Data Descriptor: An open resource for transdiagnostic research in pediatric mental health and learning disorders

Technological and methodological innovations are equipping researchers with unprecedented capabilities for detecting and characterizing pathologic processes in the developing human brain. As a result, ambitions to achieve clinically useful tools to assist in the diagnosis and management of mental health and learning disorders are gaining momentum. To this end, it is critical to accrue large-scale multimodal datasets that capture a broad range of commonly encountered clinical psychopathology. The Child Mind Institute has launched the Healthy Brain Network (HBN), an ongoing initiative focused on creating and sharing a biobank of data from 10,000 New York area participants (ages 5–21). The HBN Biobank houses data about psychiatric, behavioral, cognitive, and lifestyle phenotypes, as well as multimodal brain imaging (resting and naturalistic viewing fMRI, diffusion MRI, morphometric MRI), electroencephalography, eyetracking, voice and video recordings, genetics and actigraphy. Here, we present the rationale, design and implementation of HBN protocols. We describe the first data release (n =664) and the potential of the biobank to advance related areas (e.g., biophysical modeling, voice analysis

α-amanitin resistance in Drosophila melanogaster: A genome-wide association approach

© This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. We investigated the mechanisms of mushroom toxin resistance in the Drosophila Genetic Reference Panel (DGRP) fly lines, using genome-wide association studies (GWAS). While Drosophila melanogaster avoids mushrooms in nature, some lines are surprisingly resistant to α-amanitin-a toxin found solely in mushrooms. This resistance may represent a preadaptation, which might enable this species to invade the mushroom niche in the future. Although our previous microarray study had strongly suggested that pesticide-metabolizing detoxification genes confer α-amanitin resistance in a Taiwanese D. melanogaster line Ama-KTT, none of the traditional detoxification genes were among the top candidate genes resulting from the GWAS in the current study. Instead, we identified Megalin, Tequila, and widerborst as candidate genes underlying the α-amanitin resistance phenotype in the North American DGRP lines, all three of which are connected to the Target of Rapamycin (TOR) pathway. Both widerborst and Tequila are upstream regulators of TOR, and TOR is a key regulator of autophagy and Megalin-mediated endocytosis. We suggest that endocytosis and autophagy of α-amanitin, followed by lysosomal degradation of the toxin, is one of the mechanisms that confer α-amanitin resistance in the DGRP lines

Larval viability variation in the DGRP lines in response to α-amanitin.

<p>The y-axis shows individual viability values, while the x-axis represents the individual DGRP lines. The lines are sorted from lowest α-amanitin resistance (left) to highest α-amanitin resistance (right). The error bars represent the standard error of the mean (SEM). A) 180 lines tested on 0.2 μg/g α-amanitin. (Individual line numbers are not shown but can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0173162#pone.0173162.s001" target="_blank">S1 Table</a>). The y-axis represents the average number of flies hatched from 10 larvae placed on toxic food. B) 180 lines tested on 2.0 μg/g α-amanitin. (Individual line numbers are not shown but can be can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0173162#pone.0173162.s001" target="_blank">S1 Table</a>). The y-axis represents the average hatch counts out of 10 larvae placed on toxic food. C). The y-axis represents the LC₅₀ values of the 37-line subset. The line numbers are shown on the x-axis.</p

Candidate genes resulting from the 180-line GWAS on 2.0 μg/g and the 37-line GWAS.

<p>Candidate genes resulting from the 180-line GWAS on 2.0 μg/g and the 37-line GWAS.</p

Manhattan plots for the three GWAS.

<p>A) 37-line GWAS using LC₅₀ values, B) 180-line GWAS on 2.0 μg/g α-amanitin, C) 180-line GWAS on 0.2 μg/g α-amanitin. Selected significant gene names are printed on the top right of the corresponding dots in the graphs.</p

An open resource for transdiagnostic research in pediatric mental health and learning disorders

AbstractTechnological and methodological innovations are equipping researchers with unprecedented capabilities for detecting and characterizing pathologic processes in the developing human brain. As a result, ambitions to achieve clinically useful tools to assist in the diagnosis and management of mental health and learning disorders are gaining momentum. To this end, it is critical to accrue large-scale multimodal datasets that capture a broad range of commonly encountered clinical psychopathology. The Child Mind Institute has launched the Healthy Brain Network (HBN), an ongoing initiative focused on creating and sharing a biobank of data from 10,000 New York area participants (ages 5–21). The HBN Biobank houses data about psychiatric, behavioral, cognitive, and lifestyle phenotypes, as well as multimodal brain imaging (resting and naturalistic viewing fMRI, diffusion MRI, morphometric MRI), electroencephalography, eye-tracking, voice and video recordings, genetics and actigraphy. Here, we present the rationale, design and implementation of HBN protocols. We describe the first data release (n=664) and the potential of the biobank to advance related areas (e.g., biophysical modeling, voice analysis).</jats:p