Installation of O-glycan sulfation capacities in human HEK293 cells for display of sulfated mucins

Abstract

The human genome contains at least 35 genes that encode Golgi sulfotransferases that function in the secretory pathway, where they are involved in decorating glycosaminoglycans, glycolipids, and glycoproteins with sulfate groups. Although a number of important interactions by proteins such as selectins, galectins, and sialic acid–binding immunoglobulin-like lectins are thought to mainly rely on sulfated O-glycans, our insight into the sulfotransferases that modify these glycoproteins, and in particular GalNAc-type O-glycoproteins, is limited. Moreover, sulfated mucins appear to accumulate in respiratory diseases, arthritis, and cancer. To explore further the genetic and biosynthetic regulation of sulfated O-glycans, here we expanded a cell-based glycan array in the human embryonic kidney 293 (HEK293) cell line with sulfation capacities. We stably engineered O-glycan sulfation capacities in HEK293 cells by site-directed knockin of sulfotransferase genes in combination with knockout of genes to eliminate endogenous O-glycan branching (core2 synthase gene GCNT1 ) and/or sialylation capacities in order to provide simplified substrates (core1 Galβ1–3GalNAcα1–O-Ser/Thr) for the introduced sulfotransferases. Expression of the galactose 3- O -sulfotransferase 2 in HEK293 cells resulted in sulfation of core1 and core2 O-glycans, whereas expression of galactose 3- O -sulfotransferase 4 resulted in sulfation of core1 only. We used the engineered cell library to dissect the binding specificity of galectin-4 and confirmed binding to the 3- O -sulfo-core1 O-glycan. This is a first step toward expanding the emerging cell-based glycan arrays with the important sulfation modification for display and production of glycoconjugates with sulfated O-glycans.

Mechanistic and Structural Insights into the Specificity and Biological Functions of Bacterial Sulfoglycosidases

“…More interestingly, Gram-positive bacteria that encode BbhII orthologues, for example, Actinomyces sp. Oral and Actinomyces graevenitzii, are found to be more abundant in the tongue dorsum and other oral cavity locations, which could be an implication of these organisms taking advantage of the sulfoglycans in the human diet or participating the oral homeostasis by changing the 6S-GlcNAc-containing glycans from sulfomucins that coat the oral surface, such as MUC5B , and MUC1. , …”

“…Oral and Actinomyces graevenitzii, are found to be more abundant in the tongue dorsum and other oral cavity locations, which could be an implication of these organisms taking advantage of the sulfoglycans in the human diet or participating the oral homeostasis by changing the 6S-GlcNAc-containing glycans from sulfomucins that coat the oral surface, such as MUC5B 49,50 and MUC1. 51,52 ■ DISCUSSION The 6-sulfate adjunct on GlcNAc is contributed by three types of human GlcNAc-6-O-sulfotransferases (GlcNAc6STs-CHT2, CHT4, and CHT5) and it serves diverse roles in biological processes. 53,54 The characterization of exo-acting 6S-GlcNAcases expressed by prominent human microbes is an important step forward to advance our understanding of the complex relationship between these bacteria and their human host.…”

Deep learning explains the biology of branched glycans from single-cell sequencing data

“…We envision a pipeline in which this explainable DL approach is used to analyze data generated by SUGAR-seq or similar technologies, to decode the biology of less well-studied glycoforms (e.g., high-mannose glycans, sulfated glycans, and I-branched glycans) and their importance in disease ( Chuzel et al., 2021 ; Dimitroff, 2019 ; de Leoz et al., 2011 ; Loke et al., 2016 ; Sun et al., 2022 ). For this, future work needs to expand the capabilities of the associated lectin-seq, similar to the already reported Glycan-seq technology ( Oinam et al., 2021 ).…”

Deep Learning Explains the Biology of Branched Glycans from Single-Cell Sequencing Data

“…We envision a pipeline in which this explainable DL approach is used to analyze data generated by SUGAR-seq or similar technologies, to decode the biology of less well-studied glycoforms (e.g., high-mannose glycans, sulfated glycans, I-branched glycans) and their importance in disease (Chuzel et al, 2021; Dimitroff, 2019; de Leoz et al, 2011; Loke et al, 2016; Sun et al, 2022). For this, future work needs to expand the capabilities of the associated lectin-seq, similar to the already reported Glycan-seq technology (Oinam et al, 2021).…”

“…In line with the immunosuppressive role of β1,6-branched glycans, CCL2 secreted by cancer cells contributes to an immunosuppressive tumor microenvironment, and blockade of CCR2 in mice improved the efficacy of immune checkpoint therapy (Matsuo et al, 2021;Tu et al, 2020), suggesting a direct biomedically relevant role of this glycan feature. We envision a pipeline in which this explainable DL approach is used to analyze data generated by SUGAR-seq or similar technologies, to decode the biology of less well-studied glycoforms (e.g., high-mannose glycans, sulfated glycans, I-branched glycans) and their importance in disease (Chuzel et al, 2021;Dimitroff, 2019;de Leoz et al, 2011;Loke et al, 2016;Sun et al, 2022). For this, future work needs to expand the capabilities of the associated lectin-seq, similar to the already reported Glycan-seq technology (Oinam et al, 2021).…”