Oligosaccharyltransferase Inhibition Induces Senescence in RTK-Driven Tumor Cells

Asparagine (N)-linked glycosylation is a protein modification critical for glycoprotein folding, stability, and cellular localization. To identify small molecules that inhibit new targets in this biosynthetic pathway, we initiated a cell-based high throughput screen and lead compound optimization campaign that delivered a cell permeable inhibitor (NGI-1). NGI-1 targets the oligosaccharyltransferase (OST), a hetero-oligomeric enzyme that exists in multiple isoforms and transfers oligosaccharides to recipient proteins. In non-small cell lung cancer cells NGI-1 blocks cell surface localization and signaling of the EGFR glycoprotein, but selectively arrests proliferation in only those cell lines that are dependent on EGFR (or FGFR) for survival. In these cell lines OST inhibition causes cell cycle arrest accompanied by induction of p21, autofluorescence, and changes in cell morphology; all hallmarks of senescence. These results identify OST inhibition as a potential therapeutic approach for treating receptor tyrosine kinase-dependent tumors and provides a chemical probe for reversibly regulating N-linked glycosylation in mammalian cells

Identification of Genes Required for Neural-Specific Glycosylation Using Functional Genomics

Glycosylation plays crucial regulatory roles in various biological processes such as development, immunity, and neural functions. For example, α1,3-fucosylation, the addition of a fucose moiety abundant in Drosophila neural cells, is essential for neural development, function, and behavior. However, it remains largely unknown how neural-specific α1,3-fucosylation is regulated. In the present study, we searched for genes involved in the glycosylation of a neural-specific protein using a Drosophila RNAi library. We obtained 109 genes affecting glycosylation that clustered into nine functional groups. Among them, members of the RNA regulation group were enriched by a secondary screen that identified genes specifically regulating α1,3-fucosylation. Further analyses revealed that an RNA–binding protein, second mitotic wave missing (Swm), upregulates expression of the neural-specific glycosyltransferase FucTA and facilitates its mRNA export from the nucleus. This first large-scale genetic screen for glycosylation-related genes has revealed novel regulation of fucTA mRNA in neural cells

Genomics Meets Glycomics—The First GWAS Study of Human N-Glycome Identifies HNF1α as a Master Regulator of Plasma Protein Fucosylation

Over half of all proteins are glycosylated, and alterations in glycosylation have been observed in numerous physiological and pathological processes. Attached glycans significantly affect protein function; but, contrary to polypeptides, they are not directly encoded by genes, and the complex processes that regulate their assembly are poorly understood. A novel approach combining genome-wide association and high-throughput glycomics analysis of 2,705 individuals in three population cohorts showed that common variants in the Hepatocyte Nuclear Factor 1α (HNF1α) and fucosyltransferase genes FUT6 and FUT8 influence N-glycan levels in human plasma. We show that HNF1α and its downstream target HNF4α regulate the expression of key fucosyltransferase and fucose biosynthesis genes. Moreover, we show that HNF1α is both necessary and sufficient to drive the expression of these genes in hepatic cells. These results reveal a new role for HNF1α as a master transcriptional regulator of multiple stages in the fucosylation process. This mechanism has implications for the regulation of immunity, embryonic development, and protein folding, as well as for our understanding of the molecular mechanisms underlying cancer, coronary heart disease, and metabolic and inflammatory disorders

A recurrent homozygous missense DPM3 variant leads to muscle and brain disease.

Biallelic pathogenic variants in the genes encoding the dolichol-phosphate mannose synthase subunits (DPM) which produce mannosyl donors for glycosylphosphatidylinositols, N-glycan and protein O- and C-mannosylation, are rare causes of congenital disorders of glycosylation. Pathogenic variants in DPM1 and DPM2 are associated with muscle-eye-brain (MEB) disease, whereas DPM3 variants have mostly been reported in patients with isolated muscle disease-dystroglycanopathy. Thus far, only one affected individual with compound heterozygous DPM3 variants presenting with myopathy, mild intellectual disability, seizures, and nonspecific white matter abnormalities (WMA) around the lateral ventricles has been described. Here we present five affected individuals from four unrelated families with global developmental delay/intellectual disability ranging from mild to severe, microcephaly, seizures, WMA, muscle weakness and variable cardiomyopathy. Exome sequencing of the probands revealed an ultra-rare homozygous pathogenic missense DPM3 variant NM_018973.4:c.221A>G, p.(Tyr74Cys) which segregated with the phenotype in all families. Haplotype analysis indicated that the variant arose independently in three families. Functional analysis did not reveal any alteration in the N-glycosylation pathway caused by the variant; however, this does not exclude its pathogenicity in the function of the DPM complex and related cellular pathways. This report provides supporting evidence that, besides DPM1 and DPM2, defects in DPM3 can also lead to a muscle and brain phenotype

Importance of Glycosylation on Function of a Potassium Channel in Neuroblastoma Cells

