal antibody against trisaccharide epitope was reported to be in nanomolar range (Muller-Loennies et al., 2000). Arraying antibodies is therefore not necessary in diagnostics and their practical applications are often based on a single anti-carbohydrate antibody. Antibody arrays have the potential in high-throughput glycan screening though. Not very many antibody applications, compared to lectins, have been reported so far, however it seems that lectins and lectin arrays are better in deciphering complex carbohydrates and their changes, while antibodies are more suitable to detect specific carbohydrate epitopes related to certain disease conditions. Availability for practical applications is more favourable for antibodies, the number of available specificities being very high in comparison to lectins. Another favourable circumstance is that methods of large-scale industrial production of monoclonal antibodies for therapeutic purposes, mostly of IgG class, are easily transferable to the production of diagnostic antibodies.

The comparison of lectins to antibodies with the emphasis on their parameters important for the future application in the biomedical and clinical diagnosis is summarized in Table 2.

Table 2.

Comparison of potential of anti-carbohydrate antibodies and lectins in biomedical and clinical analysis
Property Lectins Antibodies
Origin Naturally occurring in plants or animals Produced by immune response in mammals, polyclonal or monoclonal
Valency of interaction Single/multi Single, possible avidity
Selectivity for receptor Low/mediate High
Affinity to receptor Low/mediate High
Structural complementarity Low/mediate High
Commercial production Isolation from natural materials, some recombinant lectins already produced by recombinant cells Fermentation production by mammlian or bacterial cells (hybridoma culture or phage display)
Form for user Mostly labeled Use of secondary antibody or labeled
Availability Mediate (< 1000), limited by natural occurrence High (> 38,000), possibility to generate antibodies specific to any immunogenic carbohydrate structure
Production, industrial Currently mostly isolation from natural materials 15–20,000 L reactors, max 2 g mAb/L medium
Full-size table

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There are two principal cases how antibodies can be used in diagnostics: I) naturally occurring epitope associated with a certain disease condition is detected by an artificially prepared antibody and II) naturally occurring anti-carbohydrate antibody associated with a certain disease condition is detected usually using a certain immunodiagnostic technique. Historically, the main focus was on the role of anti-carbohydrate antibodies in vivo (case II), however recently multiple reports have been published that describe application of anti-carbohydrate antibodies in the diagnosis of some diseases, tissue typing etc (mostly case I).

Clinical and experimental experiences with ABO-incompatible organ and marrow allotransplantations and addressing the mechanism, by which organs or cells survive in the presence of natural anti-carbohydrate antibodies were recently reviewed (Wu et al., 2003 A. Wu, L.H. Buhler and D.K. Cooper, ABO-incompatible organ and bone marrow transplantation: current status, Transpl Int 16 (2003), pp. 291–299. View Record in Scopus | Cited By in Scopus (31)Wu et al., 2003). Methods based on α-d-galactose detecting lectins and antibodies to identify potentially xenogenic α-d-galactose in donor tissues were published recently (Kirkeby et al., 2004). Xenograft rejection can be attenuated by removing antibodies to identified carbohydrate or administering xenogenic antigen before transplantation (Good et al., 1998). Refined carbohydrate-based therapeutics and adsorbents of natural anti-carbohydrate antibodies are currently considered as possible anti-rejection therapies (Holgersson et al., 2005).

Carbohydrates in the form of capsular polysaccharides or lipopolysaccharides are a major component of the bacterial surface and there is an obvious potential to detect these pathogens and diagnose the diseases caused by them. Immunology of bacterial carbohydrates was exhaustively reviewed (Weintraub, 2003). Pathogens belonging to the genus Chlamydia contain specific lipopolysaccharide and use of murine monoclonal antibodies binding to its trisaccharide epitope was employed to detect chlamydial lipopolysaccharide (Muller-Loennies et al., 2000). Another example is the use of monoclonal antibodies against lipopolysaccharide from Campylobacter jejuni to detect Guillain-Barré (GBS) and Fisher (FS) syndrome (Houliston et al., 2007).

