Hydroxylation Increases the Neurotoxic Potential of BDE-47 to Affect Exocytosis and Calcium Homeostasis in PC12 Cells

BACKGROUND: Oxidative metabolism, resulting in the formation of hydroxylated polybrominated diphenyl ether (PBDE) metabolites, may enhance the neurotoxic potential of brominated flame retardants. OBJECTIVE: Our objective was to investigate the effects of a hydroxylated metabolite of 2,2',4,4'-tetra-bromodiphenyl ether (BDE-47; 6-OH-BDE-47) on changes in the intracellular Ca2+ concentration ([Ca2+]i) and vesicular catecholamine release in PC12 cells. METHODS: We measured vesicular catecholamine release and [Ca2+]i using amperometry and imaging of the fluorescent Ca2+-sensitive dye Fura-2, respectively. RESULTS: Acute exposure of PC12 cells to 6-OH-BDE-47 (5 microM) induced vesicular catecholamine release. Catecholamine release coincided with a transient increase in [Ca2+]i, which was observed shortly after the onset of exposure to 6-OH-BDE-47 (120 microM). An additional late increase in [Ca2+]i was often observed at > or =1 microM 6-OH-BDE-47. The initial transient increase was absent in cells exposed to the parent compound BDE-47, whereas the late increase was observed only at 20 microM. Using the mitochondrial uncoupler carbonyl cyanide 4-(trifluoromethoxy)phenylhydrazone (FCCP) and thapsigargin to empty intracellular Ca2+ stores, we found that the initial increase originates from emptying of the endoplasmic reticulum and consequent influx of extracellular Ca2+, whereas the late increase originates primarily from mitochondria. CONCLUSION: The hydroxylated metabolite 6-OH-BDE-47 is more potent in disturbing Ca2+ homeostasis and neurotransmitter release than the parent compound BDE-47. The present findings indicate that bioactivation by oxidative metabolism adds considerably to the neurotoxic potential of PBDEs. Additionally, based on the observed mechanism of action, a cumulative neurotoxic effect of PBDEs and ortho-substituted polychlorinated biphenyls on [Ca2+]i cannot be ruled out

PhenoScore quantifies phenotypic variation for rare genetic diseases by combining facial analysis with other clinical features using a machine-learning framework

Several molecular and phenotypic algorithms exist that establish genotype-phenotype correlations, including facial recognition tools. However, no unified framework that investigates both facial data and other phenotypic data directly from individuals exists. We developed PhenoScore: an open-source, artificial intelligence-based phenomics framework, combining facial recognition technology with Human Phenotype Ontology data analysis to quantify phenotypic similarity. Here we show PhenoScore's ability to recognize distinct phenotypic entities by establishing recognizable phenotypes for 37 of 40 investigated syndromes against clinical features observed in individuals with other neurodevelopmental disorders and show it is an improvement on existing approaches. PhenoScore provides predictions for individuals with variants of unknown significance and enables sophisticated genotype-phenotype studies by testing hypotheses on possible phenotypic (sub)groups. PhenoScore confirmed previously known phenotypic subgroups caused by variants in the same gene for SATB1, SETBP1 and DEAF1 and provides objective clinical evidence for two distinct ADNP-related phenotypes, already established functionally.PhenoScore is an open-source machine-learning tool that combines facial image recognition with Human Phenotype Ontology for genetic syndrome identification without genomic data, with applications to subgroup analysis and variants of unknown significance classification.Genetics of disease, diagnosis and treatmen