Detection of site-specific glycosylation in proteins using flow cytometry

O-GlcNAcylation in early stages of chronic lymphocytic leukemia; protocol development for flow cytometry

BACKGROUND

Recent studies proved that metabolic changes in malignant disorders have an impact on protein glycosylation, however, only a few attempts have been made so far to use O-GlcNAc analysis as a prognostic tool. Glucose metabolism is reported to be altered in hematological malignancies thus, we hypothesized that monitoring intracellular O-GlcNAc levels in Rai stage 0-I (Binet A) CLL patients could give deeper insights regarding subtle metabolic changes of progression which are not completely detected by the routine follow-up procedures.

OBJECTIVE

In this proof of concept study we established a flow cytometric detection method for the assessment of O-GlcNAcylation as a possible prognostic marker in CLL malignancy which was supported by fluorescence microscopy.

METHODS

Healthy volunteers and CLL patients were recruited for this study. Lymphocytes were isolated, fixed and permeabilised by various methods to find the optimal experimental condition for O-GlcNAc detection by flow cytometry. O-GlcNAc levels were measured and compared to lymphocyte count and various blood parameters including plasma glucose level.

RESULTS

The protocol we developed includes red blood cell lysis, formalin fixation, 0.1% Tween 20 permeabilisation and employs standardized cell number per sample and unstained controls. We have found significant correlation between O-GlcNAc levels and WBC (R2= 0.8535, p< 0.0029) and lymphocyte count (R2= 0.9225, p< 0.0006) in CLL patients. Interestingly, there was no such correlation in healthy individuals (R2= 0.05664 for O-GlcNAc vs WBC and R2= 0.04379 for O-GlcNAc vs lymphocytes).

CONCLUSION

Analyzing O-GlcNAc changes in malignant disorders, specifically in malignant hematologic diseases such as CLL, could be a useful tool to monitor the progression of the disease.

GlycoMinestruct: a new bioinformatics tool for highly accurate mapping of the human N-linked and O-linked glycoproteomes by incorporating structural features

Glycosylation plays an important role in cell-cell adhesion, ligand-binding and subcellular recognition. Current approaches for predicting protein glycosylation are primarily based on sequence-derived features, while little work has been done to systematically assess the importance of structural features to glycosylation prediction. Here, we propose a novel bioinformatics method called GlycoMine^struct(http://glycomine.erc.monash.edu/Lab/GlycoMine_Struct/) for improved prediction of human N- and O-linked glycosylation sites by combining sequence and structural features in an integrated computational framework with a two-step feature-selection strategy. Experiments indicated that GlycoMine^struct outperformed NGlycPred, the only predictor that incorporated both sequence and structure features, achieving AUC values of 0.941 and 0.922 for N- and O-linked glycosylation, respectively, on an independent test dataset. We applied GlycoMine^struct to screen the human structural proteome and obtained high-confidence predictions for N- and O-linked glycosylation sites. GlycoMine^struct can be used as a powerful tool to expedite the discovery of glycosylation events and substrates to facilitate hypothesis-driven experimental studies.

Competition between core-2 GlcNAc-transferase and ST6GalNAc-transferase regulates the synthesis of the leukocyte selectin ligand on human P-selectin glycoprotein ligand-1

