Identification Of Glycan Structures Research Articles

GIPS-Mix for Accurate Identification of Isomeric Components in Glycan Mixtures Using Intelligent Group-Opting Strategy.

Accurate identification of glycan structures is highly desirable as they are intimately linked to their different functions. However, glycan samples generally exist as mixtures with multiple isomeric structures, making assignment of individual glycan components very challenging, even with the aid of multistage mass spectrometry (MSn). Here, we present an approach, GIPS-mix, for assignment of isomeric glycans within a mixture using an intelligent group-opting strategy. Our approach enumerates all possible combinations (groupings) of candidate glycans and opts in the best-matched glycan group(s) based on the similarity between the simulated spectra of each glycan group and the acquired experimental spectra of the mixture. In the case that a single group could not be elected, a tie break is performed by additional MSn scanning using intelligently selected precursors. With 11 standard mixtures and 6 human milk oligosaccharide fractions, we demonstrate the application of GIPS-mix in assignment of individual glycans in mixtures with high accuracy and efficiency.

GlycoDeNovo2: An Improved MS/MS-Based De Novo Glycan Topology Reconstruction Algorithm.

Glycan structure identification is essential to understanding the roles of glycans in various biological processes. Previously, we developed GlycoDeNovo, a de novo algorithm for reconstructing glycan topologies from tandem mass spectra (MS/MS). In this work, we introduce GlycoDeNovo2 that contains two major improvements to GlycoDeNovo. First, we use the precursor mass measured for a peak that likely corresponds to a glycan to determine its potential compositions, which are used to constrain the search space, enable parallel computation, and hence speed up topology reconstruction. Second, we developed a procedure to calculate the empirical p-value of a reconstructed topology candidate. Experimental results are provided to demonstrate the effectiveness of GlycoDeNovo2.

Dopant-Enriched Nitrogen Gas for Enhanced Electrospray Ionization of Released Glycans in Negative Ion Mode.

The desolvation and ionization process of analytes can significantly be improved by enriching the nebulizing gas with a dopant (dopant enriched nitrogen (DEN) gas) in the electrospray source. However, for the analysis of released glycans in negative ion mode, the usage of DEN gas remains largely unexplored. For this purpose, we investigated the effect of different polar protic solvents (methanol, ethanol, and isopropanol) as well as using solely the nebulizing gas or ambient air on the ionization and charge state distribution of released N- and O-glycans. Compared to the standard acetonitrile enriched nitrogen gas, isopropanol showed the highest increase in regards to peak areas. Moreover, it showed large benefits for the identification of glycan structures at high sensitivity as the increased precursor intensities subsequently resulted in higher intensities in tandem MS mode. While similar effects are noted for both neutral and sialylated species, the most significant effect was observed for early eluting glycans where very low acetonitrile concentrations were present in the eluent. The best results in terms of S/N ratios were obtained with methanol, with less effect on the MS/MS signal enhancement. Therefore, the use of this dopant would be particularly beneficial for high sensitivity MS-mode applications. In conclusion, isopropanol enriched DEN gas greatly improves the detection of both N-and O-glycan species and their tandem mass spectra, particularly for the early eluting species whose ionization in the absence of DEN gas is hindered by low organic concentrations.

Towards a more complete glycome: Advances in ion chromatography-mass spectrometry (IC-MS) for improved separation and analysis of carbohydrates

To date, few tools are available for the analysis of the glycome without derivatization, a process which is known to introduce issues such as differential loss of sialic acid and incomplete labeling. We have previously reported the use of ion chromatography-mass spectrometry (IC-MS) to analyze native sialylated and sulfated glycans. Here, we introduce improvements to IC column technology, enabling the separation of neutral glycans while maintaining charge separation capabilities. When implemented in an IC-MS workflow, this enables the structural characterization of a broad array of chemically distinct glycans. With the newly developed IC column and modified IC-MS instrumentation configuration, we qualitatively investigated O-glycome profiles in bovine fetuin and porcine gastric mucins. The improved chromatographic resolution in combination with high-resolution MS data present a powerful tool for glycan structural identification.

