Membrane glycomics reveal heterogeneity and quantitative distribution of cell surface sialylation.

Given that unnatural sugar expression is metabolically achieved, the kinetics and disposition of incorporation can lend insight into the temporal and localization preferences of sialylation across the cell surface. However, common detection schemes lack the ability to detail the molecular diversity and distribution of target moieties. Here we employed a mass spectrometric approach to trace the placement of azido sialic acids on membrane glycoconjugates, which revealed substantial variations in incorporation efficiencies between N-/O-glycans, glycosites, and glycosphingolipids. To further explore the propensity for sialylation, we subsequently mapped the native glycome of model epithelial cell surfaces and illustrate that while glycosylation sites span broadly across the extracellular region, a higher number of heterogeneous glycoforms occur on sialylated sites closest to the transmembrane domain. Beyond imaging techniques, this integrative approach provides unprecedented details about the frequency and structure-specific distribution of cell surface sialylation, a critical feature that regulates cellular interactions and homeostatic pathways

The Hitchhiker’s Guide to the Metaverse

Non peer reviewe

MaskOCR: Text Recognition with Masked Encoder-Decoder Pretraining

Text images contain both visual and linguistic information. However, existing pre-training techniques for text recognition mainly focus on either visual representation learning or linguistic knowledge learning. In this paper, we propose a novel approach MaskOCR to unify vision and language pre-training in the classical encoder-decoder recognition framework. We adopt the masked image modeling approach to pre-train the feature encoder using a large set of unlabeled real text images, which allows us to learn strong visual representations. In contrast to introducing linguistic knowledge with an additional language model, we directly pre-train the sequence decoder. Specifically, we transform text data into synthesized text images to unify the data modalities of vision and language, and enhance the language modeling capability of the sequence decoder using a proposed masked image-language modeling scheme. Significantly, the encoder is frozen during the pre-training phase of the sequence decoder. Experimental results demonstrate that our proposed method achieves superior performance on benchmark datasets, including Chinese and English text images

SIRT5 promotes IDH2 desuccinylation and G6PD deglutarylation to enhance cellular antioxidant defense

Abstract Excess in mitochondrial reactive oxygen species (ROS) is considered as a major cause of cellular oxidative stress. NADPH, the main intracellular reductant, has a key role in keeping glutathione in its reduced form GSH, which scavenges ROS and thus protects the cell from oxidative damage. Here, we report that SIRT5 desuccinylates and deglutarylates isocitrate dehydrogenase 2 (IDH2) and glucose‐6‐phosphate dehydrogenase (G6PD), respectively, and thus activates both NADPH‐producing enzymes. Moreover, we show that knockdown or knockout of SIRT5 leads to high levels of cellular ROS. SIRT5 inactivation leads to the inhibition of IDH2 and G6PD, thereby decreasing NADPH production, lowering GSH, impairing the ability to scavenge ROS, and increasing cellular susceptibility to oxidative stress. Our study uncovers a SIRT5‐dependent mechanism that regulates cellular NADPH homeostasis and redox potential by promoting IDH2 desuccinylation and G6PD deglutarylation