Differential recognition of oligomannose isomers by glycan-binding proteins involved in innate and adaptive immunity

Gao, Chao; Stavenhagen, Kathrin; Eckmair, Barbara; McKitrick, Tanya R.; Mehta, Akul Y.; Matsumoto, Yasuyuki; McQuillan, Alyssa M; Hanes, Melinda S.; Eriş, Deniz; Jia, Nan; Wei, Mohui; Heimburg-Molinaro, Jamie; Ernst, Beat; Cummings, Richard D.

doi:10.1126/sciadv.abf6834

Cited by 20 publications

(21 citation statements)

References 68 publications

(101 reference statements)

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“…We found that Manα1,2Manα1,3Man and GlcNAcβ1,2Manα1,3Man bound with DC-SIGN were more stable than Manα1,2Manα1,2Man based on residence time. The findings were consistent with those from a recent glycan assay study ( Figure 6 C) [ 51 ]. We inferred that DC-SIGN recognizes glycan epitop50es when glycoforms expose them in a particular conformation.…”

Section: Resultssupporting

confidence: 92%

Glycan Epitopes and Potential Glycoside Antagonists of DC-SIGN Involved in COVID-19: In Silico Study

Gao

et al. 2021

Biomolecules

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Glycosylation is an important post-translational modification that affects a wide variety of physiological functions. DC-SIGN (Dendritic Cell-Specific Intercellular adhesion molecule-3-Grabbing Non-integrin) is a protein expressed in antigen-presenting cells that recognizes a variety of glycan epitopes. Until now, the binding of DC-SIGN to SARS-CoV-2 Spike glycoprotein has been reported in various articles and is regarded to be a factor in systemic infection and cytokine storm. The mechanism of DC-SIGN recognition offers an alternative method for discovering new medication for COVID-19 treatment. Here, we discovered three potential pockets that hold different glycan epitopes by performing molecular dynamics simulations of previously reported oligosaccharides. The “EPN” motif, “NDD” motif, and Glu354 form the most critical pocket, which is known as the Core site. We proposed that the type of glycan epitopes, rather than the precise amino acid sequence, determines the recognition. Furthermore, we deduced that oligosaccharides could occupy an additional site, which adds to their higher affinity than monosaccharides. Based on our findings and previously described glycoforms on the SARS-CoV-2 Spike, we predicted the potential glycan epitopes for DC-SIGN. It suggested that glycan epitopes could be recognized at multiple sites, not just Asn234, Asn149 and Asn343. Subsequently, we found that Saikosaponin A and Liquiritin, two plant glycosides, were promising DC-SIGN antagonists in silico.

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Section: Resultssupporting

confidence: 92%

Glycan Epitopes and Potential Glycoside Antagonists of DC-SIGN Involved in COVID-19: In Silico Study

Gao

et al. 2021

Biomolecules

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“…First, we used the recently published oligomannose array for this purpose. [ 53 ] Here, investigators assayed a variety of subtly different oligomannose glycans for their binding to various lectins. The slight changes in glycan structure, which nonetheless led to appreciable differences in their binding behavior, made this a particularly challenging case study for our model.…”

Section: Resultsmentioning

confidence: 99%

LectinOracle: A Generalizable Deep Learning Model for Lectin–Glycan Binding Prediction

Lundstrøm

Korhonen

Lisacek³

et al. 2021

Advanced Science

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Ranging from bacterial cell adhesion over viral cell entry to human innate immunity, glycan-binding proteins or lectins are abound in nature. Widely used as staining and characterization reagents in cell biology and crucial for understanding the interactions in biological systems, lectins are a focal point of study in glycobiology. Yet the sheer breadth and depth of specificity for diverse oligosaccharide motifs has made studying lectins a largely piecemeal approach, with few options to generalize. Here, LectinOracle, a model combining transformer-based representations for proteins and graph convolutional neural networks for glycans to predict their interaction, is presented. Using a curated data set of 564,647 unique protein-glycan interactions, it is shown that LectinOracle predictions agree with literature-annotated specificities for a wide range of lectins. Using a range of specialized glycan arrays, it is shown that LectinOracle predictions generalize to new glycans and lectins, with qualitative and quantitative agreement with experimental data. It is further demonstrated that LectinOracle can be used to improve lectin classification, accelerate lectin directed evolution, predict epidemiological outcomes in the context of influenza virus, and analyze whole lectomes in host-microbe interactions. It is envisioned that the herein presented platform will advance both the study of lectins and their role in (glyco)biology.

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“…Supporting the cell surface expression of oligomannose, several mannose-recognizing receptors such as macrophage mannose receptor (CD206), DC-SIGN [ 79 ], langerin [ 80 ] and dectin-2 [ 81 ] have been found to recognize oligomannosidic N -glycans expressed on tumour cells [ 82 ]. The recent development of a comprehensive oligomannose microarray may open for the identification of additional receptors recognizing oligomannose-decorated cancer cells and tissues [ 83 ].…”

Section: Resultsmentioning

confidence: 99%

Trends in oligomannosylation and α1,2-mannosidase expression in human cancers

et al. 2021

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Aberrant protein glycosylation is a prominent cancer feature. While many tumour-associated glycoepitopes have been reported, advances in glycoanalytics continue to uncover new associations between glycosylation and cancer. Guided by a comprehensive literature survey suggesting that oligomannosylation (Man 5–9 GlcNAc 2 ) is a widespread and often regulated glycosignature in human cancers, we here revisit a valuable compilation of nearly 500 porous graphitized carbon LC-MS/MS N -glycomics datasets acquired across 11 human cancer types to systematically test for oligomannose-cancer associations. Firstly, the quantitative glycomics data obtained across 34 cancerous cell lines demonstrated that oligomannosylation is a pan-cancer feature spanning in a wide abundance range. In keeping with literature, our quantitative glycomics data of tumour and matching control tissues and new MALDI-MS imaging data of tissue microarrays showed a strong cancer-associated elevation of oligomannosylation in both basal cell ( p = 1.78 × 10 –12 ) and squamous cell ( p = 1.23 × 10 –11 ) skin cancer and colorectal cancer ( p = 8.0 × 10 –4 ). The glycomics data also indicated that some cancer types including gastric and liver cancer exhibit unchanged or reduced oligomannose levels, observations also supported by literature and MALDI-MS imaging data. Finally, expression data from public cancer repositories indicated that several α1,2-mannosidases are regulated in tumour tissues suggesting that these glycan-processing enzymes may contribute to the cancer-associated modulation of oligomannosylation. This omics-centric study has compiled robust glycomics and enzyme expression data revealing interesting molecular trends that open avenues to better understand the role of oligomannosylation in human cancers.

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Differential recognition of oligomannose isomers by glycan-binding proteins involved in innate and adaptive immunity

Cited by 20 publications

References 68 publications

Glycan Epitopes and Potential Glycoside Antagonists of DC-SIGN Involved in COVID-19: In Silico Study

Glycan Epitopes and Potential Glycoside Antagonists of DC-SIGN Involved in COVID-19: In Silico Study

LectinOracle: A Generalizable Deep Learning Model for Lectin–Glycan Binding Prediction

Trends in oligomannosylation and α1,2-mannosidase expression in human cancers

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