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      Identification of chemicals that mimic transcriptional changes associated with autism, brain aging and neurodegeneration

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          Abstract

          Environmental factors, including pesticides, have been linked to autism and neurodegeneration risk using retrospective epidemiological studies. Here we sought to prospectively identify chemicals that share transcriptomic signatures with neurological disorders, by exposing mouse cortical neuron-enriched cultures to hundreds of chemicals commonly found in the environment and on food. We find that rotenone, a pesticide associated with Parkinson's disease risk, and certain fungicides, including pyraclostrobin, trifloxystrobin, famoxadone and fenamidone, produce transcriptional changes in vitro that are similar to those seen in brain samples from humans with autism, advanced age and neurodegeneration (Alzheimer's disease and Huntington's disease). These chemicals stimulate free radical production and disrupt microtubules in neurons, effects that can be reduced by pretreating with a microtubule stabilizer, an antioxidant, or with sulforaphane. Our study provides an approach to prospectively identify environmental chemicals that transcriptionally mimic autism and other brain disorders.

          Abstract

          This study presents gene expression responses of cultured brain cells to hundreds of chemicals found in the environment and in food. The authors identified chemicals that induce transcriptomic profiles that overlap those seen in human brains affected with autism, aging, and neurodegeneration.

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          Most cited references63

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          Differential expression analysis for sequence count data

          High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. We propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, DESeq, as an R/Bioconductor package.
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            Silhouettes: A graphical aid to the interpretation and validation of cluster analysis

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              TopHat: discovering splice junctions with RNA-Seq

              Motivation: A new protocol for sequencing the messenger RNA in a cell, known as RNA-Seq, generates millions of short sequence fragments in a single run. These fragments, or ‘reads’, can be used to measure levels of gene expression and to identify novel splice variants of genes. However, current software for aligning RNA-Seq data to a genome relies on known splice junctions and cannot identify novel ones. TopHat is an efficient read-mapping algorithm designed to align reads from an RNA-Seq experiment to a reference genome without relying on known splice sites. Results: We mapped the RNA-Seq reads from a recent mammalian RNA-Seq experiment and recovered more than 72% of the splice junctions reported by the annotation-based software from that study, along with nearly 20 000 previously unreported junctions. The TopHat pipeline is much faster than previous systems, mapping nearly 2.2 million reads per CPU hour, which is sufficient to process an entire RNA-Seq experiment in less than a day on a standard desktop computer. We describe several challenges unique to ab initio splice site discovery from RNA-Seq reads that will require further algorithm development. Availability: TopHat is free, open-source software available from http://tophat.cbcb.umd.edu Contact: cole@cs.umd.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group
                2041-1723
                31 March 2016
                2016
                : 7
                : 11173
                Affiliations
                [1 ]Department of Cell Biology and Physiology, UNC Neuroscience Center, University of North Carolina at Chapel Hill , 111 Mason Farm Road, Chapel Hill, North Carolina 27599-7545, USA
                [2 ]Carolina Institute for Developmental Disabilities, University of North Carolina Chapel Hill , Chapel Hill, North Carolina 27599-7255, USA
                Author notes
                [*]

                These authors contributed equally to this work.

                Author information
                http://orcid.org/0000-0003-3906-1663
                Article
                ncomms11173
                10.1038/ncomms11173
                4821887
                27029645
                d99f8d60-0ef6-4c33-8c0b-1ba7624dc475
                Copyright © 2016, Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                : 01 December 2015
                : 29 February 2016
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