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      Discovery of potential urine-accessible metabolite biomarkers associated with muscle disease and corticosteroid response in the mdx mouse model for Duchenne

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          Abstract

          Urine is increasingly being considered as a source of biomarker development in Duchenne Muscular Dystrophy (DMD), a severe, life-limiting disorder that affects approximately 1 in 4500 boys. In this study, we considered the mdx mice—a murine model of DMD—to discover biomarkers of disease, as well as pharmacodynamic biomarkers responsive to prednisolone, a corticosteroid commonly used to treat DMD. Longitudinal urine samples were analyzed from male age-matched mdx and wild-type mice randomized to prednisolone or vehicle control via liquid chromatography tandem mass spectrometry. A large number of metabolites (869 out of 6,334) were found to be significantly different between mdx and wild-type mice at baseline (Bonferroni-adjusted p-value < 0.05), thus being associated with disease status. These included a metabolite with m/z = 357 and creatine, which were also reported in a previous human study looking at serum. Novel observations in this study included peaks identified as biliverdin and hypusine. These four metabolites were significantly higher at baseline in the urine of mdx mice compared to wild-type, and significantly changed their levels over time after baseline. Creatine and biliverdin levels were also different between treated and control groups, but for creatine this may have been driven by an imbalance at baseline. In conclusion, our study reports a number of biomarkers, both known and novel, which may be related to either the mechanisms of muscle injury in DMD or prednisolone treatment.

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          Most cited references25

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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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            A call for transparent reporting to optimize the predictive value of preclinical research.

            The US National Institute of Neurological Disorders and Stroke convened major stakeholders in June 2012 to discuss how to improve the methodological reporting of animal studies in grant applications and publications. The main workshop recommendation is that at a minimum studies should report on sample-size estimation, whether and how animals were randomized, whether investigators were blind to the treatment, and the handling of data. We recognize that achieving a meaningful improvement in the quality of reporting will require a concerted effort by investigators, reviewers, funding agencies and journal editors. Requiring better reporting of animal studies will raise awareness of the importance of rigorous study design to accelerate scientific progress.
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              CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets.

              Liquid chromatography coupled to mass spectrometry is routinely used for metabolomics experiments. In contrast to the fairly routine and automated data acquisition steps, subsequent compound annotation and identification require extensive manual analysis and thus form a major bottleneck in data interpretation. Here we present CAMERA, a Bioconductor package integrating algorithms to extract compound spectra, annotate isotope and adduct peaks, and propose the accurate compound mass even in highly complex data. To evaluate the algorithms, we compared the annotation of CAMERA against a manually defined annotation for a mixture of known compounds spiked into a complex matrix at different concentrations. CAMERA successfully extracted accurate masses for 89.7% and 90.3% of the annotatable compounds in positive and negative ion modes, respectively. Furthermore, we present a novel annotation approach that combines spectral information of data acquired in opposite ion modes to further improve the annotation rate. We demonstrate the utility of CAMERA in two different, easily adoptable plant metabolomics experiments, where the application of CAMERA drastically reduced the amount of manual analysis. © 2011 American Chemical Society
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: Writing – original draftRole: Writing – review & editing
                Role: InvestigationRole: Writing – original draftRole: Writing – review & editing
                Role: InvestigationRole: MethodologyRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: SoftwareRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: InvestigationRole: MethodologyRole: SoftwareRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: SoftwareRole: Writing – review & editing
                Role: InvestigationRole: Writing – original draftRole: Writing – review & editing
                Role: InvestigationRole: MethodologyRole: ResourcesRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Funding acquisitionRole: InvestigationRole: MethodologyRole: ResourcesRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: SoftwareRole: SupervisionRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2019
                16 July 2019
                : 14
                : 7
                : e0219507
                Affiliations
                [1 ] Department of Neurology, George Washington University School of Medicine and Children’s National Health Systems, Washington, D.C., United States of America
                [2 ] Department of Genomics and Precision Medicine, George Washington University School of Medicine and Children’s National Health Systems, Washington, D.C., United States of America
                [3 ] Department of Oncology, Georgetown University Medical Center, Washington, D.C., United States of America
                [4 ] School of Pharmacy & Pharmaceutical Sciences, Binghamton University, Binghamton, N.Y., United States of America
                [5 ] Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, D.C., United States of America
                [6 ] Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University Medical Center, Washington, D.C., United States of America
                Swinburne University of Technology, AUSTRALIA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-1400-3398
                Article
                PONE-D-19-00276
                10.1371/journal.pone.0219507
                6634414
                31310630
                4c26794f-fec3-41fc-8f2b-dc51476fa82c
                © 2019 Thangarajh et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 4 January 2019
                : 25 June 2019
                Page count
                Figures: 7, Tables: 1, Pages: 17
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000054, National Cancer Institute;
                Award ID: P30CA051008
                Funded by: funder-id http://dx.doi.org/10.13039/100005202, Muscular Dystrophy Association;
                Award ID: MDA353094
                Award Recipient :
                Funded by: American Academy of Neurology/American Brain Foundation
                Award ID: Clinical Research Training Fellowship
                Award Recipient :
                MT acknowledges the support of the American Academy of Neurology/American Brain Foundation Clinical Research Training Fellowship, (2015-2017). YH acknowledges the support of the Muscular Dystrophy Association (MDA353094). This research was also supported by the National Cancer Institute, (P30CA051008). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Biochemistry
                Metabolism
                Metabolites
                Physical Sciences
                Chemistry
                Chemical Compounds
                Organic Compounds
                Creatine
                Physical Sciences
                Chemistry
                Organic Chemistry
                Organic Compounds
                Creatine
                Biology and Life Sciences
                Biochemistry
                Biomarkers
                Biology and Life Sciences
                Anatomy
                Body Fluids
                Urine
                Medicine and Health Sciences
                Anatomy
                Body Fluids
                Urine
                Biology and Life Sciences
                Physiology
                Body Fluids
                Urine
                Medicine and Health Sciences
                Physiology
                Body Fluids
                Urine
                Research and Analysis Methods
                Animal Studies
                Experimental Organism Systems
                Model Organisms
                Mouse Models
                Research and Analysis Methods
                Model Organisms
                Mouse Models
                Research and Analysis Methods
                Animal Studies
                Experimental Organism Systems
                Animal Models
                Mouse Models
                Biology and Life Sciences
                Biochemistry
                Biomarkers
                Creatinine
                Medicine and Health Sciences
                Pharmacology
                Pharmacokinetics
                Drug Metabolism
                Medicine and Health Sciences
                Neurology
                Muscular Dystrophies
                Duchenne Muscular Dystrophy
                Medicine and Health Sciences
                Clinical Genetics
                X-Linked Traits
                Duchenne Muscular Dystrophy
                Biology and Life Sciences
                Genetics
                Heredity
                Genetic Linkage
                Sex Linkage
                X-Linked Traits
                Duchenne Muscular Dystrophy
                Custom metadata
                All relevant data are within the paper and its Supporting Information files. Raw data are also available through the Dryad Digital Repository (doi: 10.5061/dryad.v42h23k).

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