Glycobiology, 2002, Vol. 12, No. 11 713-719
© 2002 Oxford University Press
A novel computational approach to integrate NMR spectroscopy and capillary electrophoresis for structure assignment of heparin and heparan sulfate oligosaccharides
2 Institute for Chemical and Biochemical Research "G. Ronzoni," via G. Colombo 81, 20133 Milan, Italy, and 3 Division of Bioengineering and Environmental Health, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Received on December 14, 2001; revised on July 4, 2002; accepted on July 6, 2002
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Abstract |
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Heparin and heparan sulfate (HS) glycosaminoglycans (GAGs) are cell surface polysaccharides that bind to a multitude of signaling molecules, enzymes, and pathogens and modulate critical biological processes ranging from cell growth and development to anticoagulation and viral invasion. Heparin has been widely used as an anticoagulant in a variety of clinical applications for several decades. The heterogeneity and complexity of HS GAGs pose significant challenges to their purification and characterization of structure–function relationships. Nuclear magnetic resonance (NMR) spectroscopy is a promising tool that provides abundant sequence and structure information for characterization of HS GAGs. However, complex NMR spectra and low sensitivity often make analysis of HS GAGs a daunting task. We report the development of a novel methodology that incorporates distinct linkage information between adjacent monosaccharides obtained from NMR and capillary electrophoresis (CE) data using a property encoded nomenclature (PEN) computational framework to facilitate a rapid and unbiased procedure for sequencing HS GAG oligosaccharides. We demonstrate that the integration of NMR and CE data sets with the help of the PEN framework dramatically reduces the number of experimental constraints required to arrive at an HS GAG oligosaccharide sequence.
Key words: capillary electrophoresis/heparin oligosaccharides/nuclear magnetic resonance/property encoded nomenclature
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Introduction |
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Heparin and heparan sulfate (HS) glycosaminoglycans (GAGs) are complex acidic polysaccharides that are involved in a variety of physiological and pathological conditions. Advances in different areas of biology have elucidated the potential roles of HS GAGs in key biological processes (Casu and Lindahl, 2001
; Lindahl, 2000; Sasisekharan and Venkataraman, 2000; Shriver et al., 2002) including thrombosis (Petitou et al., 1999), angiogenesis (Sasisekharan et al., 1997), viral invasion (Chen et al., 1997; Fry et al., 1999; Shukla et al., 1999), and tumor growth (Hulett et al., 1999; Vlodavsky et al., 1999; Liu et al., 2002). The repeat unit of an HS GAG polymer is a disaccharide comprising of a uronic acid (U), which can exist in two different epimeric forms—
-L-iduronic (I) or ß-D-glucuronic (G), linked 1
4 to a -D-glucosamine residue (A). There are variations within the disaccharide unit in the form of sulfation at the 2-O position of the uronic acid, 3-O and 6-O position of the glucosamine and sulfation, or acetylation of the N-position of the glucosamine (Casu and Lindahl, 2001).
Perhaps the best studied structure–activity relationship in HS GAGs is a pentasaccharide sequence in heparin that specifically binds to and activates antithrombin-III, thereby playing an inhibitory role in the blood coagulation cascade (Bourin and Lindahl, 1993). Heparin and its derivatives low-molecular-weight heparins (LMWHs) are the most widely used clinical agents for prevention of deep vein thrombosis after surgery (Breddin, 2000) and myocardial infarction after coronary invasion procedures (Cohen, 1999). Based on the anticoagulant properties of heparin, new therapeutic applications are being envisaged (Rosenberg, 2001). A synthetic version of the pentasaccharide has been used as an antithrombotic drug (Turpie et al., 2001).
Toward understanding the structure–activity relationship of HS GAGs, several analytical tools have been successfully developed for sequencing oligosaccharides, including gel electrophoresis (Turnbull et al., 1999), capillary electrophoresis (Linhardt and Toida, 1999), high-performance liquid chromatography (Vives et al., 1999), matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) (Venkataraman et al., 1999), and nanoelectrospray mass spectrometry (Pope et al., 2001). These analytical tools have been applied to dissect the HS GAG oligosaccharide into smaller fragments using a battery of depolymerizing enzymes and other chemical methods and determining the sequence of the oligosaccharides based on specific properties of the smaller fragments (Kreuger et al., 2001).