The Kv3.1 glycoprotein, a voltage-gated potassium channel, is expressed throughout the central nervous system. The role of N-glycans attached to the Kv3.1 glycoprotein on conducting and non-conducting functions of the Kv3.1 channel are quite limiting. Glycosylated (wild type), partially glycosylated (N220Q and N229Q), and unglycosylated (N220Q/N229Q) Kv3.1 proteins were expressed and characterized in a cultured neuronal-derived cell model, B35 neuroblastoma cells. Western blots, whole cell current recordings, and wound healing assays were employed to provide evidence that the conducting and non-conducting properties of the Kv3.1 channel were modified by N-glycans of the Kv3.1 glycoprotein. Electrophoretic migration of the various Kv3.1 proteins treated with PNGase F and neuraminidase verified that the glycosylation sites were occupied and that the N-glycans could be sialylated, respectively. The unglycosylated channel favored a different whole cell current pattern than the glycoform. Further the outward ionic currents of the unglycosylated channel had slower activation and deactivation rates than those of the glycosylated Kv3.1 channel. These kinetic parameters of the partially glycosylated Kv3.1 channels were also slowed. B35 cells expressing glycosylated Kv3.1 protein migrated faster than those expressing partially glycosylated and much faster than those expressing the unglycosylated Kv3.1 protein. These results have demonstrated that N-glycans of the Kv3.1 glycoprotein enhance outward ionic current kinetics, and neuronal migration. It is speculated that physiological changes which lead to a reduction in N-glycan attachment to proteins will alter the functions of the Kv3.1 channel

Intragenic deletion in the LARGE gene causes Walker-Warburg syndrome

Intragenic homozygous deletions in the Large gene are associated with a severe neuromuscular phenotype in the myodystrophy (myd) mouse. These mutations result in a virtual lack of glycosylation of α-dystroglycan. Compound heterozygous LARGE mutations have been reported in a single human patient, manifesting with mild congenital muscular dystrophy (CMD) and severe mental retardation. These mutations are likely to retain some residual LARGE glycosyltransferase activity as indicated by residual α-dystroglycan glycosylation in patient cells. We hypothesized that more severe LARGE mutations are associated with a more severe CMD phenotype in humans. Here we report a 63-kb intragenic LARGE deletion in a family with Walker-Warburg syndrome (WWS), which is characterized by CMD, and severe structural brain and eye malformations. This finding demonstrates that LARGE gene mutations can give rise to a wide clinical spectrum, similar as for other genes that have a role in the post-translational modification of the α-dystroglycan protein

A new framework for cortico-striatal plasticity: behavioural theory meets In vitro data at the reinforcement-action interface

Operant learning requires that reinforcement signals interact with action representations at a suitable neural interface. Much evidence suggests that this occurs when phasic dopamine, acting as a reinforcement prediction error, gates plasticity at cortico-striatal synapses, and thereby changes the future likelihood of selecting the action(s) coded by striatal neurons. But this hypothesis faces serious challenges. First, cortico-striatal plasticity is inexplicably complex, depending on spike timing, dopamine level, and dopamine receptor type. Second, there is a credit assignment problem—action selection signals occur long before the consequent dopamine reinforcement signal. Third, the two types of striatal output neuron have apparently opposite effects on action selection. Whether these factors rule out the interface hypothesis and how they interact to produce reinforcement learning is unknown. We present a computational framework that addresses these challenges. We first predict the expected activity changes over an operant task for both types of action-coding striatal neuron, and show they co-operate to promote action selection in learning and compete to promote action suppression in extinction. Separately, we derive a complete model of dopamine and spike-timing dependent cortico-striatal plasticity from in vitro data. We then show this model produces the predicted activity changes necessary for learning and extinction in an operant task, a remarkable convergence of a bottom-up data-driven plasticity model with the top-down behavioural requirements of learning theory. Moreover, we show the complex dependencies of cortico-striatal plasticity are not only sufficient but necessary for learning and extinction. Validating the model, we show it can account for behavioural data describing extinction, renewal, and reacquisition, and replicate in vitro experimental data on cortico-striatal plasticity. By bridging the levels between the single synapse and behaviour, our model shows how striatum acts as the action-reinforcement interface

Cell Surface Sialylation and Fucosylation Are Regulated by L1 via Phospholipase Cγ and Cooperate to Modulate Neurite Outgrowth, Cell Survival and Migration

BACKGROUND: Cell surface glycosylation patterns are markers of cell type and status. However, the mechanisms regulating surface glycosylation patterns remain unknown. METHODOLOGY/PRINCIPAL FINDINGS: Using a panel of carbohydrate surface markers, we have shown that cell surface sialylation and fucosylation were downregulated in L1(-/y) neurons versus L1(+/y) neurons. Consistently, mRNA levels of sialyltransferase ST6Gal1, and fucosyltransferase FUT9 were significantly reduced in L1(-/y) neurons. Moreover, treatment of L1(+/y) neurons with L1 antibodies, triggering signal transduction downstream of L1, led to an increase in cell surface sialylation and fucosylation compared to rat IgG-treated cells. ShRNAs for both ST6Gal1 and FUT9 blocked L1 antibody-mediated enhancement of neurite outgrowth, cell survival and migration. A phospholipase Cgamma (PLCgamma) inhibitor and shRNA, as well as an Erk inhibitor, reduced ST6Gal1 and FUT9 mRNA levels and inhibited effects of L1 on neurite outgrowth and cell survival. CONCLUSIONS: Neuronal surface sialylation and fucosylation are regulated via PLCgamma by L1, modulating neurite outgrowth, cell survival and migration

Genome-scale modeling of the protein secretory machinery in yeast

The protein secretory machinery in Eukarya is involved in post-translational modification (PTMs) and sorting of the secretory and many transmembrane proteins. While the secretory machinery has been well-studied using classic reductionist approaches, a holistic view of its complex nature is lacking. Here, we present the first genome-scale model for the yeast secretory machinery which captures the knowledge generated through more than 50 years of research. The model is based on the concept of a Protein Specific Information Matrix (PSIM: characterized by seven PTMs features). An algorithm was developed which mimics secretory machinery and assigns each secretory protein to a particular secretory class that determines the set of PTMs and transport steps specific to each protein. Protein abundances were integrated with the model in order to gain system level estimation of the metabolic demands associated with the processing of each specific protein as well as a quantitative estimation of the activity of each component of the secretory machinery