N-glycan structures of target cancer biomarker characterized by two-dimensional gel electrophoresis and mass spectrometry

Glycoproteins are important biomarkers for cancers, while most glycoproteomics biomarkers suffering from low sensitivity and specificity due to their uncharacterized glycan structures. AZGP1 is a potential biomarker for salivary diagnostics of lung cancer, which is used as a model glycoprotein in this study for method development. We initially analyzed salivary N-glycoproteome by using lectin affinity chromatography and more than 300 N-glycoproteins were identified, including AZGP1. 7 gel spots of AZGP1 were resolved by two-dimensional gel electrophoresis and further confirmed by two-dimensional western blot as well as mass spectrometry. The isomeric glycan structures of AZGP1 in these spots were systematically characterized both at composition level and at structure level. Our results revealed 10 glycan compositions for salivary AZGP1, including core fucosylated glycans on Asn128 and sialylated glycans on Asn109 and Asn112. We further compared the glycan structures of salivary AZGP1 from lung cancer group and control group. Accordingly, 14 and 7 potential glycan structures were successfully revealed, respectively. In total, 15 glycan compositions and 22 potential glycan structures were identified and characterized for AZGP1, including some different structures with the same compositions. In particular, 5 potential glycan structures were identified as lung cancer unique signatures. Our developed strategy holds promise for thorough identification of glycan structures on a target glycoprotein biomarker. In-depth characterization of its glycan structures will ultimately enhance its sensitivity and specificity for cancer detection.

Identification of glycan branching patterns using multistage mass spectrometry with spectra tree analysis

Glycans are crucial to a wide range of biological processes, and their biological activities are closely related to the branching patterns of structures. Different from the simple linear chains of proteins, branching patterns of glycans are more complicated, making their identification extremely challenging. Tandem mass spectrometry (MS2) cannot provide sufficient structural information to deduce glycan branching patterns even with the assistance of various bioinformatic tools and algorithms.The promising technology to identify glycan branching patterns is multi-stage mass spectrometry (MSn). The production-relationship among MSn spectra of a glycan is essentially a tree, making deducing glycan structures from MSn spectra a great challenge. In the present study, we report an approach called glyBranch (glycan Branching pattern identification based on spectra tree) to fully exploit the information contained in the MSn spectra tree for glycan identification. Using 14 glycan standards, including 2 pairs with isomeric sequence, and 16 complex N-glycans isolated from RNase B and IgG, we demonstrated the successful application of glyBranch to branching pattern analysis.The source code of glyBranch is available at https://github.com/bigict/glyBranch/. We have also developed a web-server, which is freely accessible at http://glycan.ict.ac.cn/glyBranch/. SignificanceGlycans are crucial in various biological processes and their functions are closely related to the details of their structures; thus, the identification of glycan branching patterns is of great significance to biological studies. Multistage mass spectrometry (MSn) can provide detailed structural information by generating multiple-level fragments through consecutive fragmentation; however, the interpretation of numerous MSn spectra is extremely challenging. In this study, we present an approach called glyBranch (glycan Branching pattern identification based on spectra tree) to exploit the information contained in MSn spectra tree for glycan identification. This approach will greatly facilitate the automated identification of glycan structures and related biological studies.

Omics-based Identification of Glycan Structures as Biomarkers for a Variety of Diseases.

Glycans play important roles in cell communication, protein interaction, and immunity, and structural changes in glycans are associated with the regulation of a range of biological pathways involved in disease. However, our understanding of the detailed relationships between specific diseases and glycans is very limited. In this study, we proposed an omics-based method to investigate the correlations between glycans and a wide range of human diseases. We analyzed the gene expression patterns of glycogenes (glycosyltransferases and glycosidases) for 79 different diseases. A biological pathway-based glycogene signature was constructed to identify the alteration in glycan biosynthesis and the associated glycan structures for each disease state. The degradation of N-glycan and keratan sulfate, for example, may promote the growth or metastasis of multiple types of cancer, including endometrial, gastric, and nasopharyngeal. Our results also revealed that commonalities between diseases can be interpreted using glycogene expression patterns, as well as the associated glycan structure patterns at the level of the affected pathway. The proposed method is expected to be useful for understanding the relationships between glycans, glycogenes, and disease and identifying disease-specific glycan biomarkers.

