Zinc oxide nanoparticles (ZnO NPs) have been widely used in various materials including sunscreens, cosmetics, over-the-counter topical skin products, and pigments. As traces of the used ZnO NPs have been found in the kidney, it is crucial to uncover their potential risks. The aim of this study is to elucidate detrimental effects of ZnO NPs and the molecular mechanism behind their renal toxicity. Cytotoxic effects were measured by MTT assay after HK2 cells were exposed to ZnO NPs for 24 h and IC₅₀ value was determined. ROS and intracellular Zn²⁺ levels were detected by flow cytometry, and localization of Zn²⁺ and lysosome was determined by confocal microscopy. Occurrence of autophagy and detection of autophagic flux were determined by Western blot and confocal microscopy, respectively. We performed unpaired student t test for two groups, and one-way ANOVA with Tukey’s post hoc for over three groups. ZnO NPs induced cell death in human renal proximal tubule epithelial cells, HK2. Cytosolic Zn²⁺ caused autophagy-mediated cell death rather than apoptosis. Cytosolic Zn²⁺ processed in lysosome was released by TRPML1, and inhibition of TRPML1 significantly decreased autophagic flux and cell death. The findings of this study suggest that ZnO NPs strongly induce autophagy-mediated cell death in human kidney cells. Controlling TRPML1 can be potentially used to prevent the kidney from ZnO NPs-induced toxicity.

Background: The lysosome has emerged as a promising target for overcoming chemoresistance, owing to its role in facilitating the lysosomal sequestration of drugs. The lysosomal calcium channel TRPML1 not only influences lysosomal biogenesis but also coordinates both endocytosis and exocytosis. This study explored the modulation of cisplatin sensitivity by regulating TRPML1-mediated lysosomal exocytosis and identified the metabolomic profile altered by TRPML1 inhibition. Methods: We used four types of ovarian cancer cells: two cancer cell lines (OVCAR8 and TOV21G) and two patient-derived ovarian cancer cells. Metabolomic analyses were conducted to identify altered metabolites by TRPML1 inhibition. Results: Lysosomal exocytosis in response to cisplatin was observed in resistant cancer cells, whereas the phenomenon was absent in sensitive cancer cells. Through the pharmacological intervention of TRPML1, lysosomal exocytosis was interrupted, leading to the sensitization of resistant cancer cells to cisplatin treatment. To assess the impact of lysosomal exocytosis on chemoresistance, we conducted an untargeted metabolomic analysis on cisplatin-resistant ovarian cancer cells with TRPML1 inhibition. Among the 1446 differentially identified metabolites, we focused on 84 significant metabolites. Metabolite set analysis revealed their involvement in diverse pathways. Conclusions: These findings collectively have the potential to enhance our understanding of the interplay between lysosomal exocytosis and chemoresistance, providing valuable insights for the development of innovative therapeutic strategies.

Zinc oxide nanoparticles (ZnO NPs) are widely used in versatile applications, from high technology to household products. While numerous studies have examined the toxic gene profile of ZnO NPs across various tissues, the specific lipid species associated with adverse effects and potential biomarkers remain elusive. In this study, we conducted a liquid chromatography-mass spectrometry based lipidomics analysis to uncover potential lipid biomarkers in human kidney cells following treatment with ZnO NPs. Furthermore, we employed lipid pathway enrichment analysis (LIPEA) to elucidate altered lipid-related signaling pathways. Our results demonstrate that ZnO NPs induce cytotoxicity in renal epithelial cells and modulate lipid species; we identified 64 lipids with a fold change (FC) > 2 and p < 0.01 with corrected p < 0.05 in HK2 cells post-treatment with ZnO NPs. Notably, the altered lipids between control HK2 cells and those treated with ZnO NPs were associated with the sphingolipid, autophagy, and glycerophospholipid pathways. This study unveils novel potential lipid biomarkers of ZnO NP nanotoxicity, representing the first lipidomic profiling of ZnO NPs in human renal epithelial cells.

Mobile communication technology is evolving rapidly and becoming increasingly ubiquitous, thereby increasing the demand for uplink data-intensive applications (e.g., personal broadcasting and live augmented/virtual reality videos). Recently, to facilitate a cost-effective and smooth transition from 4G to 5G networks, most carriers leverage existing 4G infrastructures using a dual connectivity (DC) feature. DC increases uplink throughput and mobility robustness; however, it also causes unprecedented dynamic fluctuations in radio channels due to the coverage discrepancy between 4G and 5G networks. Thus, in this article, we propose a self-attention-based deep learning model to predict uplink radio resources in 5G DC. We trained the proposed model on commercial 5G DC traffic data from three major carriers in South Korea and obtained an average prediction accuracy of 95.08% under various mobility and cell-load conditions. The proposed model explains the rationale for the obtained predictions by highlighting the parts of the input time-series data that are important to realize accurate prediction. We also demonstrate the usability of the proposed model using a network emulator based on real-world 5G trace data. Extensive evaluations demonstrate that the existing congestion control algorithms can achieve excellent performance when used with the proposed model.

Pancreatic cancer is characterized by its high mortality rate and limited treatment options, often driven by oncogenic RAS mutations. In this study, we investigated the metabolomic profiles of pancreatic cancer cells based on their KRAS genetic status. Utilizing both KRAS-wildtype BxPC3 and KRAS-mutant PANC1 cell lines, we identified 195 metabolites differentially altered by KRAS status through untargeted metabolomics. Principal component analysis and hierarchical condition trees revealed distinct separation between KRAS-wildtype and KRAS-mutant cells. Metabolite set enrichment analysis highlighted significant pathways such as homocysteine degradation and taurine and hypotaurine metabolism. Additionally, lipid enrichment analysis identified pathways including fatty acyl glycosides and sphingoid bases. Mapping of identified metabolites to KEGG pathways identified nine significant metabolic pathways associated with KRAS status, indicating diverse metabolic alterations in pancreatic cancer cells. Furthermore, we explored the impact of TRPML1 inhibition on the metabolomic profile of KRAS-mutant pancreatic cancer cells. TRPML1 inhibition using ML-SI1 significantly altered the metabolomic profile, leading to distinct separation between vehicle-treated and ML-SI1-treated PANC1 cells. Metabolite set enrichment analysis revealed enriched pathways such as arginine and proline metabolism, and mapping to KEGG pathways identified 17 significant metabolic pathways associated with TRPML1 inhibition. Interestingly, some metabolites identified in PANC1 compared to BxPC3 were oppositely regulated by TRPML1 inhibition, suggesting their potential as biomarkers for KRAS-mutant cancer cells. Overall, our findings shed light on the distinct metabolite changes induced by both KRAS status and TRPML1 inhibition in pancreatic cancer cells, providing insights into potential therapeutic targets and biomarkers for this deadly disease.