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      High levels of modified ceramides are a defining feature of murine and human cancer cachexia

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          Abstract

          Background

          Cancer cachexia (CCx) is a multifactorial energy‐wasting syndrome reducing the efficiency of anti‐cancer therapies, quality of life, and survival of cancer patients. In the past years, most studies focused on the identification of tumour and host‐derived proteins contributing to CCx. However, there is still a lack of studies addressing the changes in bioactive lipids. The aim of this study was to identify specific lipid species as a hallmark of CCx by performing a broad range lipid analysis of plasma from well‐established CCx mouse models as well as cachectic and weight stable cancer patients.

          Methods

          Plasma from non‐cachectic (PBS‐injected mice, NC26 tumour‐bearing mice), pre‐cachectic and cachectic mice (C26 and LLC tumour‐bearing mice, Apc Min/+ mutant mice), and plasma from weight stable and cachectic patients with gastrointestinal cancer, were analysed using the Lipidyzer™ platform. In total, 13 lipid classes and more than 1100 lipid species, including sphingolipids, neutral and polar glycerolipids, were covered by the analysis. Correlation analysis between specific lipid species and readouts of CCx were performed. Lipidomics data were confirmed by gene expression analysis of metabolic organs to analyse enzymes involved in sphingolipid synthesis and degradation.

          Results

          A decrease in several lysophosphatidylcholine (LPC) species and an increase in numerous sphingolipids including sphingomyelins (SMs), ceramides (CERs), hexosyl‐ceramides (HCERs) and lactosyl‐ceramides (LCERs), were mutual features of CCx in both mice and cancer patients. Notably, sphingolipid levels gradually increased during cachexia development. Key enzymes involved in ceramide synthesis were elevated in liver but not in adipose, muscle, or tumour tissues, suggesting that ceramide turnover in the liver is a major contributor to elevated sphingolipid levels in CCx. LPC(16:1), LPC(20:3), SM(16:0), SM(24:1), CER(16:0), CER(24:1), HCER(16:0), and HCER(24:1) were the most consistently affected lipid species between mice and humans and correlated negatively (LPCs) or positively (SMs, CERs and HCERs) with the severity of body weight loss.

          Conclusions

          High levels of sphingolipids, specifically ceramides and modified ceramides, are a defining feature of murine and human CCx and may contribute to tissue wasting and skeletal muscle atrophy through the inhibition of anabolic signals. The progressive increase in sphingolipids during cachexia development supports their potential as early biomarkers for CCx.

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          Most cited references60

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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            Definition and classification of cancer cachexia: an international consensus.

            To develop a framework for the definition and classification of cancer cachexia a panel of experts participated in a formal consensus process, including focus groups and two Delphi rounds. Cancer cachexia was defined as a multifactorial syndrome defined by an ongoing loss of skeletal muscle mass (with or without loss of fat mass) that cannot be fully reversed by conventional nutritional support and leads to progressive functional impairment. Its pathophysiology is characterised by a negative protein and energy balance driven by a variable combination of reduced food intake and abnormal metabolism. The agreed diagnostic criterion for cachexia was weight loss greater than 5%, or weight loss greater than 2% in individuals already showing depletion according to current bodyweight and height (body-mass index [BMI] <20 kg/m(2)) or skeletal muscle mass (sarcopenia). An agreement was made that the cachexia syndrome can develop progressively through various stages--precachexia to cachexia to refractory cachexia. Severity can be classified according to degree of depletion of energy stores and body protein (BMI) in combination with degree of ongoing weight loss. Assessment for classification and clinical management should include the following domains: anorexia or reduced food intake, catabolic drive, muscle mass and strength, functional and psychosocial impairment. Consensus exists on a framework for the definition and classification of cancer cachexia. After validation, this should aid clinical trial design, development of practice guidelines, and, eventually, routine clinical management. Copyright © 2011 Elsevier Ltd. All rights reserved.
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              MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis

