Cell Line-, Protein-, and Sialoglycosite-Specific Control of Flux-Based Sialylation in Human Breast Cells: Implications for Cancer Progression

Abstract

Sialylation, a post-translational modification that impacts the structure, activity, and longevity of glycoproteins has been thought to be controlled primarily by the expression of sialyltransferases (STs). In this report we explore the complementary impact of metabolic flux on sialylation using a glycoengineering approach. Specifically, we treated three human breast cell lines (MCF10A, T-47D, and MDA-MB-231) with 1,3,4-O-Bu 3 ManNAc, a "high flux" metabolic precursor for the sialic acid biosynthetic pathway. We then analyzed N-glycan sialylation using solid phase extraction of glycopeptides (SPEG) mass spectrometry-based proteomics under conditions that selectively captured sialic acid-containing glycopeptides, referred to as "sialoglycosites." Gene ontology (GO) analysis showed that flux-based changes to sialylation were broadly distributed across classes of proteins in 1,3,4-O-Bu 3 ManNAc-treated cells. Only three categories of proteins, however, were "highly responsive" to flux (defined as two or more sialylation changes of 10-fold or greater). Two of these categories were cell signaling and cell adhesion, which reflect well-known roles of sialic acid in oncogenesis. A third category-protein folding chaperones-was unexpected because little precedent exists for the role of glycosylation in the activity of these proteins. The highly flux-responsive proteins were all linked to cancer but sometimes as tumor suppressors, other times as proto-oncogenes, or sometimes both depending on sialylation status. A notable aspect of our analysis of metabolically glycoengineered breast cells was decreased sialylation of a subset of glycosites, which was unexpected because of the increased intracellular levels of sialometabolite "building blocks" in the 1,3,4-O-Bu 3 ManNAc-treated cells. Sites of decreased sialylation were minor in the MCF10A (<25% of all glycosites) and T-47D (<15%) cells but dominated in the MDA-MB-231 line (∼60%) suggesting that excess sialic acid could be detrimental in advanced cancer and cancer cells can Saeui et al. Flux-Based Control of Sialylation evolve mechanisms to guard against hypersialylation. In summary, flux-driven changes to sialylation offer an intriguing and novel mechanism to switch between context-dependent pro-or anti-cancer activities of the several oncoproteins identified in this study. These findings illustrate how metabolic glycoengineering can uncover novel roles of sialic acid in oncogenesis.

Multi-Faceted Effects of ST6Gal1 Expression on Precursor B-Lineage Acute Lymphoblastic Leukemia

“…Indeed, Oswald et al ( 32 ), who studied mice with liver-specific St6gal1 knockout, remarked “our findings demonstrate that transcriptional changes, or lack thereof, cannot be reliably used as a surrogate for regulated changes in protein glycosylation within a cell”. In addition, the sialylation of glycan structures is determined not only by ST6Gal1 protein levels but also by hypoxia ( 55 ), interactions of ST6Gal1 with the glycosyltransferase B4Galt1 ( 56 ), and metabolic flux ( 57 , 58 ), which can regulate the availability of the donor sialic acid. The existence of inherent variability in sialylation is supported by other studies in which we analyzed the glycome of US7, LAX56 and LAX57 EV, and ST6Gal1 OE cells (Oliveira et al., in preparation).…”

Mass spectrometry and the cellular surfaceome

“…As mentioned before, many surface proteins are glycosylated with aberrant glycosylation patterns often related to disease (Komaromy et al, 2020). Cancer cells, for instance, have altered glycosylation patterns (e.g., core fucosylation [Miyoshi, 2008], alternative branching [Chandler et al, 2019], O-glycan alteration [Kudelka et al, 2015], and sialylation [Almaraz et al, 2012, Saeui et al, 2020). Such altered patterns can lead to disruption of cell-cell adhesion (Reticker-Flynn & Bhatia, 2015), alter T-cell recognition (Azevedo et al, 2018) and are associated with drug resistance (Very et al, 2017).…”

Strategies for Glycoengineering Therapeutic Proteins

“…Online tools such as the NetNGlyc Server, an N-linked glycosylation prediction site ( Gupta & Brunak 2002 ), can be used to estimate the likelihood that each of the possible engineered glycosylation sites will be successfully glycosylated. Sites with low likelihood of glycosylation (<0.55) can be disregarded before proceeding; in our experience, most sites with prediction frequencies of >0.70 or more are successfully glycosylated ( Saeui et al, 2020 ).…”