The binding of selectins to carbohydrate ligands expressed on leukocytes regulates immunity and inflammation. Among the human selectin ligands, the O-linked glycans at the N-terminus of the leukocyte cell-surface molecule P-selectin glycoprotein ligand-1 (PSGL-1, CD162) are important because they bind all selectins (L-, E-, and P-selectin) with high affinity under hydrodynamic shear conditions. Analysis of glycan microheterogeneity at this site is complicated by the presence of 72 additional potential O-linked glycosylation sites on this mucinous protein. To overcome this limitation, truncated forms of PSGL-1, called "PSGL-1 peptide probes," were developed. Ultra-high sensitivity mass spectrometry analysis of glycans released from such probes along with glycoproteomic analysis demonstrate the presence of both the sialyl Lewis-X (sLe(X)) and the di-sialylated T-antigen (NeuAcα2,3Galβ1,3(NeuAcα2,6)GalNAc) at the PSGL-1 N-terminus. Overexpression of glycoprotein-specific ST6GalNAc-transferases (ST6GalNAc1, -2, or -4) in human promyelocytic HL-60 cells altered glycan structures and cell adhesion properties. In particular, ST6GalNAc2 overexpression abrogated cell surface HECA-452/CLA expression, reduced the number of rolling leukocytes on P- and L-selectin-bearing substrates by ~85%, and increased median rolling velocity of remaining cells by 80-150%. Cell rolling on E-selectin was unaltered although the number of adherent cells was reduced by 60%. ST6GalNAc2 partially co-localizes in the Golgi with the core-2 β(1,6)GlcNAc-transferase C2GnT-1. Overall, the data describe the glycan microheterogeneity at the PSGL-1 N-terminus. They suggest that a competition between ST6GalNAc2 and C2GnT-1 for the core-1/Galβ1,3GalNAc glycan may regulate leukocyte adhesion under fluid shear.

Silencing α1,3-fucosyltransferases in human leukocytes reveals a role for FUT9 enzyme during E-selectin-mediated cell adhesion

Leukocyte adhesion during inflammation is initiated by the binding of sialofucosylated carbohydrates expressed on leukocytes to endothelial E/P-selectin. Although the glycosyltransferases (glycoTs) constructing selectin-ligands have largely been identified using knock-out mice, important differences may exist between humans and mice. To address this, we developed a systematic lentivirus-based shRNA delivery workflow to create human leukocytic HL-60 cell lines that lack up to three glycoTs. Using this, the contributions of all three myeloid α1,3-fucosyltransferases (FUT4, FUT7, and FUT9) to selectin-ligand biosynthesis were evaluated. The cell adhesion properties of these modified cells to L-, E-, and P-selectin under hydrodynamic shear were compared with bone marrow-derived neutrophils from Fut4(-/-)Fut7(-/-) dual knock-out mice. Results demonstrate that predominantly FUT7, and to a lesser extent FUT4, forms the selectin-ligand at the N terminus of leukocyte P-selectin glycoprotein ligand-1 (PSGL-1) in humans and mice. Here, 85% reduction in leukocyte interaction was observed in human FUT4(-)7(-) dual knockdowns on P/L-selectin substrates. Unlike Fut4(-/-)Fut7(-/-) mouse neutrophils, however, human knockdowns lacking FUT4 and FUT7 only exhibited partial reduction in rolling interaction on E-selectin. In this case, the third α1,3-fucosyltransferase FUT9 played an important role because leukocyte adhesion was reduced by 50-60% in FUT9-HL-60, 70-80% in dual knockdown FUT7(-)9(-) cells, and ∼85% in FUT4(-)7(-)9(-) triple knockdowns. Gene silencing results are in agreement with gain-of-function experiments where all three fucosyltransferases conferred E-selectin-mediated rolling in HEK293T cells. This study advances new tools to study human glycoT function. It suggests a species-specific role for FUT9 during the biosynthesis of human E-selectin ligands.

Systems glycobiology: biochemical reaction networks regulating glycan structure and function

There is a growing use of bioinformatics based methods in the field of Glycobiology. These have been used largely to curate glycan structures, organize array-based experimental data and display existing knowledge of glycosylation-related pathways in silico. Although the cataloging of vast amounts of data is beneficial, it is often a challenge to gain meaningful mechanistic insight from this exercise alone. The development of specific analysis tools to query the database is necessary. If these queries can integrate existing knowledge of glycobiology, new insights may be gained. Such queries that couple biochemical knowledge and mathematics have been developed in the field of Systems Biology. The current review summarizes the current state of the art in the application of computational modeling in the field of Glycobiology. It provides (i) an overview of experimental and online resources that can be used to construct glycosylation reaction networks, (ii) mathematical methods to formulate the problem including a description of ordinary differential equation and logic-based reaction networks, (iii) optimization techniques that can be applied to fit experimental data for the purpose of model reconstruction and for evaluating unknown model parameters, (iv) post-simulation analysis methods that yield experimentally testable hypotheses and (v) a summary of available software tools that can be used by non-specialists to perform many of the above functions.