Automatic Full Glycan Structural Determination through Logically Derived Sequence Tandem Mass Spectrometry.

Glycans have diverse functions and play vital roles in many biological systems, such as influenza, vaccines, and cancer biomarkers. However, full structural identification of glycans remains challenging. The glycan structure was conventionally determined by chemical methods or NMR spectroscopy, which require a large amount of sample and are not readily applicable for glycans extracted from biological samples. Although it has high sensitivity and is widely used for structural determination of molecules, current mass spectrometry can only reveal parts of the glycan structure. Herein, the full structures of glycans, including diastereomers, the anomericity of each monosaccharide, and the linkage position of each glycosidic bond, which can be determined by using tandem mass spectrometry guided by a logically derived sequence (LODES), are shown. This new method provides de novo oligosaccharide structural identification with high sensitivity and has been applied to automatic in situ structural determination of oligosaccharides eluted by means of HPLC. It is shown that the structure of a given trisaccharide from a trisaccharide mixture and bovine milk were determined from nearly 3000 isomers by using 6-7 logically selected collision-induced dissociation spectra. The entire procedure for mass spectrometry measurement guided by LODES can be programmed in a computer for automatic full glycan structure identification.

De novo glycan structural identification from mass spectra using tree merging strategy

MotivationGlycans are large molecules with specific tree structures. Glycans play important roles in a great variety of biological processes. These roles are primarily determined by the fine details of their structures, making glycan structural identification highly desirable. Mass spectrometry (MS) has become the major technology for elucidation of glycan structures. Most de novo approaches to glycan structural identification from mass spectra fall into three categories: enumerating followed by filtering approaches, heuristic and dynamic programming-based approaches. The former suffers from its low efficiency while the latter two suffer from the possibility of missing the actual glycan structures. Thus, how to reliably and efficiently identify glycan structures from mass spectra still remains challenging. ResultsIn this study we propose an efficient and reliable approach to glycan structure identification using tree merging strategy. Briefly, for each MS peak, our approach first calculated monosaccharide composition of its corresponding fragment ion, and then built a constraint that forces these monosaccharides to be directly connected in the underlying glycan tree structure. According to these connecting constraints, we next merged constituting monosaccharides of the glycan into a complete structure step by step. During this process, the intermediate structures were represented as subtrees, which were merged iteratively until a complete tree structure was generated. Finally the generated complete structures were ranked according to their compatibility to the input mass spectra. Unlike the traditional enumerating followed by filtering strategy, our approach performed deisomorphism to remove isomorphic subtrees, and ruled out invalid structures that violates the connection constraints at each tree merging step, thus significantly increasing efficiency. In addition, all complete structures satisfying the connection constraints were enumerated without any missing structure. Over a test set of 10 N-glycan standards, our approach accomplished structural identification in minutes and gave the manually-validated structure first three highest score. We further successfully applied our approach to profiling and subsequent structure assignment of glycans released from glycoprotein mAb, which was in perfect agreement with previous studies and CE analysis.

Toward Automated Identification of Glycan Branching Patterns Using Multistage Mass Spectrometry with Intelligent Precursor Selection.

Glycans play important roles in a variety of biological processes. Their activities are closely related to the fine details of their structures. Unlike the simple linear chains of proteins, branching is a unique feature of glycan structures, making their identification extremely challenging. Multistage mass spectrometry (MS n) has become the primary method for glycan structural identification. The major difficulty for MS n is the selection of fragment ions as precursors for the next stage of scanning. Widely used strategies are either manual selection by experienced experts, which requires considerable expertise and time, or simply selecting the most intense peaks by which the product-ion spectrum generated may not be structurally informative and therefore fail to make the assignment. We here report a glycan "intelligent precursor selection" strategy (GIPS) to guide MS n experiments. Our approach consists of two key elements, an empirical model to calculate candidate glycan's probability and a statistical model to calculate fragment ion's distinguishing power in order to select the structurally most informative peak as the precursor for next-stage scanning. Using 15 glycan standards, including three pairs with isomeric sequences and eight variously fucosylated oligosaccharides on linear or branched hexasaccharide backbones isolated from a human milk oligosaccharide fraction by HPLC, we demonstrate its successful application to branching pattern analysis with improved efficiency and sensitivity and also the potential for automated operation.

