NUMB dysfunction defines a novel mechanism underlying hyperuricemia and gout.

Defective renal excretion and increased production of uric acid engender hyperuricemia that predisposes to gout. However, molecular mechanisms underlying defective uric acid excretion remain largely unknown. Here, we report a rare genetic variant of gout-unprecedented NUMB gene within a hereditary human gout family, which was identified by an unbiased genome-wide sequencing approach. This dysfunctional missense variant within the conserved region of the NUMB gene (NUMBR630H) underwent intracellular redistribution and degradation through an autophagy-dependent mechanism. Mechanistically, we identified the uric acid transporter, ATP Binding Cassette Subfamily G Member 2 (ABCG2), as a novel NUMB-binding protein through its intracellular YxNxxF motif. In polarized renal tubular epithelial cells (RTECs), NUMB promoted ABCG2 trafficking towards the apical plasma membrane. Genetic loss-of-function of NUMB resulted in redistribution of ABCG2 in the basolateral domain and ultimately defective excretion of uric acid. To recapitulate the clinical situation in human gout patients, we generated a NUMBR630H knock-in mouse strain, which showed marked increases of serum urate and decreased uric acid excretion. The NUMBR630H knock-in mice exhibited clinically relevant hyperuricemia. In summary, we have uncovered a novel NUMB-mediated mechanism of uric acid excretion and a functional missense variant of NUMB in humans, which causes hyperuricemia and gout.

Association between Serum Phosphorus Levels and Diabetic Retinopathy: A Cross-Sectional Study.

Background and Aims: The aim of this study was to investigate the association between serum phosphate levels and diabetic retinopathy (DR) in patients with type 2 diabetes mellitus (T2DM). Methods and Results: The study sample consisted of 1657 T2DM patients hospitalized between 2017 and 2019. Patients were categorized into quartiles based on their serum phosphate levels (Q1-Q4). An increasing trend in the prevalence of DR was observed across these quartiles. Subsequently, logistic regression analysis was employed to adjust for potential confounders, such as gender, age, BMI, and duration of diabetes, and to evaluate the odds ratios (ORs) associated with these quartiles. The prevalence of DR showed an increasing trend with elevated serum phosphate levels. Logistic regression further confirmed that serum phosphate levels remain an independent risk factor for DR. Conclusion: Elevated serum phosphate levels are closely associated with the prevalence of DR in hospitalized T2DM patients. Further studies are needed to establish causality. This trial is registered with chiCTR2000032374.

Nonenzymatic function of DPP4 in diabetes-associated mitochondrial dysfunction and cognitive impairment.

Dipeptidyl peptidase-4 (DPP4) has been proven to exert its functions by both enzymatic and nonenzymatic pathways. The nonenzymatic function of DPP4 in diabetes-associated cognitive impairment remains unexplored. We determined DPP4 protein concentrations or its enzymatic activity in type 2 diabetic patients and db/db mice and tested the impact of the non-enzymatic function of DPP4 on mitochondrial dysfunction and cognitive impairment both in vivo and in vitro. The results show that increased DPP4 activity was an independent risk factor for incident mild cognitive impairment (MCI) in type 2 diabetic patients. In addition, DPP4 was highly expressed in the hippocampus of db/db mice and contributed to mitochondria dysfunction and cognitive impairment. Mechanistically, DPP4 might bind to PAR2 in the hippocampus and trigger GSK-3ß activation, which downregulates peroxisome proliferator-activated receptor gamma coactivator 1 alpha expression and leads to mitochondria dysfunction, thereby promoting cognitive impairment in diabetes. Our findings indicate that the nonenzymatic function of DPP4 might promote mitochondrial dysfunction and cognitive impairment in diabetes.

Voiceprint Identification for Limited Dataset Using the Deep Migration Hybrid Model Based on Transfer Learning.

The convolutional neural network (CNN) has made great strides in the area of voiceprint recognition; but it needs a huge number of data samples to train a deep neural network. In practice, it is too difficult to get a large number of training samples, and it cannot achieve a better convergence state due to the limited dataset. In order to solve this question, a new method using a deep migration hybrid model is put forward, which makes it easier to realize voiceprint recognition for small samples. Firstly, it uses Transfer Learning to transfer the trained network from the big sample voiceprint dataset to our limited voiceprint dataset for the further training. Fully-connected layers of a pre-training model are replaced by restricted Boltzmann machine layers. Secondly, the approach of Data Augmentation is adopted to increase the number of voiceprint datasets. Finally, we introduce fast batch normalization algorithms to improve the speed of the network convergence and shorten the training time. Our new voiceprint recognition approach uses the TLCNN-RBM (convolutional neural network mixed restricted Boltzmann machine based on transfer learning) model, which is the deep migration hybrid model that is used to achieve an average accuracy of over 97%, which is higher than that when using either CNN or the TL-CNN network (convolutional neural network based on transfer learning). Thus, an effective method for a small sample of voiceprint recognition has been provided.