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      Plasma and Serum Metabolite Association Networks: Comparability within and between Studies Using NMR and MS Profiling

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          Abstract

          Blood is one of the most used biofluids in metabolomics studies, and the serum and plasma fractions are routinely used as a proxy for blood itself. Here we investigated the association networks of an array of 29 metabolites identified and quantified via NMR in the plasma and serum samples of two cohorts of ∼1000 healthy blood donors each. A second study of 377 individuals was used to extract plasma and serum samples from the same individual on which a set of 122 metabolites were detected and quantified using FIA–MS/MS. Four different inference algorithms (ARANCE, CLR, CORR, and PCLRC) were used to obtain consensus networks. The plasma and serum networks obtained from different studies showed different topological properties with the serum network being more connected than the plasma network. On a global level, metabolite association networks from plasma and serum fractions obtained from the same blood sample of healthy people show similar topologies, and at a local level, some differences arise like in the case of amino acids.

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          minet: A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual Information

          Results This paper presents the R/Bioconductor package minet (version 1.1.6) which provides a set of functions to infer mutual information networks from a dataset. Once fed with a microarray dataset, the package returns a network where nodes denote genes, edges model statistical dependencies between genes and the weight of an edge quantifies the statistical evidence of a specific (e.g transcriptional) gene-to-gene interaction. Four different entropy estimators are made available in the package minet (empirical, Miller-Madow, Schurmann-Grassberger and shrink) as well as four different inference methods, namely relevance networks, ARACNE, CLR and MRNET. Also, the package integrates accuracy assessment tools, like F-scores, PR-curves and ROC-curves in order to compare the inferred network with a reference one. Conclusion The package minet provides a series of tools for inferring transcriptional networks from microarray data. It is freely available from the Comprehensive R Archive Network (CRAN) as well as from the Bioconductor website.
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            Reflections on univariate and multivariate analysis of metabolomics data

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              Differences between Human Plasma and Serum Metabolite Profiles

              Background Human plasma and serum are widely used matrices in clinical and biological studies. However, different collecting procedures and the coagulation cascade influence concentrations of both proteins and metabolites in these matrices. The effects on metabolite concentration profiles have not been fully characterized. Methodology/Principal Findings We analyzed the concentrations of 163 metabolites in plasma and serum samples collected simultaneously from 377 fasting individuals. To ensure data quality, 41 metabolites with low measurement stability were excluded from further analysis. In addition, plasma and corresponding serum samples from 83 individuals were re-measured in the same plates and mean correlation coefficients (r) of all metabolites between the duplicates were 0.83 and 0.80 in plasma and serum, respectively, indicating significantly better stability of plasma compared to serum (p = 0.01). Metabolite profiles from plasma and serum were clearly distinct with 104 metabolites showing significantly higher concentrations in serum. In particular, 9 metabolites showed relative concentration differences larger than 20%. Despite differences in absolute concentration between the two matrices, for most metabolites the overall correlation was high (mean r = 0.81±0.10), which reflects a proportional change in concentration. Furthermore, when two groups of individuals with different phenotypes were compared with each other using both matrices, more metabolites with significantly different concentrations could be identified in serum than in plasma. For example, when 51 type 2 diabetes (T2D) patients were compared with 326 non-T2D individuals, 15 more significantly different metabolites were found in serum, in addition to the 25 common to both matrices. Conclusions/Significance Our study shows that reproducibility was good in both plasma and serum, and better in plasma. Furthermore, as long as the same blood preparation procedure is used, either matrix should generate similar results in clinical and biological studies. The higher metabolite concentrations in serum, however, make it possible to provide more sensitive results in biomarker detection.
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                Author and article information

                Journal
                J Proteome Res
                J. Proteome Res
                pr
                jprobs
                Journal of Proteome Research
                American Chemical Society
                1535-3893
                1535-3907
                18 May 2017
                07 July 2017
                : 16
                : 7
                : 2547-2559
                Affiliations
                []Laboratory of Systems and Synthetic Biology, Wageningen University & Research , Stippeneng 4, 6708 WE Wageningen, The Netherlands
                []Research Unit of Molecular Epidemiology, Helmholtz Zentrum München , 85764 München-Neuherberg, Germany
                [§ ]Institute of Epidemiology II, Helmholtz Zentrum München , 85764 München-Neuherberg, Germany
                []German Center for Diabetes Research (DZD), Helmholtz Zentrum München , 85764 München-Neuherberg, Germany
                []Institute of Experimental Genetics, Helmholtz Zentrum München , 85764 München-Neuherberg, Germany
                [# ]Chair of Experimental Genetics, Center of Life and Food Sciences Weihenstephan, Technische Universität München , 85353 Freising, Germany
                []Department of Pharmacy, University of Patras , 26504 Rio, Greece
                []Magnetic Resonance Center (CERM), University of Florence , Via L. Sacconi 6, 50019 Sesto Fiorentino, Italy
                []Department of Chemistry, University of Florence , Via della Lastruccia 3, 50019 Sesto Fiorentino, Italy
                []Department of Environmental Health, Harvard School of Public Health , Boston, Massachusetts 02115, United States
                [+ ]Bone Marrow Transplantation Unit, University Hospital Patras , 26500 Rio, Greece
                []Department of Experimental and Clinical Medicine, University of Florence , Largo Brambilla 3, 501134 Florence, Italy
                Author notes
                [* ]E.S.: E-mail: esaccenti@ 123456gmail.com .
                [* ]M.S.-D.: E-mail: maria.suarezdiez@ 123456wur.nl . Tel: +31 (0)317 482018.
                Article
                10.1021/acs.jproteome.7b00106
                5645760
                28517934
                d6ba7bc1-0049-4fb3-a72a-acef1e41c03b
                Copyright © 2017 American Chemical Society

                This is an open access article published under a Creative Commons Non-Commercial No Derivative Works (CC-BY-NC-ND) Attribution License, which permits copying and redistribution of the article, and creation of adaptations, all for non-commercial purposes.

                History
                : 22 February 2017
                Categories
                Article
                Custom metadata
                pr7b00106
                pr-2017-00106k

                Molecular biology
                blood,serum,plasma,low molecular weight metabolites,correlations,mutual information,network inference,differential network analysis,network topology

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