Anti-carbohydrate antibodies appear in human blood also in inflammatory, autoimmune, and other diseases. The use of anti-glycan antibodies as biomarkers for diagnosis and prognosis of inflammatory and autoimmune diseases was reviewed by Dotan et al. (2006). A pattern of anti-carbohydrate antibody responses was found to be associated with the presence of advanced atherosclerosis (Mosedale et al., 2006).

Tissues contain a wide variety of surface carbohydrates suitable as targets for immunodiagnostics. The use of lectins was combined with anti-carbohydrate antibodies to differentiate human embryonic stem cells (Wearne et al., 2006). A monoclonal anti-carbohydrate antibody was used to stain blots from myosin preparations (Kirkeby, 1996). Breast milk contains many different carbohydrate structures and these protein-bound carbohydrate epitopes in breast milk from different biological species were phenotyped using lectins and anti-carbohydrate antibodies (Gustafsson et al., 2005).

Anti-carbohydrate antibodies are currently being applied also outside biomedical diagnostics or in some interdisciplinary fields. Anthrax spores contain tetrasaccharide side-chain on their surface, which can be detected using suitable antibodies (Tamborrini et al., 2006). Gum additives in processed food were detected by polyclonal antibodies raised in rabbit immunized with polysaccharide gum (Pazur and Li, 2004). Multiple pectic epitopes were reported to be recognized by anti-pectin monoclonal antibodies (Willats et al., 2000). Two antibodies with specificity for d-xylose residues and d-galacturonic acid residues of flaxseed polysaccharides were isolated and characterized (Pazur et al., 2001).
10. Scientometry for anti-carbohydrate antibodies in biomedical and clinical diagnostics

Anti-carbohydrate antibodies began to be widely applied in biomedical diagnostics only after 2001 (Mullican, 2007b). The period of 2001–2005 is characterized by precipitous increase of the number of publications and patents dealing with both anti-carbohydrate antibodies and lectins. However, the respective number (446 publications and 151 patents) were lower than those for the lectins. Beginning from 2004-2005, a number of publications and patents on anti-carbohydrate antibodies has not risen (Mullican, 2007b), whereas the use of lectins in diagnostics continues to be attractive (Fig. 1), which is not the case of immunodiagnostics (Fig. 5).



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Fig. 5. Patent and publication number bar chart for anti-carbohydrate antibodies used as diagnostics within period of 1986–2006 (Mullican, 2007b).

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11. Carbohydrate–protein interaction profiles and binding affinities

Detailed knowledge of the nature of intermolecular forces provides helpful information for bioengineering of lectins or anti-carbohydrate antibodies for both, therapeutic or diagnostic praxis. Experimental methods, such as X-ray (Krengel and Imberty, 2007) or NMR (Groves et al., 2007) are commonly used to elucidate carbohydrate–lectin interactions. Recent development in computer and information technologies opened unprecedented opportunities also for efficient modelling of these interactions.

We will address the ability of molecular modelling to predict binding differences in selected protein–carbohydrate complexes, in order to highlight the difference in carbohydrate recognition by lectins and antibodies. For example, glycophorin A, the major protein of erythrocytes, can interact and bind to different macromolecules, such as lectins, antibodies and antigens. Sialoglycopeptide segment from glycophorin A binding with wheat germ agglutinin (WGA) has been experimentally elucidated (Wright and Jaeger, 1993) and the sugar–lectin interactions have already been modelled ([Neumann et al., 2002] and [Neumann et al., 2004]).