Nuclear magnetic resonance (NMR) spectroscopy is a promising analytical tool for addressing the complexity and heterogeneity of HS GAG oligosaccharides. Characteristic proton and 13C chemical shifts have been identified for commonly occurring monosaccharides and heparin, and the relative abundance of these monosaccharides can be quantitatively determined by integrating the proton signals. In addition to characterization of the monosaccharides, the anomeric proton signals of the glucosamines can be resolved further to give linkage to the neighboring uronic acid (A–U linkage) with a defined epimeric and sulfation state (Mulloy and Johnson, 1987; Yates et al., 1996). The strength of NMR analysis is that simple 1D proton NMR and 2D correlation spectroscopy (COSY)/total correlation spectroscopy (TOCSY) NMR spectra provide quantitative information on multiple parameters, including monosaccharide composition, sulfation states, and A–U linkage information that define the sequence of a HS GAG oligosaccharide. Furthermore, NMR is the most accurate method for direct quantification of the iduronic and glucuronic acid content in a sequence. As such, these developments have led to the use of NMR as a common tool for sequence assignment of pure heparin-derived tetra-decasaccharides (Yamada et al., 1993, 1999; Chuang et al., 2001) as well as for obtaining monosaccharide composition fingerprints of heterogeneous unfractionated heparins from pig and beef mucosa (Casu et al., 1996) and of LMWHs (Desai and Linhardt, 1994; Casu and Torri, 1999). In addition, NMR spectroscopy has also been used to probe the 3D structure and the conformational dynamics of heparin-derived oligosaccharides on protein binding in solution (Hricovini et al., 1999, 2001).
Despite these clear advantages, interpretation of NMR spectra of HS GAGs has certain limitations due to overlaps in proton signals and absence of measurable coupling constants. Also, the sensitivity of this technique is lower than those based on detection of chromatographic effluents (Turnbull et al., 1999; Vives et al., 1999; Kreuger et al., 2001) and on mass spectrometry (Pope et al., 2001; Venkataraman et al., 1999), a serious limitation for characterization of samples that are only available in small quantities. Although good sensitivity can be obtained for 1D proton and 2D COSY/TOCSY spectra for small sample amounts typical of HS GAGs, nuclear Overhauser effect spectroscopy (NOESY) and rotating frame Overhauser effect spectroscopy (ROESY) spectra usually need more sample and high field instruments (up to 400 MHz) for reasonable sensitivity and signals resolution.
In this study we developed a methodology that utilizes the strengths of NMR by combining the A–U linkage information obtained from NMR analysis and the U–A linkage information obtained from a single capillary electrophoresis (CE) experiment to rapidly arrive at the sequence of HS GAG oligosaccharides. Using a numerical property encoding nomenclature (PEN) framework, we efficiently incorporated these two distinct linkage data sets to construct the sequence in a systematic and unbiased fashion. We developed the PEN numerical framework to successfully sequence HS GAG oligosaccharides by applying MALDI-MS and CE data as constraints (Venkataraman et al., 1999). Herein we extend this framework to incorporate NMR data and demonstrate the effectiveness of our technique in improving the application of NMR analysis of HS GAGs and in reducing the experimental constraints required to sequence HS GAG oligosaccharides.
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Results |
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Two synthetic pentasaccharides and a heparin-derived decasaccharide are used as examples to illustrate our methodology. Although the first two synthetic pentasaccharide examples clearly outline the methodology of our approach, they are not the best examples to demonstrate the utility of the computational framework. The importance of the PEN framework is better illustrated by the longer and more complex heparin derived oligosaccharide (H10) discussed later. This sequence is presently among the most complex heparin-derived oligosaccharide sequenced to date. It is important to point out that much effort has gone into the isolation and sequencing of H10 (Toida et al., 1996
). Due to its complexity there were inaccuracies in its structure determination in the past, and only recently using a combination of analytical tools was this sequence was established (Venkataraman et al., 1999; Shriver et al., 2000a,b). NMR spectroscopy has been used in the past to corroborate its sequence (Shriver et al., 2000a), but only the monosaccharide composition was established and there was bias in the interpretation of the NMR data based on the determined sequence. Using these examples, we highlight the flexibility of our approach in providing an unbiased assignment of complex heparin-derived oligosaccharide structures. All the notations used in the PEN framework are described in Figure 1.
