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      Integrated transcriptomics and metabolomics reveal signatures of lipid metabolism dysregulation in HepaRG liver cells exposed to PCB 126

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          Abstract

          Chemical pollutant exposure is a risk factor contributing to the growing epidemic of non-alcoholic fatty liver disease (NAFLD) affecting human populations that consume a western diet. Although it is recognized that intoxication by chemical pollutants can lead to NAFLD, there is limited information available regarding the mechanism by which typical environmental levels of exposure can contribute to the onset of this disease. Here, we describe the alterations in gene expression profiles and metabolite levels in the human HepaRG liver cell line, a validated model for cellular steatosis, exposed to the polychlorinated biphenyl (PCB) 126, one of the most potent chemical pollutants that can induce NAFLD. Sparse partial least squares classification of the molecular profiles revealed that exposure to PCB 126 provoked a decrease in polyunsaturated fatty acids as well as an increase in sphingolipid levels, concomitant with a decrease in the activity of genes involved in lipid metabolism. This was associated with an increased oxidative stress reflected by marked disturbances in taurine metabolism. A gene ontology analysis showed hallmarks of an activation of the AhR receptor by dioxin-like compounds. These changes in metabolome and transcriptome profiles were observed even at the lowest concentration (100 pM) of PCB 126 tested. A decrease in docosatrienoate levels was the most sensitive biomarker. Overall, our integrated multi-omics analysis provides mechanistic insight into how this class of chemical pollutant can cause NAFLD. Our study lays the foundation for the development of molecular signatures of toxic effects of chemicals causing fatty liver diseases to move away from a chemical risk assessment based on in vivo animal experiments.

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          The online version of this article (10.1007/s00204-018-2235-7) contains supplementary material, which is available to authorized users.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Toxicology for the twenty-first century.

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              Filtering for increased power for microarray data analysis

              Background Due to the large number of hypothesis tests performed during the process of routine analysis of microarray data, a multiple testing adjustment is certainly warranted. However, when the number of tests is very large and the proportion of differentially expressed genes is relatively low, the use of a multiple testing adjustment can result in very low power to detect those genes which are truly differentially expressed. Filtering allows for a reduction in the number of tests and a corresponding increase in power. Common filtering methods include filtering by variance, average signal or MAS detection call (for Affymetrix arrays). We study the effects of filtering in combination with the Benjamini-Hochberg method for false discovery rate control and q-value for false discovery rate estimation. Results Three case studies are used to compare three different filtering methods in combination with the two false discovery rate methods and three different preprocessing methods. For the case studies considered, filtering by detection call and variance (on the original scale) consistently led to an increase in the number of differentially expressed genes identified. On the other hand, filtering by variance on the log2 scale had a detrimental effect when paired with MAS5 or PLIER preprocessing methods, even when the testing was done on the log2 scale. A simulation study was done to further examine the effect of filtering by variance. We find that filtering by variance leads to higher power, often with a decrease in false discovery rate, when paired with either of the false discovery rate methods considered. This holds regardless of the proportion of genes which are differentially expressed or whether we assume dependence or independence among genes. Conclusion The case studies show that both detection call and variance filtering are viable methods of filtering which can increase the number of differentially expressed genes identified. The simulation study demonstrates that when paired with a false discovery rate method, filtering by variance can increase power while still controlling the false discovery rate. Filtering out 50% of probe sets seems reasonable as long as the majority of genes are not expected to be differentially expressed.
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                Author and article information

                Contributors
                michael.antoniou@kcl.ac.uk
                Journal
                Arch Toxicol
                Arch. Toxicol
                Archives of Toxicology
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                0340-5761
                1432-0738
                14 June 2018
                14 June 2018
                2018
                : 92
                : 8
                : 2533-2547
                Affiliations
                [1 ]ISNI 0000 0001 2322 6764, GRID grid.13097.3c, Gene Expression and Therapy Group, King’s College London, Faculty of Life Sciences and Medicine, Department of Medical and Molecular Genetics, 8th Floor, Tower Wing, Guy’s Hospital, ; Great Maze Pond, London, SE1 9RT UK
                [2 ]ISNI 0000 0001 2322 6764, GRID grid.13097.3c, Genomics Centre, , King’s College London, ; Waterloo Campus, 150 Stamford Street, London, SE1 9NH UK
                [3 ]GRID grid.420267.5, INRA UMR1331, Toxalim, Research Centre in Food Toxicology, ; Toulouse, France
                [4 ]ISNI 0000 0001 2171 1133, GRID grid.4868.2, Genome Centre, , Barts and the London School of Medicine and Dentistry, John Vane Science Centre, ; London, EC1M 6BQ UK
                Author information
                http://orcid.org/0000-0003-1732-4741
                http://orcid.org/0000-0002-3185-5314
                Article
                2235
                10.1007/s00204-018-2235-7
                6063328
                29947894
                78d46df6-6ddb-4282-b778-522fdb3e6bc1
                © The Author(s) 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 8 February 2018
                : 4 June 2018
                Funding
                Funded by: Sustainable Food Alliance
                Categories
                Molecular Toxicology
                Custom metadata
                © Springer-Verlag GmbH Germany, part of Springer Nature 2018

                Toxicology
                nafld,transcriptome,metabolome,heparg,liver,pcb
                Toxicology
                nafld, transcriptome, metabolome, heparg, liver, pcb

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