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      Transcriptome–metabolome wide association study (TMWAS) of maneb and paraquat neurotoxicity reveals network level interactions in toxicologic mechanism

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          Abstract

          A combination of the herbicide paraquat (PQ) and fungicide maneb (MB) has been linked to Parkinson's disease. Previous studies show that this involves an additive toxicity with at least two different mechanisms. However, detailed understanding of mixtures is often difficult to elucidate because of the multiple ways by which toxic agents can interact. In the present study, we used a combination of transcriptomics and metabolomics to investigate mechanisms of toxicity of PQ and MB in a neuroblastoma cell line. Conditions were studied with concentrations of PQ and MB that each individually caused 20% cell death and together caused 50% cell death. Transcriptomic and metabolomic samples were collected at time points prior to significant cell death. Statistical and bioinformatic methods were applied to the resulting 30,869 transcripts and 1358 metabolites. Results showed that MB significantly changed more transcripts and metabolites than PQ, and combined PQ + MB impacted more than MB alone. Transcriptome–metabolome-wide association study (TMWAS) showed that significantly changed transcripts and metabolites mapped to two network substructures, one associating with significant effects of MB and the other included features significantly associated with PQ + MB. The latter contained 4 clusters of genes and associated metabolites, with one containing genes for two cation transporters and a cation transporter regulatory protein also recognized as a pro-apoptotic protein. Other clusters included stress response genes and transporters linked to cytoprotective mechanisms. MB also had a significant network structure linked to cell proliferation. Together, the results show that the toxicologic mechanism of the combined neurotoxicity of PQ and MB involves network level interactions and that TMWAS provides an effective approach to investigate such complex mechanisms.

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          Most cited references33

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

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            METLIN: a metabolite mass spectral database.

            Endogenous metabolites have gained increasing interest over the past 5 years largely for their implications in diagnostic and pharmaceutical biomarker discovery. METLIN (http://metlin.scripps.edu), a freely accessible web-based data repository, has been developed to assist in a broad array of metabolite research and to facilitate metabolite identification through mass analysis. METLINincludes an annotated list of known metabolite structural information that is easily cross-correlated with its catalogue of high-resolution Fourier transform mass spectrometry (FTMS) spectra, tandem mass spectrometry (MS/MS) spectra, and LC/MS data.
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              integrOmics: an R package to unravel relationships between two omics datasets

              Motivation: With the availability of many ‘omics’ data, such as transcriptomics, proteomics or metabolomics, the integrative or joint analysis of multiple datasets from different technology platforms is becoming crucial to unravel the relationships between different biological functional levels. However, the development of such an analysis is a major computational and technical challenge as most approaches suffer from high data dimensionality. New methodologies need to be developed and validated. Results: integrOmics efficiently performs integrative analyses of two types of ‘omics’ variables that are measured on the same samples. It includes a regularized version of canonical correlation analysis to enlighten correlations between two datasets, and a sparse version of partial least squares (PLS) regression that includes simultaneous variable selection in both datasets. The usefulness of both approaches has been demonstrated previously and successfully applied in various integrative studies. Availability: integrOmics is freely available from http://CRAN.R-project.org/ or from the web site companion (http://math.univ-toulouse.fr/biostat) that provides full documentation and tutorials. Contact: k.lecao@uq.edu.au Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Journal
                101630272
                42420
                Toxicol Rep
                Toxicol Rep
                Toxicology reports
                2214-7500
                28 September 2016
                24 July 2014
                2014
                06 October 2016
                : 1
                : 435-444
                Affiliations
                [a ]Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Emory University, Atlanta, GA 30322, United States
                [b ]Clinical Biomarkers Laboratory, Emory University, Atlanta, GA 30322, United States
                [c ]School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, United States
                [d ]College of Pharmacy, Korea University, Sejong City, Republic of Korea
                Author notes
                [* ]Corresponding author at: 615 Michael St., Atlanta, GA 30322, United States. Tel.: +1 404 727 5970; fax: +1 404 712 2974. dpjones@ 123456emory.edu (D.P.Jones)
                [1]

                Co-first authorship.

                Article
                NIHMS616852
                10.1016/j.toxrep.2014.07.006
                5053767
                002d4667-a38f-4bdc-8b67-09c90967858d

                This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/3.0/).

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                Categories
                Article

                maneb,metabolomics,paraquat,parkinson's disease
                maneb, metabolomics, paraquat, parkinson's disease

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