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Example 1: pentasaccharide P1
Several characteristic chemical shifts of the monosaccharide anomeric protons were observed in the 1D proton NMR spectrum of the pentasaccharide (Figure 2A). The 1D proton signals along with the 2D COSY and TOCSY spectra were used to assign the monosaccharides. The signal patterns at 5.648, 5.438, and 5.041 ppm were assigned to the anomeric protons of N-sulfated glucosamines (ANS,6X). Furthermore, the 6-O sulfation of all these glucosamines was confirmed by TOCSY data (not shown). The signal at 3.43/57.5 ppm indicates the presence of an O-methyl group linked to the reducing terminal. In addition, the presence of the methyl group at the reducing end also accounts for the absence of the typical reducing end carbon chemical shift (92–93 ppm). The chemical shifts at 5.251 and 4.635 are in agreement with an I2S and G, respectively. The anomeric proton signals at 5.648 and 5.438 are distinguished further as arising from ANS,6S–I2S and ANS,6S–G, respectively.
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Integration of these peaks (Guerrini et al., 2001
) gave the relative molar abundance of the glucosamines as ANS,6S–I2S:ANS,6S–G:ANS,6S = 1:1:1 and that of the uronic acids as I2S:G = 1:1. Thus from the 1D and 2D NMR data the identity Ai = [58; 58; 58 (ANS,6S)], Ui = [14 (I2S); 24 (G)] and relative abundance of the monosaccharides constituting the sequence were determined. Furthermore the linkages Di' = [–516 (ANS,6S–I2S); –616 (ANS,6S–G)] were also obtained form NMR data. Based on this information, there can be two possible (LNMR) pentasaccharide sequences: 58 14 58 24 58 (ANS,6S–I2S–ANS,6S–G–ANS,6S,OMe) and 58 24 58 14 58 (ANS,6S–G–ANS,6S–I2S–ANS,6S,OMe).
CE of the fragments formed by complete digestion of the pentasaccharide with the heparinases resulted two peaks (data not shown) corresponding to a trisulfated 
U2SANS,6S and a disulfated disaccharide UANS,6S thus defining ± Di = [±D16; ±516]. The relative molar abundance of these two disaccharides was calculated as 1:1 by integration of the CE signals and normalizing the peak areas using an internal calibration. The migration time of the ±D16 disaccharide was slightly different from the standard indicating that the methylated glucosamine is a part of the trisulfated ±D16 disaccharide. Thus the data from CE fixes the sulfation state of methylated reducing end disaccharide. Incorporating the constraints from CE data eliminated one of the sequences from LNMR, thus converging on 58 24 58 14 58 (ANS,6S–G–ANS,6S–I2S–ANS,6S,OMe).
Example 2: pentasaccharide P2
From the 1D proton spectrum (Figure 2B) the signal pattern (anomeric peaks at 5.64/99.6 ppm, 5.50/98.1 ppm) is consistent with N-sulfated, 6-O-sulfated glucosamines (ANS,6S), and A* bearing an extra 3-O sulfate group. Similar to example 1, the signal pattern at 3.43/57.5 ppm corresponds to a glucosamine with a methylated reducing end. Also the anomeric chemical shift at 5.64 ppm arises from a ANS,6S linked to G as shown in example 1. Signals at 5.20/101.7 ppm and 4.78/72.4 ppm agree with H1 and H5 of I2S residue, and the anomeric signal at 4.6/103.3 ppm agrees with G (Mulloy and Johnson, 1987; Yates et al., 1996). Thus Ui = [14 (I2S); 24 (G)] and Ai = [58; 58 (ANS,6S); 78 (A*)]. In this case we have only one of the two elements of Di' defined = [–616 (ANS,6S–G)]. Incorporating the inferences from NMR data as constraints we get LNMR = two sequences: 58 24 78 14 58 (ANS,6S–G–A*–I2S–ANS,6S,OMe) and 78 14 58 24 58 (A*–I2S–ANS,6S–G–ANS,6S,OMe)
The disaccharide composition analysis using CE resulted in a single peak corresponding to a trisulfated disaccharide with a shifted migration time, indicating the presence of the methylated glucosamine (±Di = [±D16]). Using the data from CE one of the sequences from LNMR was eliminated to give the right sequence 58 24 78 14 58 (ANS,6S–G–A*–I2S–ANS,6S,OMe). This sequence is consistent with the notion that the ANS,6S–G linkage is resistant to cleavage by heparinase I, II, and III due to the presence of the 3-O sulfated A* (Shriver et al., 2000b) thus resulting only in a single disaccharide observed using CE.