The experimental structural data for WGA is accessible in the PDB (Protein Data Bank) as 2CWG. Two alternative conformations for glycophorin A NeuNAc(α2-3)-Gal(β1-3)-NeuNAc(α2-6)-GalNAc sialotetrasaccharide (ST) are detected, namely the extended conformation in the major binding mode and the bent conformation in the minor binding mode. More recently, a modelling study analyzed the binding of O-glycan NeuNAc(α2-3)-Gal(β1-3)-NeuNAc(α2-6)-GalNAc of glycophorin A with the binding site of RII, the erythrocyte binding domain of EBA 175 (erythrocyte binding antigen of Plasmodium falciaparium) (Tolia et al., 2005). At the same time, the experimentally determined structure (by X-ray crystallography) of NNA7 Fab complexed with 2-(N-morpholino)ethanesulfonic acid (MES) molecule, suggested the mode of blood-group recognition (Xie et al., 2005). Xie et al. (2005) hypothesized that the MES molecule might be able to mimic the galactose residue of the glycophorin tetrasaccharide, however detailed molecular interaction pattern between the ST tetrasaccharide and NNA7 Fab remained elusive. Thus, we utilized the power of computational molecular modelling for a comparative analysis of interaction patterns between ST and the WGA lectin, and ST and the NNA7 Fab and a point mutant NNA7-G91S. The results from the docking calculations allowed us to identify the differences between lectin and antibody binding profiles.

AutoDock (Goodsell et al., 1996) based calculations are compared to experimental results in Fig. 6 and Fig. 7, and indicate that the docked tetrasaccharides fit in an extended and bent conformations into the WGA binding sites. As it can be seen from the presented figures, the docking results reasonably mimic the X-ray data. Matching to the X-ray derived structure, all interactions of sialic acids with Tyr21, Tyr23, Ser19, Asp29 and Tyr30, were reproduced in the computational AutoDock model for the lowest-energy bent conformation. Additionally, all direct interactions within the major binding site were detected. It is noteworthy to mention, that the water-mediated interaction with Glu115 is absent, however this is due to lack of inclusion of water molecules in this modelling study.



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Fig. 6. Top: Carbohydrate-binding of the glycophorin A sialylated tetrasaccharide in the minor (A) and major (B) binding sites of wheat germ agglutinin (WGA) in bent and extended conformations. The 2CWG oligosaccharide structural data retrieved from PDB are in light blue colour and visualized in ball and stick representation. The oligosaccharide docking results are visualized in yellow stick representation. The protein surface is in gold colour for the A chain and violet for the B chain of 2CWG visualized with Accelrys Discovery Studio Visualizer (Accelrys, 2002–2006). Bottom: Binding prediction of the same tetrasaccharide into NNA7 Fab (PDB code 1T2Q; C) and corresponding G91S mutant; (PDB code 2D03; D). The circles highlight the difference in the location of sialic acid of the glycophorin antigen. The co-crystallized MES is shown in CPK spice filling spheres. The surface of the L chain of both antibody fragments is in red colour, the H chain in blue colour.

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Fig. 7. Top: Ribbon representation of the bivalent WGA with detailed description of the interaction pattern (drawn using Ligplot program (Wallace et al., 1995) for the bent (A) and extended (B) tetrasaccharide conformation. Bottom: Ribbon representation of NNA7 Fab and of the G91S mutant. The mutation position on C and the superposition of sialic acid with MES on D are indicated by arrows. The colour coding is the same as in Fig. 6.

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When the interaction between tetrasaccharides and the antibody fragment was analyzed, as illustrated in Fig. 6 and Fig. 7, computational modelling was able to confirm the binding differences between the NNA7 Fab and G91S mutant. In case of NNA7 Fab, one of the hydroxylic groups of sialic acid creates a hydrogen bond at 2.9 Å with Gly97. [Note that Gly97 (as presented in PDB) corresponds to Gly91 based on Kabat numbering scheme for numbering the residues in an antibody used in reference (Xie et al., 2005)]. The oligosaccharide is further stabilized by interactions with Trp52, Gly55 (from CDR-2) and Tyr102, Tyr104 (from CDR-3) within the binding site. As it was originally proposed, the galactose residue loosely superposes with MES. However, when the interaction of oligosaccharide with the G91S mutant was analyzed, the sialic acid matched the position of MES instead of galactose. Due to the point mutation, the stabilizing hydrogen bond with Gly97 was abolished. Other residues within CDR-2 and CDR-3 remained involved in stabilizing the interaction with the carbohydrate ligand.

