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      Developmental Exposure to PCB153 (2,2’,4,4’,5,5’-Hexachlorobiphenyl) Alters Circadian Rhythms and the Expression of Clock and Metabolic Genes

      1 , 1 , 1 , 2 , 1
      Toxicological Sciences
      Oxford University Press (OUP)

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          Abstract

          Polychlorinated biphenyls (PCBs) are highly persistent and ubiquitously distributed environmental pollutants. Based on their chemical structure, PCBs are classified into non-ortho-substituted and ortho-substituted congeners. Non-ortho-substituted PCBs are structurally similar to dioxin and their toxic effects and mode of action are well-established. In contrast, very little is known about the effects of ortho-substituted PCBs, particularly, during early development. The objective of this study is to investigate the effects of exposure to an environmentally prominent ortho-substituted PCB (2,2’,4,4’,5,5’-hexachlorobiphenyl; PCB153) on zebrafish embryos. We exposed zebrafish embryos to 3 different concentrations of PCB153 starting from 4 to 120 hours post-fertilization (hpf). We quantified gross morphological changes, behavioral phenotypes, gene expression changes, and circadian behavior in the larvae. There were no developmental defects during the exposure period, but starting at 7 dpf, we observed spinal deformity in the 10 μM PCB153 treated group. A total of 633, 2227, and 3378 differentially expressed genes were observed in 0.1 μM (0.036 μg/ml), 1 μM (0.36 μg/ml), and 10 μM (3.6 μg/ml) PCB153-treated embryos, respectively. Of these, 301 genes were common to all treatment groups. KEGG pathway analysis revealed enrichment of genes related to circadian rhythm, FoxO signaling, and insulin resistance pathways. Behavioral analysis revealed that PCB153 exposure significantly alters circadian behavior. Disruption of circadian rhythms has been associated with the development of metabolic and neurological diseases. Thus, understanding the mechanisms of action of environmental chemicals in disrupting metabolism and other physiological processes is essential.

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          Most cited references69

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          Is Open Access

          edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

          Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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            Is Open Access

            HTSeq—a Python framework to work with high-throughput sequencing data

            Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability and implementation: HTSeq is released as an open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq. Contact: sanders@fs.tum.de
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              RSeQC: quality control of RNA-seq experiments.

              RNA-seq has been extensively used for transcriptome study. Quality control (QC) is critical to ensure that RNA-seq data are of high quality and suitable for subsequent analyses. However, QC is a time-consuming and complex task, due to the massive size and versatile nature of RNA-seq data. Therefore, a convenient and comprehensive QC tool to assess RNA-seq quality is sorely needed. We developed the RSeQC package to comprehensively evaluate different aspects of RNA-seq experiments, such as sequence quality, GC bias, polymerase chain reaction bias, nucleotide composition bias, sequencing depth, strand specificity, coverage uniformity and read distribution over the genome structure. RSeQC takes both SAM and BAM files as input, which can be produced by most RNA-seq mapping tools as well as BED files, which are widely used for gene models. Most modules in RSeQC take advantage of R scripts for visualization, and they are notably efficient in dealing with large BAM/SAM files containing hundreds of millions of alignments. RSeQC is written in Python and C. Source code and a comprehensive user's manual are freely available at: http://code.google.com/p/rseqc/.
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                Author and article information

                Journal
                Toxicological Sciences
                Oxford University Press (OUP)
                1096-6080
                1096-0929
                January 2020
                January 01 2020
                October 17 2019
                January 2020
                January 01 2020
                October 17 2019
                : 173
                : 1
                : 41-52
                Affiliations
                [1 ]Biology Department, Woods Hole Center for Oceans and Human Health, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts 02543
                [2 ]Biology Department, Spelman College, Atlanta, Georgia 30314
                Article
                10.1093/toxsci/kfz217
                6944218
                31621872
                5ab348c6-152e-4c04-a516-351f78ee4f68
                © 2019

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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