Example 3: decasaccharide (H10)
The signal line broadening of the proton spectrum of H10 (Figure 3A) is caused by the complexation of paramagnetic ions with the negatively charged groups. Addition of ethylenediamine tetra-acetic acid (EDTA) provides a better resolved spectrum (Figure 3B) by removal of these paramagnetic ions. (Neville et al., 1989)
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The assignment of the anomeric signals (Figure 3C) and their respective proton patterns (Table I), were carried out by COSY and TOCSY experiments.
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The signals detected between 4.2 and 4.4 ppm are in agreement with H-6 proton from 6-O sulfated glucosamine. However, because this chemical shift lies in the crowded area of the spectrum and due to the presence of minor impurities in the sample, it was not possible to accurately determine the molar abundance of glucosamines containing the 6-O sulfate groups. However, disaccharide compositional analysis of H10 using CE indicated the presence of three major disaccharide components—
U2S–ANS, 6S, U–ANAc, 6S, and U–A* in the ratio 3:1:1 giving ±Di = [±D; ±4; ±7]. Thus the data from CE fixed the 6-O sulfation of all the glucosamines.
The relative abundance of the glucosamine monosaccharides calculated by signal integration were 58 (ANS,6S): 78 (A*): 48 (ANAc,6S) = 3:1:1, thus Ai = [58; 58; 58; 78; 48]. The two anomeric signals at 5.20 and 5.019 ppm arise from 2-O sulfated and nonsulfated iduronic acid, respectively, as demonstrated by the chemical shift pattern. The only ß proton signal of the spectrum (at 4.669 ppm) belongs to a glucuronic acid residue. Protons at 6 ppm and 5.521 ppm belong to the H4 and H1 of the U residue. The H2 at 4.635 ppm indicates that the unsaturated uronic acid residue is 2-O-sulfated. The relative abundance of the uronic acid monosaccharides was calculated as I2S:I:G:U2S were identified in the ratio 2:1:1:1, thereby defining Ui = [14; 14; 04; 24; (*1)4] (where the asterisk stands for U because this bit is not defined).
The chemical shift of the signal at 5.369 ppm agrees with a ANS,6S linked to I. The 1H anomeric chemical shift of a ANAc, 6S is distinct for ANAc,6S–I (5.14–5.18 ppm) and ANAc,6S–G (5.30–5.36 ppm) linkages (Cohen, 1999; Chuang et al., 2001). The anomeric proton of ANAc,6S at 5.386 ppm confirms the presence of ANAc,6S–G linkage in the sequence. The chemical shift at 5.42 agrees with both ANS,6S linked to I2S and ANS,6S at the reducing end. Because the U residue is linked to an ANS,6S unit and a second ANS,6S is linked to I, two possibilities are left for the reducing end, one with ANS,6S and the other with A*. However, the chemical shift pattern associated with A* (H1, 5.464 ppm; H2, 3.480 ppm; H3, 4.564 ppm, H4, 4.041 ppm) is the same as found by Yamada et al. (1993) for a heparin tetrasaccharide with this residue at the reducing end (chemical shifts in Yamada et al. (1993) are systematically shifted about –0.03 ppm with respect to our values). Thus the signal pattern of A* is consistent with its location at the reducing end. Based on the relative abundance of these signals all the elements of Di' were defined as [–516; –516; –416; –216]. Translating the Ai, Ui, and Di' to constraints using the PEN framework LNMR = 12 sequences were obtained (Figure 4).
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Eliminating the sequences from LNMR that do not contain disaccharide linkages corresponding to ±Di resulted in a single sequence ±DDD4–7, which is consistent with the H10 sequence obtained earlier (Shriver et al., 2000a
). This sequence was also independently confirmed by obtaining the complete 2D NOESY spectrum and assignment of all the linkages based on NOESY (for the first time). The 2D NOESY spectrum was possible only on a 600 MHz instrument using cryoprobe (Figure 5).
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Discussion |
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TopAbstractIntroductionResultsDiscussionMaterials and methodsAcknowledgmentsAbbreviationsReferences |
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HS GAGs play important regulatory roles in critical biological processes. Owing to the limitations in purification and characterization, it has been a challenging task to determine their structure–function relationships. Several analytical techniques (including gel electrophoresis, chromatography, and mass spectrometry) have been successfully applied to sequence small amounts of HS GAG oligosaccharides. The prerequisite for the success of most of these techniques is predictable and controlled depolymerization of HS GAG oligosaccharide into smaller fragments using a combination of enzymatic and chemical degradation methods. Importantly, for some of the sequencing strategies, the use of multiple exo-enzymes are required to accurately determine the different modifications of the disaccharide units.