              Abstract We present a new update to MetaboAnalyst (version 4.0) for comprehensive metabolomic data analysis, interpretation, and integration with other omics data. Since the last major update in 2015, MetaboAnalyst has continued to evolve based on user feedback and technological advancements in the field. For this year's update, four new key features have been added to MetaboAnalyst 4.0, including: (1) real-time R command tracking and display coupled with the release of a companion MetaboAnalystR package; (2) a MS Peaks to Pathways module for prediction of pathway activity from untargeted mass spectral data using the mummichog algorithm; (3) a Biomarker Meta-analysis module for robust biomarker identification through the combination of multiple metabolomic datasets and (4) a Network Explorer module for integrative analysis of metabolomics, metagenomics, and/or transcriptomics data. The user interface of MetaboAnalyst 4.0 has been reengineered to provide a more modern look and feel, as well as to give more space and flexibility to introduce new functions. The underlying knowledgebases (compound libraries, metabolite sets, and metabolic pathways) have also been updated based on the latest data from the Human Metabolome Database (HMDB). A Docker image of MetaboAnalyst is also available to facilitate download and local installation of MetaboAnalyst. MetaboAnalyst 4.0 is freely available at http://metaboanalyst.ca.
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                Author and article information

                Contributors
                mauricio.berrieldiaz@helmholtz-muenchen.de
                maria.rohm@helmholtz-muenchen.de
                Journal
                J Cachexia Sarcopenia Muscle
                J Cachexia Sarcopenia Muscle
                10.1007/13539.2190-6009
                JCSM
                Journal of Cachexia, Sarcopenia and Muscle
                John Wiley and Sons Inc. (Hoboken )
                2190-5991
                2190-6009
                08 October 2020
                December 2020
                : 11
                : 6 ( doiID: 10.1002/jcsm.v11.6 )
                : 1459-1475
                Affiliations
                [ 1 ] Institute for Diabetes and Cancer Helmholtz Center Munich Neuherberg Germany
                [ 2 ] Joint Heidelberg‐IDC Translational Diabetes Program, Inner Medicine 1 Heidelberg University Hospital Heidelberg Germany
                [ 3 ] German Center for Diabetes Research (DZD) Neuherberg Germany
                [ 4 ] Molecular Metabolic Control Technical University of Munich Munich Germany
                [ 5 ] Research Unit Molecular Endocrinology and Metabolism Helmholtz Center Munich, German Research Center for Environmental Health Neuherberg Germany
                [ 6 ] Cancer Metabolism Research Group, LIM 26 HC, Medical School University of São Paulo São Paulo Brazil
                [ 7 ] Experimentelle Genetik Technical University of Munich Freising‐Weihenstephan Neuherberg Germany
                [ 8 ] Department of Biochemistry, Yong Loo Lin School of Medicine National University of Singapore Singapore
                Author notes
                [*] [* ]Correspondence to: Mauricio Berriel Diaz and Maria Rohm, Institute for Diabetes and Cancer, Helmholtz Center Munich, 85764 Oberschleißheim, Germany; Joint Heidelberg‐IDC Translational Diabetes Program, Inner Medicine 1, Heidelberg University Hospital, Heidelberg, Germany. Email: maria.rohm@ 123456helmholtz-muenchen.de ; mauricio.berrieldiaz@ 123456helmholtz-muenchen.de
                [#]

                Pauline Morigny and Julia Zuber contributed equally to the study.

                [Correction added on 7 November 2020, after first online publication: Projekt Deal funding statement has been added.]

                Author information
                https://orcid.org/0000-0003-4670-919X
                https://orcid.org/0000-0003-3926-1534
                Article
                JCSM12626 JCSM-D-20-00400
                10.1002/jcsm.12626
                7749558
                33090732
                67b0746a-fd41-4951-a0fe-23d76fa2ccba
                © 2020 The Authors. Journal of Cachexia, Sarcopenia and Muscle published by John Wiley & Sons Ltd on behalf of Society on Sarcopenia, Cachexia and Wasting Disorders

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 06 July 2020
                : 19 August 2020
                Page count
                Figures: 6, Tables: 2, Pages: 17, Words: 7697
                Funding
                Funded by: FAPESP 2012/50079‐0
                Funded by: Novo Nordisk Foundation , open-funder-registry 10.13039/501100009708;
                Award ID: NNFOC150019050
                Funded by: Austrian Science Fund , open-funder-registry 10.13039/501100002428;
                Funded by: DFG, Deutsche Forschungsgemeinschaft , open-funder-registry 10.13039/501100001659;
                Award ID: SFB1321
                Funded by: Helmholtz Gemeinschaft , open-funder-registry 10.13039/501100001656;
                Categories
                Original Article
                Original Articles
                Custom metadata
                2.0
                December 2020
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.9.5 mode:remove_FC converted:19.12.2020

                Orthopedics
                cancer cachexia,lipidomics,signalling lipids,sphingolipids,ceramides
                Orthopedics
                cancer cachexia, lipidomics, signalling lipids, sphingolipids, ceramides

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