The docking methods, in addition to predict the detailed protein–ligand interaction patterns, can reasonably well forecast binding affinity in carbohydrate–lectin and carbohydrate–antibody complexes. The efficiency of carbohydrate recognition with lectins of anti-carbohydrate antibodies can thus be appraised accordingly. Such comparative data are still missing in the literature. According to the AutoDock results, WGA binds the tetrasaccharide slightly less efficiently than the antibody does. The difference in the binding energies is quite low — below 1.5 kcal/mol and can easily be compensated by lectin's multivalency. More molecular modelling data for several carbohydrate–lectin and carbohydrate–antibody complexes together with isothermal calorimetry (ITC) or SPR studies are advised in order to generalize the carbohydrate recognition preference. More importantly, computer-aided drug design together with virtual screening methodologies can be further exploited to design molecules with selective binding for diagnostic and/or therapeutic purposes.
12. Conclusions

Although lectin microarrays have provided additional information with reference to traditional lectin-based analytical tools (e.g. clear distinction between sera from healthy and sick people ([Mecklenburg et al., 2002] and [Zhao et al., 2007])), the technology has not yet reached the status of a standard, routine, and robust diagnostic tool in disease diagnosis. The range of lectins currently used in microarrays is not sufficient to cover the enormous complexity of glycan structures, however this deficiency can eventually be overcome using artificial/recombinant lectins (Ferrand et al., 2007) carefully designed with the aid of modelling studies. Detailed knowledge of the nature of intermolecular forces involved in recognition of glycan structures is essential for bioengineering of lectins or anti-carbohydrate antibodies for their bioanalytical applications. Better understanding of a precise mechanism involved in glycan recognition will most likely launch a wide range of new biorecognition molecules like DNA/RNA and peptide aptamers ([Davis et al., 2007] and [Stoltenburg et al., 2007]).

The powerful combination of lectin microarrays with mass spectrometry will presumably become one of the driving forces for development in glycomics. Lectins have been successfully used in affinity pre-treatment of low abundance glycoproteins with subsequent use of mass spectrometry for identification of glycoproteins ([Drake et al., 2006] and [Sparbier et al., 2007]). Such a strategy can be used to identify new disease biomarkers, a key challenge in biomedical/clinical diagnosis in order to increase the survival rate of patients. Despite decades of intensive effort, only a few single biomarkers (not, vert, similar 20) were clinically approved (Sahab et al., 2007). This is because thousands of samples are required to validate the true potential of a biomarker or panel of biomarkers (Zhang et al., 2007). Tissue microarray can potentially be used to detect biomarkers directly in a tissue sample, thus overcoming current histological examination in a high-throughput fashion ([Kozarova et al., 2006] and [Zhang et al., 2007]).

Once a biomarker is validated and approved, an array of more specific anti-carbohydrate antibodies can be used for high-throughput screening of clinical samples. The discovery of specific and universal biomarkers can be even more challenging than previously considered, because disease-related biomarkers seem to be fragments of larger biomolecules (Petricoin et al., 2006) with fragmentation patterns that vary from patient to patient.
Notes added in proof

In the two recent papers, Hirabayashi and co-workers used lectin microarrays to confirm the presence of the glycan structure after two mucin-type human glycoproteins were genetically engineered and expressed in Saccharomyces cerevisiae (Amano et al., 2008) and to find a lectin (Wisteria floribunda agglutinin) able to clearly distinguish cancerous from normal epithelia, which was successfully used within the histochemical investigation (Matsuda et al., 2008). A study describing preparation of recombinant lectin microarray based on non-glycosylated lectins of bacterial origin with suppressed false positive results was published, as well (Hsu et al., 2008).
Acknowledgements

The financial support by grants VEGA 2/7028/07, 2/7033/07 and 2/7053/07 are gratefully acknowledged.
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