GRP78 Contributes to the Beneficial Effects of SGLT2 Inhibitor on Proximal Tubular Cells in DKD

The beneficial effects of sodium–glucose cotransporter 2 (SGLT2) inhibitors on kidney function are well-known; however, their molecular mechanisms are not fully understood. We focused on 78-kDa glucose-regulated protein (GRP78) and its interaction with SGLT2 and integrin-β1 beyond the chaperone property of GRP78. In streptozotocin (STZ)-induced diabetic mouse kidneys, GRP78, SGLT2, and integrin-β1 increased in the plasma membrane fraction, while they were suppressed by canagliflozin. The altered subcellular localization of GRP78/integrin-β1 in STZ mice promoted epithelial mesenchymal transition (EMT) and fibrosis, which were mitigated by canagliflozin. High-glucose conditions reduced intracellular GRP78, increased its secretion, and caused EMT-like changes in cultured HK2 cells, which were again inhibited by canagliflozin. Urinary GRP78 increased in STZ mice, and in vitro experiments with recombinant GRP78 suggested that inflammation spread to surrounding tubular cells and that canagliflozin reversed this effect. Under normal glucose culture, canagliflozin maintained sarco/endoplasmic reticulum (ER) Ca2+-ATPase (SERCA) activity, promoted ER robustness, reduced ER stress response impairment, and protected proximal tubular cells. In conclusion, canagliflozin restored subcellular localization of GRP78, SGLT2, and integrin-β1 and inhibited EMT and fibrosis in DKD. In nondiabetic chronic kidney disease, canagliflozin promoted ER robustness by maintaining SERCA activity and preventing ER stress response failure, and it contributed to tubular protection

Literary Form in Derek Jarman's The Angelic Conversation II : Dream Allegory

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Discriminative application of string similarity methods to chemical and non-chemical names for biomedical abbreviation clustering

BACKGROUND: Various computational methods are presently available to classify whether a protein variation is disease-associated or not. However data derived from recent technological advancements make it feasible to extend the annotation of disease-associated variations in order to include specific phenotypes. Here we tackle the problem of distinguishing between genetic variations associated to cancer and variations associated to other genetic diseases. RESULTS: We implement a new method based on Support Vector Machines that takes as input the protein variant and the protein function, as described by its associated Gene Ontology terms. Our approach succeeds in discriminating between germline variants that are likely to be cancer-associated from those that are related to other genetic disorders. The method performs with values of 90% accuracy and 0.61 Matthews correlation coefficient on a set comprising 6478 germline variations (16% are cancer-associated) in 592 proteins. The sensitivity and the specificity on the cancer class are 69% and 66%, respectively. Furthermore the method is capable of correctly excluding some 96% of 3392 somatic cancer-associated variations in 1983 proteins not included in the training/testing set. CONCLUSIONS: Here we prove feasible that a large set of cancer associated germline protein variations can be successfully discriminated from those associated to other genetic disorders. This is a step further in the process of protein variant annotation. Scoring largely improves when protein function as encoded by Gene Ontology terms is considered, corroborating the role of protein function as a key feature for a correct annotation of its variations