Though NMR spectroscopy has a lower sensitivity compared to the other analytical tools mentioned, it is a powerful tool to determine quantitatively numerous parameters that define the sequence of an intact HS GAG oligosaccharide including monosaccharide composition, sulfation pattern, and linkage between glucosamine and uronic acid (A–U). These critical parameters for sequence determination can be readily determined independent of sequence length and variability of building blocks, using a single series of simple 1D proton and 2D COSY/TOCSY experiments. It is important to point out that NMR is currently the most appropriate method to distinguish quantitatively the iduronic and glucuronic acids in a given sample. Advances in high field instrumentation are improving the sensitivity of NMR analysis, which is particularly relevant for analysis of tissue derived HS GAG oligosaccharides. Despite these developments, the complete assignment of complex HS GAG oligosaccharides is still a daunting task owing to fuzziness in interpreting the spectra of NOESY/ROESY experiments.
The rationale behind our strategy is to utilize the quantitative measure of A–U linkages obtained from simple NMR experiments and an orthogonal set of U–A linkage information obtained from CE to construct the sequence of an HS GAG oligosaccharide. We used a numerical PEN framework to seamlessly integrate the diverse information obtained from NMR and CE data that include monosaccharide composition, sulfation states, and two different types of linkages. The numerical nature of the PEN framework facilitates moving between monosaccharide, disaccharide U–A and A–U linkage information using simple mathematical operations, thus facilitating a systematic and unbiased way of rapidly arriving at an HS GAG sequence. By applying the U–A linkage information from CE, our strategy drastically reduces the need for NOESY/ROESY experiments, which require a lot more sample and more sophisticated instruments for reasonable sensitivity.
The three examples discussed in our study provide a clear picture of the application of our methodology and the novelty of our computational approach. The pentasaccharides in examples 1 and 2 contain both I2S and G, thereby having sufficient variability. These examples demonstrated the importance of determining the signature of the reducing end (methylation) for successful application of our methodology. Having determined all the A–U and U–A linkages, the knowledge of the reducing or nonreducing end enables the determination of the sequence by moving forward or backward. Such signatures are easy to obtain by chemical degradation or end labeling of the sample. Decasaccharide H10 is one of the most complex decasaccharides that has been characterized and verified using a combination of analytical tools. This example was chosen to highlight the strengths of our technique in comparison with other analytical tools that have been used for its characterization in the past.
Our earlier sequencing approaches for this decasaccharide required numerous steps. Using our methodology we arrived at the sequence of H10 in an unbiased fashion from two distinct A–U and U–A linkage information that were quantitatively determined using a minimal set of experimental data. This example illustrates the flexibility of the computational method to construct a list of all possible combinations of sequences satisfying the linkage and monosaccharide composition obtained from NMR data and elimination of sequences that did not satisfy the CE data.
In summary, we developed another important analytical tool for the characterization of HS GAGs that is critical for elucidating their structure–function relationships in key biological processes. We are pursuing the potential extension of this methodology to characterize oligosaccharide sequences in a heterogeneous mixture typically found in commercially available LMWHs.
