In vivo longitudinal 920 nm two-photon intravital kidney imaging of a dynamic 2,8-DHA crystal formation and tubular deterioration in the adenine-induced chronic kidney disease mouse model

Adenine-induced animal model of chronic kidney disease: current applications and future perspectives

Chronic kidney disease (CKD) with high morbidity and mortality all over the world is characterized by decreased kidney function, a condition which can result from numerous risk factors, including diabetes, hypertension and obesity. Despite significant advances in our understanding of the pathogenesis of CKD, there are still no treatments that can effectively combat CKD, which underscores the urgent need for further study into the pathological mechanisms underlying this condition. In this regard, animal models of CKD are indispensable. This article reviews a widely used animal model of CKD, which is induced by adenine. While a physiologic dose of adenine is beneficial in terms of biological activity, a high dose of adenine is known to induce renal disease in the organism. Following a brief description of the procedure for disease induction by adenine, major mechanisms of adenine-induced CKD are then reviewed, including inflammation, oxidative stress, programmed cell death, metabolic disorders, and fibrillation. Finally, the application and future perspective of this adenine-induced CKD model as a platform for testing the efficacy of a variety of therapeutic approaches is also discussed. Given the simplicity and reproducibility of this animal model, it remains a valuable tool for studying the pathological mechanisms of CKD and identifying therapeutic targets to fight CKD.

Discovery of 2,8-dihydroxyadenine in HUA patients with uroliths and biomarkers for its associated nephropathy

Currently, it is acknowledged that gout is caused by uric acid (UA). However, some studies have revealed no correlation between gout and UA levels, and growing evidence suggests that 2,8-dihydroxyadenine (2,8-DHA), whose structural formula is similar to UA but is less soluble, may induce gout. Hence, we hypothesized that uroliths from hyperuricemia (HUA) patients, which is closely associated with gout, may contain 2,8-DHA. In this study, 2,8-DHA in uroliths and serum of HUA patients were determined using HPLC. Moreover, bioinformatics was used to investigate the pathogenic mechanisms of 2,8-DHA nephropathy. Subsequently, a mouse model of 2,8-DHA nephropathy established by the gavage administration of adenine, as well as a model of injured HK-2 cells induced by 2,8-DHA were used to explore the pathogenesis of 2,8-DHA nephropathy. Interestingly, 2,8-DHA could readily deposit in the cortex of the renal tubules, and was found in the majority of these HUA patients. Additionally, the differentially expressed genes between 2,8-DHA nephropathy mice and control mice were found to be involved in inflammatory reactions. Importantly, CCL2 and IL-1β genes had the maximum degree, closeness, and betweenness centrality scores. The expressions of CCL2 and IL-1β genes were significantly increased in the serum of 24 HUA patients with uroliths, indicating that they may be significant factors for 2,8-DHA nephropathy. Further analysis illustrated that oxidative damage and inflammation were the crucial processes of 2,8-DHA renal injury, and CCL2 and IL-1β genes were verified to be essential biomarkers for 2,8-DHA nephropathy. These findings revealed further insights into 2,8-DHA nephropathy, and provided new ideas for its diagnosis and treatment.

Statistically unbiased prediction enables accurate denoising of voltage imaging data

Here we report SUPPORT (statistically unbiased prediction utilizing spatiotemporal information in imaging data), a self-supervised learning method for removing Poisson-Gaussian noise in voltage imaging data. SUPPORT is based on the insight that a pixel value in voltage imaging data is highly dependent on its spatiotemporal neighboring pixels, even when its temporally adjacent frames alone do not provide useful information for statistical prediction. Such dependency is captured and used by a convolutional neural network with a spatiotemporal blind spot to accurately denoise voltage imaging data in which the existence of the action potential in a time frame cannot be inferred by the information in other frames. Through simulations and experiments, we show that SUPPORT enables precise denoising of voltage imaging data and other types of microscopy image while preserving the underlying dynamics within the scene.