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      Metabolism dysregulation induces a specific lipid signature of nonalcoholic steatohepatitis in patients

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          Abstract

          Nonalcoholic steatohepatitis (NASH) is a condition which can progress to cirrhosis and hepatocellular carcinoma. Markers for NASH diagnosis are still lacking. We performed a comprehensive lipidomic analysis on human liver biopsies including normal liver, nonalcoholic fatty liver and NASH. Random forests-based machine learning approach allowed characterizing a signature of 32 lipids discriminating NASH with 100% sensitivity and specificity. Furthermore, we validated this signature in an independent group of NASH patients. Then, metabolism dysregulations were investigated in both patients and murine models. Alterations of elongase and desaturase activities were observed along the fatty acid synthesis pathway. The decreased activity of the desaturase FADS1 appeared as a bottleneck, leading upstream to an accumulation of fatty acids and downstream to a deficiency of long-chain fatty acids resulting to impaired phospholipid synthesis. In NASH, mass spectrometry imaging on tissue section revealed the spreading into the hepatic parenchyma of selectively accumulated fatty acids. Such lipids constituted a highly toxic mixture to human hepatocytes. In conclusion, this study characterized a specific and sensitive lipid signature of NASH and positioned FADS1 as a significant player in accumulating toxic lipids during NASH progression.

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          Most cited references49

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          A rapid method of total lipid extraction and purification.

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            Random forest: a classification and regression tool for compound classification and QSAR modeling.

            A new classification and regression tool, Random Forest, is introduced and investigated for predicting a compound's quantitative or categorical biological activity based on a quantitative description of the compound's molecular structure. Random Forest is an ensemble of unpruned classification or regression trees created by using bootstrap samples of the training data and random feature selection in tree induction. Prediction is made by aggregating (majority vote or averaging) the predictions of the ensemble. We built predictive models for six cheminformatics data sets. Our analysis demonstrates that Random Forest is a powerful tool capable of delivering performance that is among the most accurate methods to date. We also present three additional features of Random Forest: built-in performance assessment, a measure of relative importance of descriptors, and a measure of compound similarity that is weighted by the relative importance of descriptors. It is the combination of relatively high prediction accuracy and its collection of desired features that makes Random Forest uniquely suited for modeling in cheminformatics.
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              The ratio of phosphatidylcholine to phosphatidylethanolamine influences membrane integrity and steatohepatitis.

              Phosphatidylcholine (PC) and phosphatidylethanolamine (PE) are major phospholipids in mammalian membranes. In liver, PC is synthesized via the choline pathway or by methylation of PE via phosphatidylethanolamine N-methyltransferase (PEMT). Pemt(-/-) mice fed a choline-deficient (CD) diet develop rapid steatohepatitis leading to liver failure. Steatosis is observed in CD mice that lack both PEMT and multiple drug-resistant protein 2 (MDR2), required for PC secretion into bile. We demonstrate that liver failure in CD-Pemt(-/-) mice is due to loss of membrane integrity caused by a decreased PC/PE ratio. The CD-Mdr2(-/-)/Pemt(-/-) mice escape liver failure by maintaining a normal PC/PE ratio. Manipulation of PC/PE levels suggests that this ratio is a key regulator of cell membrane integrity and plays a role in the progression of steatosis into steatohepatitis. The results have clinical implications as patients with nonalcoholic steatohepatitis have a decreased ratio of PC to PE compared to control livers.
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                Author and article information

                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group
                2045-2322
                24 April 2017
                2017
                : 7
                : 46658
                Affiliations
                [1 ]Inserm, Unité 1193 , Villejuif, F-94800, France
                [2 ]Univ Paris-Sud, UMR-S1193 , Villejuif, F-94800, France
                [3 ]DHU Hepatinov , Villejuif, F-94800, France
                [4 ]AP-HP, Hôpital Paul-Brousse, Centre Hépato-Biliaire , Villejuif, F-94800, France
                [5 ]Institut de Chimie des Substances Naturelles, CNRS UPR 2301, Univ. Paris-Sud, Université Paris-Saclay , F-91198 Gif-Sur-Yvette, France
                [6 ]Inserm, Unité 1065 , Nice, F-06204, France
                [7 ]University of Nice-Sophia-Antipolis , Nice, F-06204, France
                [8 ]Centre Hospitalier Universitaire de Nice, Hôpital L’Archet , Nice Cedex 3, F-06202, France
                [9 ]Inserm, US33 , Villejuif, F-94800, France
                [10 ]Univ Paris-Sud, US33 , Villejuif, F-94800, France
                [11 ]MetaToul-Lipidomic Facility, MetaboHUB, Inserm UMR1048 , Toulouse, F-31432, France
                [12 ]AP-HP, Hôpital du Kremlin-Bicêtre, Service d’Anatomopathologie, Le Kremlin-Bicêtre , F-94275, France
                Author notes
                [*]

                These authors contributed equally to this work.

                Article
                srep46658
                10.1038/srep46658
                5402394
                28436449
                6d5b84b4-9c41-4197-bd3a-5b14ac2f6325
                Copyright © 2017, The Author(s)

                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
                : 15 November 2016
                : 28 March 2017
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