ATP5PO levels regulate enteric nervous system development in zebrafish, linking Hirschsprung disease to Down Syndrome.

Hirschsprung disease (HSCR) is a complex genetic disorder characterized by the absence of enteric nervous system (ENS) in the distal region of the intestine. Down Syndrome (DS) patients have a >50-fold higher risk of developing HSCR than the general population, suggesting that overexpression of human chromosome 21 (Hsa21) genes contribute to HSCR etiology. However, identification of responsible genes remains challenging. Here, we describe a genetic screening of potential candidate genes located on Hsa21, using the zebrafish. Candidate genes were located in the DS-HSCR susceptibility region, expressed in the human intestine, were known potential biomarkers for DS prenatal diagnosis, and were present in the zebrafish genome. With this approach, four genes were selected: RCAN1, ITSN1, ATP5PO and SUMO3. However, only overexpression of ATP5PO, coding for a component of the mitochondrial ATPase, led to significant reduction of ENS cells. Paradoxically, in vitro studies showed that overexpression of ATP5PO led to a reduction of ATP5PO protein levels. Impaired neuronal differentiation and reduced mitochondrial ATP production, were also detected in vitro, after overexpression of ATP5PO in a neuroblastoma cell line. Finally, epistasis was observed between ATP5PO and ret, the most important HSCR gene. Taken together, our results identify ATP5PO as the gene responsible for the increased risk of HSCR in DS patients in particular if RET variants are also present, and show that a balanced expression of ATP5PO is required for normal ENS development.

Artificial neural networks for adolescent marijuana use and clinical features of marijuana dependence.

This article compares the performance of multiple logistic regression (MLR) with feed-forward, artificial neural network (ANN) models for the assessment of adolescent marijuana use and clinical features of dependence based on self-evaluation from recent National Household Surveys on Drug Abuse (NHSDA). The effect of training and testing the neural networks with randomly selected data was compared to data selected as a function of survey year. The technical aim of the study was to account for adolescent marijuana use and features of marijuana dependence based on experiences with alcohol and tobacco. Similarities observed in MLR and ANN model performance may indicate no major complex or nonlinear relationships in cross-sectional epidemiological data selected to model adolescent drug use and dependence in this specific application. We concluded that ANNs should be further studied in future longitudinal research, perhaps with modeling of recursive networks, allowing feedback from drug dependence to levels of marijuana use. The ANN models also have the potential to model drug use and dependence based on input parameters with no obvious direct link to drug involvement--e.g., polymorphisms associated with "openness to experience" or other personality traits hypothesized to function as distal antecedents, and could thus be implemented to identify higher risk youths using assessments indirectly related or nonlinearly associated to adolescent drug use and dependence but less sensitive to survey-related response tendencies.