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Biology subjects

Chen, j.

Publications and source records attributed to Chen, j..

2 recordsLinked to original sources

Exposure to titanium dioxide nanoparticles accelerates abnormal fat deposition through the mediation of the ROS via the AGE-RAGE signaling pathway in mice

Titanium dioxide nanoparticles (TiO2 NPs) are widely added to various types of foods as food additives. Previous studies have shown that TiO2 NPs exposure can cause abnormal deposition of adipose tissue in organisms, resulting in lipid metabolism disorder. However, the potential molecular mechanisms underlying TiO2 NPs effects have yet to be elucidated. In this study, our data indicated that TiO2 NPs (100 mg/Kg BW, 20 nm) accelerated abnormal fat deposition in the epididymal adipose tissues and disturbed the level of blood glucose subsequently in normal-fat diet mice. Further studies showed that TiO2 NPs at a concentration of 100 {micro}g/mL significantly induced the proliferation and differentiation of 3T3-L1 preadipocytes. Mechanistic studies we revealed that TiO2 NPs induced the overproduction of reactive oxygen species (ROS) in the cytoplasm, which subsequently upregulated the expression of receptors for advanced glycation end products (AGEs) leading to lipid accumulation in the adipocytes with a higher level of ROS. However, the antioxidant N-acetylcysteine (NAC) was a therapeutic potential for lipid overaccumulation in the adipocytes. This study provides insight into the mechanism underlying fat deposition induced by TiO2 NPs and highlighted the need for reevaluation of food-grade TiO2 NPs exposure in daily life.

cell biology↗

DeepGWAS: Enhance GWAS Signals for Neuropsychiatric Disorders via Deep Neural Network

Genetic dissection of neuropsychiatric disorders can potentially reveal novel therapeutic targets. While genome-wide association studies (GWAS) have tremendously advanced our understanding, we approach a sample size bottleneck (i.e., the number of cases needed to identify >90% of all loci is impractical). Therefore, computationally enhancing GWAS on existing samples may be particularly valuable. Here, we describe DeepGWAS, a deep neural network-based method to enhance GWAS by integrating GWAS results with linkage disequilibrium and brain-related functional annotations. DeepGWAS enhanced schizophrenia (SCZ) loci by [~]3X when applied to the largest European GWAS, and 21.3% enhanced loci were validated by the latest multi-ancestry GWAS. Importantly, DeepGWAS models can be transferred to other neuropsychiatric disorders. Transferring SCZ-trained models to Alzheimers disease and major depressive disorder, we observed 1.3-17.6X detected loci compared to standard GWAS, among which 27-40% were validated by other GWAS studies. We anticipate DeepGWAS to be a powerful tool in GWAS studies.

genetics↗