RESUMO
Congenital heart disease (CHD) is the most common class of human birth defects, with a prevalence of 0.9% of births. However, two-thirds of cases have an unknown cause, and many of these are thought to be caused by in utero exposure to environmental teratogens. Here we identify a potential teratogen causing CHD in mice: maternal iron deficiency (ID). We show that maternal ID in mice causes severe cardiovascular defects in the offspring. These defects likely arise from increased retinoic acid signalling in ID embryos. The defects can be prevented by iron administration in early pregnancy. It has also been proposed that teratogen exposure may potentiate the effects of genetic predisposition to CHD through gene-environment interaction. Here we show that maternal ID increases the severity of heart and craniofacial defects in a mouse model of Down syndrome. It will be important to understand if the effects of maternal ID seen here in mice may have clinical implications for women.
Assuntos
Sistema Cardiovascular/embriologia , Embrião de Mamíferos/patologia , Deficiências de Ferro , Animais , Aorta Torácica/anormalidades , Biomarcadores/metabolismo , Diferenciação Celular , Vasos Coronários/embriologia , Vasos Coronários/patologia , Suplementos Nutricionais , Edema/patologia , Embrião de Mamíferos/anormalidades , Desenvolvimento Embrionário , Feminino , Perfilação da Expressão Gênica , Interação Gene-Ambiente , Proteínas de Fluorescência Verde/metabolismo , Ferro/metabolismo , Vasos Linfáticos/embriologia , Vasos Linfáticos/patologia , Camundongos Endogâmicos C57BL , Miocárdio/patologia , Miócitos Cardíacos/metabolismo , Miócitos Cardíacos/patologia , Penetrância , Fenótipo , Gravidez , Transdução de Sinais , Células-Tronco/patologia , Transgenes , Tretinoína/metabolismoRESUMO
Electron microscopy (EM) enables high-resolution visualization of protein distributions in biological tissues. For detection, gold nanoparticles are typically used as an electron-dense marker for immunohistochemically labeled proteins. Manual annotation of gold particle labels is laborious and time consuming, as gold particle counts can exceed 100,000 across hundreds of image segments to obtain conclusive data sets. To automate this process, we developed Gold Digger, a software tool that uses a modified pix2pix deep learning network capable of detecting and annotating colloidal gold particles in biological EM images obtained from both freeze-fracture replicas and plastic sections prepared with the post-embedding method. Gold Digger performs at near-human-level accuracy, can handle large images, and includes a user-friendly tool with a graphical interface for proof reading outputs by users. Manual error correction also helps for continued re-training of the network to improve annotation accuracy over time. Gold Digger thus enables rapid high-throughput analysis of immunogold-labeled EM data and is freely available to the research community.
