Uremic conditions are common in end-stage kidney disease (ESKD) patients. Accelerated vascular diseases in uremic patients lead to heart failure, stroke, and hypertension. To investigate the effects of uremia on porcine arterial smooth muscle cells (aSMCs), bulk RNA sequencing was used to identify uremia-induced alterations in signaling pathways of aSMCs that might explain the aggressive cardiovascular diseases seen in patients with chronic kidney disease (CKD) and ESKD. Bulk RNA sequencing was performed on porcine aSMCs cultured with serum from normal or uremic pigs. Differentially expressed gene (DEG) analysis revealed that 295 genes were upregulated and 138 genes were downregulated after uremic serum exposure. Gene Ontology molecular function analysis demonstrated that ATP-dependent activity, translation factor activity, and ATP-dependent protein folding chaperones were predicted to be negatively enriched after uremic serum exposure, while proton transmembrane transporter activity, antioxidant activity, and glutathione peroxidase activity were predicted to be positively enriched. Gene set enrichment analysis indicated that the cell cycle was predicted to be negatively enriched after uremic serum exposure in aSMCs. Overrepresentation analysis found that focal adhesion, protein processing in the endoplasmic reticulum (ER) and cell senescence were predicted to be negatively enriched, while lysosome, phagosome, apoptosis, and autophagy were predicted to be positively enriched after uremic serum exposure. This study suggests that the signaling pathways that regulate cellular redox homeostasis, the cellular waste disposal system, ER stress and autophagy are major signaling pathways involved in aSMCs’ responses to uremic serum exposure. These pathways may contribute to the severe arterial-specific clinical symptoms observed in CKD/ESKD patients, such as arterial stiffness, vascular calcification and cardiovascular disease.
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
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It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or sequence constraints.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.