An Improved Approach for N-Linked Glycan Structure Identification from HCD MS/MS Spectra.

Glycosylation is a frequently observed post-translational modification on proteins. Currently, tandem mass spectrometry (MS/MS) serves as an efficient analytical technique for characterizing structures of oligosaccharides. However, developing effective computational approaches for identifying glycan structures from mass spectra is still a great challenge in glycoproteomics research. In this study, we proposed an approach for matching the input spectra with glycan structures acquired from a glycan structure database by incorporating a de novo sequencing assisted ranking scheme. The proposed approach is implemented as a software tool, GlycoNovoDB, for automated glycan structure identification from HCD MS/MS of glycopeptides. Experimental results showed that GlycoNovoDB can identify glycans effectively and has better performance than our previously proposed de novo sequencing algorithm as well as another software GlycoMaster DB.

Glycomic and sialoproteomic data of gastric carcinoma cells overexpressing ST3GAL4.

Gastric carcinoma MKN45 cells stably transfected with the full-length ST3GAL4 gene were characterised by glycomic and sialoproteomic analysis. Complementary strategies were applied to assess the glycomic alterations induced by ST3GAL4 overexpression. The N- and O-glycome data were generated in two parallel structural analyzes, based on PGC-ESI-MS/MS. Data on glycan structure identification and relative abundance in ST3GAL4 overexpressing cells and respective mock control are presented. The sialoproteomic analysis based on titanium-dioxide enrichment of sialopeptides with subsequent LC-MS/MS identification was performed. This analysis identified 47 proteins with significantly increased sialylation. The data in this article is associated with the research article published in Biochim Biophys Acta “Glycomic analysis of gastric carcinoma cells discloses glycans as modulators of RON receptor tyrosine kinase activation in cancer” [1].

IgG and IgM glycosylation patterns in patients undergoing image-guided tumor ablation

BackgroundImage-guided tumor ablation is a technique whereby needle-like applicators are placed directly into solid tumors under guidance typically with computed tomography or ultrasound. Changes in IgG and IgM antibody glycosylation were studied during ablation-induced immune response to cancer, and the use of glycosylation as a biomarker for diagnosis, prognosis and disease treatment was examined. MethodsPlasma from 27 tumor patients was collected immediately before, after and for 6months following ablation. IgG and IgM antibodies were isolated by use high-throughput chromatography, and analyzed by hydrophilic liquid chromatography. Thorough identification of glycan structures in each chromatography peak was performed by nano-liquid chromatography electrospray ionization mass spectrometry. ResultsAlthough antibody glycosylation was found to vary with cancer type, discernable patterns of change based on the successful treatment of tumors by ablation were not identified. One patient with renal clear cell carcinoma and poor disease outcome had unexpectedly high amount of oligomannose IgG glycans during the whole period of monitoring. In contrast, IgM antibodies did not follow the same pattern. ConclusionsThese findings suggest that glycosylation patterns are indicative of an immune system that is unable to prevent different types of cancer, rather than products of the immunostimulatory response to the ablation of tumor itself. Analyses of the outcome effect suggested that IgG glycosylation and IgM glycosylation are not associated with tumor ablation. General significancePresent work opens a new way for parallel determination of glycosylation changes of both IgG and IgM antibodies by use of high-throughput methods, and their future use as biomarkers for disease diagnosis and prognosis. This article is part of a Special Issue entitled "Glycans in personalised medicine" Guest Editor: Professor Gordan Lauc.

