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Abstract A key nutrient sensing process in all animal tissues is the dynamic attachment of O-linked N-acetylglucosamine (O-GlcNAc). Determining the targets and roles of O-GlcNAc glycoproteins has the potential to reveal insights into healthy and diseased metabolic states. In cell studies, thousands of proteins are known to be O-GlcNAcylated, but reference datasets for most tissue types in animals are lacking. Here, we apply a chemoenzymatic labeling study to compile a high coverage dataset of quadriceps skeletal muscle O-GlcNAc glycoproteins from mice. Our dataset contains over 550 proteins, and > 80% of the dataset matched known O-GlcNAc proteins. This dataset was further annotated via bioinformatics, revealing the distribution, protein interactions, and gene ontology (GO) functions of these skeletal muscle proteins. We compared these quadriceps glycoproteins with a high-coverage O-GlcNAc enrichment profile from mouse hearts and describe the key overlap and differences between these tissue types. Quadriceps muscles can be used for biopsies, so we envision this dataset to have potential biomedical relevance in detecting aberrant glycoproteins in metabolic diseases and physiological studies. This new knowledge adds to the growing collection of tissues with high-coverage O-GlcNAc profiles, which we anticipate will further the systems biology of O-GlcNAc mechanisms, functions, and roles in disease. 

Abstract Accurate subseasonal prediction of heavy rainfall is helpful for disaster mitigation but challenging. The land thermal condition of Tibetan Plateau (TP), usually with climate memory ranging from weeks to seasons, has been seen as a potential predictability source for subseasonal prediction. Aiming at 2020 record‐breaking Mei‐yu rainfall, this study attempts to investigate whether and how the influence of initial TP surface thermal condition near late June influences the July rainfall prediction over the Middle and Lower Yangtze River Region (MLYR), based on two contrasting prediction experiments using a global climate ensemble prediction system. The results show that the most distinguishable change in the downstream prediction in July is the anomalous low‐tropospheric cyclone and the associated increased rainfall over MLYR corresponding to the warmer initial condition of surface TP. Influenced by the invasion of the positive potential vorticity (PV) center that generated over TP and propagated eastward, this low‐level cyclone anomaly over MLYR is formed within the first week of prediction, and persists for the next 3 weeks maintained by the positive feedback between the low‐level cyclone and middle‐tropospheric latent heating over MLYR in the prediction. This study confirmed the significant effect of TP initial thermal condition on downstream prediction ahead of 3 weeks during the Mei‐yu season (peak summer) with strong land–atmosphere coupling over TP.