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      Metabolic network-based predictions of toxicant-induced metabolite changes in the laboratory rat

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          Abstract

          In order to provide timely treatment for organ damage initiated by therapeutic drugs or exposure to environmental toxicants, we first need to identify markers that provide an early diagnosis of potential adverse effects before permanent damage occurs. Specifically, the liver, as a primary organ prone to toxicants-induced injuries, lacks diagnostic markers that are specific and sensitive to the early onset of injury. Here, to identify plasma metabolites as markers of early toxicant-induced injury, we used a constraint-based modeling approach with a genome-scale network reconstruction of rat liver metabolism to incorporate perturbations of gene expression induced by acetaminophen, a known hepatotoxicant. A comparison of the model results against the global metabolic profiling data revealed that our approach satisfactorily predicted altered plasma metabolite levels as early as 5 h after exposure to 2 g/kg of acetaminophen, and that 10 h after treatment the predictions significantly improved when we integrated measured central carbon fluxes. Our approach is solely driven by gene expression and physiological boundary conditions, and does not rely on any toxicant-specific model component. As such, it provides a mechanistic model that serves as a first step in identifying a list of putative plasma metabolites that could change due to toxicant-induced perturbations.

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          Organization of GC/MS and LC/MS metabolomics data into chemical libraries

          Background Metabolomics experiments involve generating and comparing small molecule (metabolite) profiles from complex mixture samples to identify those metabolites that are modulated in altered states (e.g., disease, drug treatment, toxin exposure). One non-targeted metabolomics approach attempts to identify and interrogate all small molecules in a sample using GC or LC separation followed by MS or MSn detection. Analysis of the resulting large, multifaceted data sets to rapidly and accurately identify the metabolites is a challenging task that relies on the availability of chemical libraries of metabolite spectral signatures. A method for analyzing spectrometry data to identify and Qu antify I ndividual C omponents in a S ample, (QUICS), enables generation of chemical library entries from known standards and, importantly, from unknown metabolites present in experimental samples but without a corresponding library entry. This method accounts for all ions in a sample spectrum, performs library matches, and allows review of the data to quality check library entries. The QUICS method identifies ions related to any given metabolite by correlating ion data across the complete set of experimental samples, thus revealing subtle spectral trends that may not be evident when viewing individual samples and are likely to be indicative of the presence of one or more otherwise obscured metabolites. Results LC-MS/MS or GC-MS data from 33 liver samples were analyzed simultaneously which exploited the inherent biological diversity of the samples and the largely non-covariant chemical nature of the metabolites when viewed over multiple samples. Ions were partitioned by both retention time (RT) and covariance which grouped ions from a single common underlying metabolite. This approach benefitted from using mass, time and intensity data in aggregate over the entire sample set to reject outliers and noise thereby producing higher quality chemical identities. The aggregated data was matched to reference chemical libraries to aid in identifying the ion set as a known metabolite or as a new unknown biochemical to be added to the library. Conclusion The QUICS methodology enabled rapid, in-depth evaluation of all possible metabolites (known and unknown) within a set of samples to identify the metabolites and, for those that did not have an entry in the reference library, to create a library entry to identify that metabolite in future studies.
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            Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease.

            Several liver disorders result from perturbations in the metabolism of hepatocytes, and their underlying mechanisms can be outlined through the use of genome-scale metabolic models (GEMs). Here we reconstruct a consensus GEM for hepatocytes, which we call iHepatocytes2322, that extends previous models by including an extensive description of lipid metabolism. We build iHepatocytes2322 using Human Metabolic Reaction 2.0 database and proteomics data in Human Protein Atlas, which experimentally validates the incorporated reactions. The reconstruction process enables improved annotation of the proteomics data using the network centric view of iHepatocytes2322. We then use iHepatocytes2322 to analyse transcriptomics data obtained from patients with non-alcoholic fatty liver disease. We show that blood concentrations of chondroitin and heparan sulphates are suitable for diagnosing non-alcoholic steatohepatitis and for the staging of non-alcoholic fatty liver disease. Furthermore, we observe serine deficiency in patients with NASH and identify PSPH, SHMT1 and BCAT1 as potential therapeutic targets for the treatment of non-alcoholic steatohepatitis.
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              Idiosyncratic drug hepatotoxicity.

              The occurrence of idiosyncratic drug hepatotoxicity is a major problem in all phases of clinical drug development and the most frequent cause of post-marketing warnings and withdrawals. This review examines the clinical signatures of this problem, signals predictive of its occurrence (particularly of more frequent, reversible, low-grade injury) and the role of monitoring in prevention by examining several recent examples (for example, troglitazone). In addition, the failure of preclinical toxicology to predict idiosyncratic reactions, and what can be done to improve this problem, is discussed. Finally, our current understanding of the pathophysiology of experimental drug hepatotoxicity is examined, focusing on acetaminophen, particularly with respect to the role of the innate immune system and control of cell-death pathways, which might provide targets for exploration and identification of risk factors and mechanisms in humans.
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                Author and article information

                Contributors
                vpannala@bhsai.org
                j.d.young@vanderbilt.edu
                sven.a.wallqvist.civ@mail.mil
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                3 August 2018
                3 August 2018
                2018
                : 8
                : 11678
                Affiliations
                [1 ]ISNI 0000 0001 0036 4726, GRID grid.420210.5, Department of Defense Biotechnology High Performance Computing Software Applications Institute, , Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, ; Fort Detrick, MD 21702 USA
                [2 ]ISNI 0000 0001 2264 7217, GRID grid.152326.1, Department of Molecular Physiology and Biophysics, , Vanderbilt University School of Medicine, ; Nashville, TN 37232 USA
                [3 ]ISNI 0000 0001 2264 7217, GRID grid.152326.1, Department of Chemical and Biomolecular Engineering, , Vanderbilt University School of Engineering, ; Nashville, TN 37232 USA
                Article
                30149
                10.1038/s41598-018-30149-7
                6076258
                30076366
                8e99dcbe-bc4a-4103-9eba-8cd1a569a98e
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 9 March 2018
                : 23 July 2018
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