An Effective Approach for Glycan Structure De Novo Sequencing From HCD Spectra

Mass spectrometry has become a widely used analytical technique for proteomics study because of its high throughput and sensitivity. Among those applications, a specific one is to characterize glycan structure. Glycosylation is a frequently occurred post-translational modification of proteins which is relevant to humans' health. Therefore, it is significant to develop effective computational methods to automate the identification of glycan structures from mass spectral data. In our research, we mathematically formulated the glycan de novo sequencing problem and proposed a heuristic algorithm for glycan de novo sequencing from HCD MS/MS spectra of N-linked glycopeptides. The algorithm proceeds in a carefully designate pathway to construct the best matched tree structure from MS/MS spectrum. Experimental results showed that our proposed approach can effectively identify glycan structures from HCD MS/MS spectra.

GlycanAnalysis Plug-in: a database search tool for N-glycan structures using mass spectrometry.

Tandem mass spectrometry (MS/MS or MS(n)) is a potent technique for characterizing N-glycan structures. GlycanAnalysis searches a glycan database to support the identification of glycan structures from MS/MS spectra. It also calculates diagnostic ions of glycan structures registered in a glycan database (GlycomeDB or KEGG GLYCAN) and searches for MS/MS spectra of N-glycans that match diagnostic ions to determine the structures. This program functions as a plug-in for Mass++, a freeware mass spectrum visualization and analysis program. The executable files of Mass++ are available for free at http://www.first-ms3d.jp/english/. The GlycanAnalysis plug-in is included in the standard package of Mass++ for Windows. k-morimt@shimadzu.co.jp or nishikaz@shimadzu.co.jp or acyshzw@shimadzu.co.jp Supplementary material are available at Bioinformatics online.

Relative quantitation of glycopeptides based on stable isotope labeling using MALDI-TOF MS.

We have developed an effective, sensitive method for quantitative glycopeptide profiling using stable isotope labeling and MALDI-TOF mass spectrometry (MS). In this study, we synthesized benzoic acid-d0 N-succinimidyl ester (BzOSu) and benzoic acid-d5 N-succinimidyl ester (d-BzOSu) as light and heavy isotope reagents for stable isotope quantification for the comparative analysis of glycopeptides. Using this approach provided enhanced ionization efficiency in both positive and negative modes by MALDI-TOF MS. These reagents were quantitatively reacted with glycopeptides from human serum IgG (hIgG) at a wide range of concentrations; the labeling efficiency of the glycopeptides showed high reproducibility and a good calibration curve was obtained. To demonstrate the practical utility of this approach, we characterized the structures of glycopeptides from hIgG and from IgG1 produced by myeloma plasma. The glycopeptides were quantitatively analyzed by mixing Bz-labeled IgG1 glycopeptides with d-Bz-labeled hIgG glycopeptides. Glycan structural identification of the hIgG glycopeptides was demonstrated by combining the highly specific recognition of endo-β-N-acetyl glucosaminidases from Streptococcus pyogenes (endoS) or from Streptococcus pneumoniae (endo-D) with MALDI-TOF MS analysis. The obtained data revealed the glycan profile and the ratio of glycan structural isomers containing a galactosylated extension on IgG1, IgG2 and IgG3 glycopetides.

Validation of the curation pipeline of UniCarb-DB: Building a global glycan reference MS/MS repository

The UniCarb-DB database is an emerging public glycomics data repository, containing over 500 tandem mass spectra (as of March 2013) of glycans released from glycoproteins. A major challenge in glycomics research is to provide and maintain high-quality datasets that will offer the necessary diversity to support the development of accurate bioinformatics tools for data deposition and analysis. The role of UniCarb-DB, as an archival database, is to provide the glycomics community with open-access to a comprehensive LC MS/MS library of N- and O- linked glycans released from glycoproteins that have been annotated with glycosidic and cross-ring fragmentation ions, retention times, and associated experimental metadata descriptions. Here, we introduce the UniCarb-DB data submission pipeline and its practical application to construct a library of LC–MS/MS glycan standards that forms part of this database. In this context, an independent consortium of three laboratories was established to analyze the same 23 commercially available oligosaccharide standards, all by using graphitized carbon-liquid chromatography (LC) electrospray ionization (ESI) ion trap mass spectrometry in the negative ion mode. A dot product score was calculated for each spectrum in the three sets of data as a measure of the comparability that is necessary for use of such a collection in library-based spectral matching and glycan structural identification. The effects of charge state, de-isotoping and threshold levels on the quality of the input data are shown. The provision of well-characterized oligosaccharide fragmentation data provides the opportunity to identify determinants of specific glycan structures, and will contribute to the confidence level of algorithms that assign glycan structures to experimental MS/MS spectra. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.