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Materials and methods |
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The synthetic pentasaccharides P1 and P2 corresponding to the active sequence of heparin for AT-III binding was gift from M. Petitou, Sanofi-Synthelabo, Toulouse, France. The decasaccharide H10, kindly provided by R.J. Linhardt (University of Iowa), was obtained by fractionation of heparinase digest of pig mucosal heparin on an AT-III column as described elsewhere (Toida et al., 1996
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NMR spectroscopy
The oligosaccharide samples were prepared by dissolving 2 mg of the pentasaccharide and 150 µg of H10 in 0.5 ml D2O 99.99%. Due to signal broadening caused by paramagnetic ions in H10, deuterated EDTA was added to the sample to remove these ions (Neville et al., 1989). The 1H-NMR spectra were recorded at 500 MHz on a Bruker AMX 500 spectrometer at 60°C with presaturation of the residual water signals and with recycle delay of 12 s; a 45° pulse was used. 2D DQF-COSY and TOCSY were measured in phase-sensitive mode using TPPI, and a shifted square sine-bell function was applied before Fourier transformation. Thirty-two and 512 scans for each FID were used for the pentasaccharide and the decasaccharide, respectively. For the 2D NOESY experiment on the pentasaccharide sample, 32 transients were collected for each free-induction decay (Kay et al., 1992). 2D NOESY spectrum of the H10 sample was recorded at 600 MHz on a Bruker Avance spectrometer equipped with cryoprobe at 50°C. Three hundred twenty scans for each of 320 free-induction decay were collected. The mixing time of NOESY experiments of both pentasaccharide and H10 samples was 300 ms. NOESY spectrum (matrix 1024 x 512 points) was zero-filled to 2K x 2K before Fourier transformation
Compositional analysis using CE
Compositional analysis of the oligosaccharides was completed by exhaustive enzymatic digest of a 30 µM sample followed by CE as described elsewhere (Venkataraman et al., 1999). Briefly, to 1 nmol oligosaccharide was added 200 nM heparinases I, II, and III in 25 mM sodium acetate, 100 mM NaCl, and 5 mM calcium acetate buffer, pH 7.0. The reaction was allowed to proceed at 30°C overnight and then analyzed by CE in reverse polarity with a running buffer of 50 mM Tris/phosphate/10 µM dextran sulfate, pH 2.5.
Incorporating CE and NMR data as constraints using PEN framework
The PEN is a numerical notation scheme that encodes the sulfation pattern of a disaccharide building block as a series of binary on/off states and epimerization of the uronic acid as a + or – sign bit leading to a signed hexadecimal coding scheme (Venkataraman et al., 1999). Although, the PEN framework was originally developed to encode a U–A disaccharide building block (Di), it was mathematically decomposed into a base-4 code for representing the uronic acid monosaccharide (U) and a base-8 code for representing the glucosamine monosaccharide (A) (Figure 1). Furthermore, the PEN framework was also used to encode an A–U disaccharide unit (Di') by transposing the three bits that encode for the sulfation state of the glucosamine with the two bits that encode for the epimeric and sulfation state of the uronic acid.
1D proton NMR spectrum along with the 2D COSY, heteronuclear single quantum coherence, and TOCSY spectra provide data on the chemical shifts and coupling constants of most the ring protons of the constituent monosaccharides. This data was used to uniquely identify the monosaccharides (Ui and Ai) and obtain the number of monosaccharides for a given length of the sequence. In addition to the identity of the glucosamine monosaccharides, their characteristic anomeric chemical shifts were further resolved to identify their linkages to adjacent uronic acids (Ai–Ui linkages defining Di' disaccharides). The Ui, Ai, and Di' information was used to build a list of all the possible sequences satisfying this data (LNMR). The sequences in LNMR represent a comprehensive sample space without any bias toward commonly occurring sequences.
Disaccharide compositional analysis using CE provides accurate information on the sulfation pattern of a U–A disaccharide, thus identifying all the U–A linkages (±Di) whose sign bit is not known due to the 4–5 unsaturated bond. Incorporating the disaccharide linkages ±Di obtained from CE data eliminates most of the sequences from LNMR converging in a single sequence.
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Acknowledgments |
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We acknowledge Detlef Moskau of Bruker AG (Faellanden, Switzerland) for recording the NOESY spectrum of H10 and Mallik Sundaram for providing technical help with the CE analysis. This investigation was funded in part by the Arnold and Mabel Beckman Foundation (to R.S.), the National Institutes of Health (Grant GM57073, CA090940 to R.S.), and the Merck/MIT Fellowship (Massachusetts Institute of Technology, to R.R).
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Abbreviations |
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TopAbstractIntroductionResultsDiscussionMaterials and methodsAcknowledgmentsAbbreviationsReferences |
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A,
-D-glucosamine; CE, capillary electrophoresis; COSY, correlation spectroscopy; EDTA, ethylenediamine tetra-acetic acid; G, ß-D-glucuronic acid; GAG, glycosaminoglycan; HS, heparan sulfate; I, -L-iduronic acid; LMWH, low-molecular-weight heparin; MALDI-MS, matrix-assisted laser desorption/ionization mass spectrometry; NMR, nuclear magnetic resonance; NOESY, nuclear Overhauser effect spectroscopy; PEN, property encoded nomenclature; ROESY, rotating frame Overhauser effect spectroscopy; TOCSY, total correlation spectroscopy; U, uronic acid (iduronic or glucuronic).
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Footnotes |
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1 To whom correspondence should be addressed; E-mail: guerrini.m@ronzoni.it
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References |
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