Exposure to N-Ethyl-N-Nitrosourea in Adult Mice Alters Structural and Functional Integrity of Neurogenic Sites

BACKGROUND: Previous studies have shown that prenatal exposure to the mutagen N-ethyl-N-nitrosourea (ENU), a N-nitroso compound (NOC) found in the environment, disrupts developmental neurogenesis and alters memory formation. Previously, we showed that postnatal ENU treatment induced lasting deficits in proliferation of neural progenitors in the subventricular zone (SVZ), the main neurogenic region in the adult mouse brain. The present study is aimed to examine, in mice exposed to ENU, both the structural features of adult neurogenic sites, incorporating the dentate gyrus (DG), and the behavioral performance in tasks sensitive to manipulations of adult neurogenesis. METHODOLOGY/PRINCIPAL FINDINGS: 2-month old mice received 5 doses of ENU and were sacrificed 45 days after treatment. Then, an ultrastructural analysis of the SVZ and DG was performed to determine cellular composition in these regions, confirming a significant alteration. After bromodeoxyuridine injections, an S-phase exogenous marker, the immunohistochemical analysis revealed a deficit in proliferation and a decreased recruitment of newly generated cells in neurogenic areas of ENU-treated animals. Behavioral effects were also detected after ENU-exposure, observing impairment in odor discrimination task (habituation-dishabituation test) and a deficit in spatial memory (Barnes maze performance), two functions primarily related to the SVZ and the DG regions, respectively. CONCLUSIONS/SIGNIFICANCE: The results demonstrate that postnatal exposure to ENU produces severe disruption of adult neurogenesis in the SVZ and DG, as well as strong behavioral impairments. These findings highlight the potential risk of environmental NOC-exposure for the development of neural and behavioral deficits

Features and strategies of ENU mouse mutagenesis.

Aim of this review is to demonstrate the relevance of animal models created by ENU mutagenesis for the pharmaceutical community to understand diseases and the modulation of disease status by pharmaceutical compounds. We give an overview of what ENU mutagenesis in mice implies and introduce the main research centers running ENU mutagenesis projects. The different strategies of ENU mutagenesis screens are explained as well as the latest advances in mapping and mutation detection strategies, which until recently have been the main limiting step in forward genetics/phenotype- driven approaches. ENU mutagenesis in mice has shown its power by providing animal models for human monogenic diseases. Moreover, the development of modifier and sensitized screens extended this resource to models for multigenic diseases and thereby opened the perspective to understand the modulation of disease states. Finally, we provide information about the accessibility and availability of these models for academic research

Random ENU Mutagenesis.

Mouse models play an important role in the elucidation of molecular pathways underlying human disease. Mutations in mouse can be generated by a variety of techniques including those using inducing agents such ionizing radiation or chemicals and those that involve genetic manipulations such as transgenic insertions or knockouts. Random mutagenesis by ionizing radiation or chemical agents has a long tradition in classical genetics and has allowed the generation of a large number of mutant phenotypes. Ionizing radiation causes breaks in the chromosome, leading to deletions, translocations, and other gross chromosomal rearrangements. Chemical mutagens, which have been shown to produce a large number of mutations, are characterised by a differential spermatogenic response

Deep Learning and Phenology Enhance Large-Scale Tree Species Classification in Aerial Imagery during a Biosecurity Response

The ability of deep convolutional neural networks (deep learning) to learn complex visual characteristics offers a new method to classify tree species using lower-cost data such as regional aerial RGB imagery. In this study, we use 10 cm resolution imagery and 4600 trees to develop a deep learning model to identify Metrosideros excelsa (pōhutukawa)—a culturally important New Zealand tree that displays distinctive red flowers during summer and is under threat from the invasive pathogen Austropuccinia psidii (myrtle rust). Our objectives were to compare the accuracy of deep learning models that could learn the distinctive visual characteristics of the canopies with tree-based models (XGBoost) that used spectral and textural metrics. We tested whether the phenology of pōhutukawa could be used to enhance classification by using multitemporal aerial imagery that showed the same trees with and without widespread flowering. The XGBoost model achieved an accuracy of 86.7% on the dataset with strong phenology (flowering). Without phenology, the accuracy fell to 79.4% and the model relied on the blueish hue and texture of the canopies. The deep learning model achieved 97.4% accuracy with 96.5% sensitivity and 98.3% specificity when leveraging phenology—even though the intensity of flowering varied substantially. Without strong phenology, the accuracy of the deep learning model remained high at 92.7% with sensitivity of 91.2% and specificity of 94.3% despite significant variation in the appearance of non-flowering pōhutukawa. Pooling time-series imagery did not enhance either approach. The accuracy of XGBoost and deep learning models were, respectively, 83.2% and 95.2%, which were of intermediate precision between the separate models