Unraveling the Glyco-Puzzle: Glycan Structure Identification by Capillary Electrophoresis

State-of-the-art high-resolution separation techniques play an important role in the full structural elucidation of glycans. Capillary electrophoresis (CE) offers a rapid yet simple method for exhaustive carbohydrate profiling. CE is a versatile analytical platform, which can be operated in several separation modes, simply by altering separation conditions during operation. For in-depth glycan structural analysis, CE has also gained significantly from the additional resolution introduced by complementary and orthogonal separation techniques such as ion exchange or hydrophilic interaction chromatography. Commercially available mass spectrometry (MS) interfaces have not only brought this information-rich detection technique within reach, but CE also represents an expedient highly efficient separation inlet for MS, capable of separating isobaric oligosaccharide isomers prior to MS detection and MS/MS fragmentation based identification. This Perspective gives a sophisticated impression of the versatility of capillary electrophoresis for deep structural elucidation of carbohydrates derived from glycoproteins of biomedical interest. Different separation modes for the analysis of both charged and neutral glycans, such as influencing electroosmotic flow, using complexation/interaction based secondary equilibria, and the use of charged and neutral labels are compared. The merits of introducing orthogonal and complementary techniques, such as exoglycosidase digestion arrays, analytical/preparative chromatography and mass spectrometric detection, extending the dynamic range and resolution of CE are all thoroughly discussed.

Human Urinary GM2-activator Protein as a Potential Biomarker for Lung Cancer

Human urine can be obtained from most patients by noninvasive ways of collection. It is a rich source of biomarkers for diagnostic tests, therapeutic guidance and prognostic information for patients and clinicians. Recently, the protein expression pattern of urine samples from lung cancer patients and healthy controls were compared using two-dimensional gel electrophoresis (2-DE). GM2 activator protein (GM2AP) in lung cancer patients with 2.5-4.0 folds higher than that found in healthy controls was the protein of interest, and this protein was focused because it is related to tumor-associated gangliosides found in cancer progression. The aim of this study was to investigate the urinary biomarkers associated with lung cancer. First, we applied proteomic approach to confirm the increase level of GM2AP in urine samples from lung cancer patients and healthy controls. Then, the expression level of urinary GM2AP was quantified using Western blotting and Enzyme-Linked Immunosorbent Assay (ELISA). The results show that the expression level of GM2AP significantly increased in each subtype of lung cancer patients (P<0.05). In addition, nano liquid chromatography coupled online with tandem mass spectrometry (NanoLC-MS/ MS) provided evidence of an increase of GM2AP with the glycan structure identification from doublely charge at m/z 1202.1025 corresponding to the structure of (Hex)3(HexNAc)2(Fuc)1 linked with peptide (PIIVOGNVTLSVVG). Overall data suggested that the GM2AP may be associated with lung cancer and may be useful as a marker to monitor lung cancer prognosis.

Enrichment of glycopeptides for glycan structure and attachment site identification

We present a method to enrich for glycoproteins from proteomic samples. Sialylated glycoproteins were selectively periodate-oxidized, captured on hydrazide beads, trypsinized and released by acid hydrolysis of sialic acid glycosidic bonds. Mass spectrometric fragment analysis allowed identification of glycan structures, and additional fragmentation of deglycosylated ions yielded peptide sequence information, which allowed glycan attachment site and protein identification. We identified 36 N-linked and 44 O-linked glycosylation sites on glycoproteins from human cerebrospinal fluid.