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Plasma and Serum Metabolite Association Networks: Comparability within and between Studies Using NMR and MS Profiling
[Image: see text] 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...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical
Society
2017
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5645760/ https://www.ncbi.nlm.nih.gov/pubmed/28517934 http://dx.doi.org/10.1021/acs.jproteome.7b00106 |
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author | Suarez-Diez, Maria Adam, Jonathan Adamski, Jerzy Chasapi, Styliani A. Luchinat, Claudio Peters, Annette Prehn, Cornelia Santucci, Claudio Spyridonidis, Alexandros Spyroulias, Georgios A. Tenori, Leonardo Wang-Sattler, Rui Saccenti, Edoardo |
author_facet | Suarez-Diez, Maria Adam, Jonathan Adamski, Jerzy Chasapi, Styliani A. Luchinat, Claudio Peters, Annette Prehn, Cornelia Santucci, Claudio Spyridonidis, Alexandros Spyroulias, Georgios A. Tenori, Leonardo Wang-Sattler, Rui Saccenti, Edoardo |
author_sort | Suarez-Diez, Maria |
collection | PubMed |
description | [Image: see text] 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. |
format | Online Article Text |
id | pubmed-5645760 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | American Chemical
Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-56457602017-10-19 Plasma and Serum Metabolite Association Networks: Comparability within and between Studies Using NMR and MS Profiling Suarez-Diez, Maria Adam, Jonathan Adamski, Jerzy Chasapi, Styliani A. Luchinat, Claudio Peters, Annette Prehn, Cornelia Santucci, Claudio Spyridonidis, Alexandros Spyroulias, Georgios A. Tenori, Leonardo Wang-Sattler, Rui Saccenti, Edoardo J Proteome Res [Image: see text] 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. American Chemical Society 2017-05-18 2017-07-07 /pmc/articles/PMC5645760/ /pubmed/28517934 http://dx.doi.org/10.1021/acs.jproteome.7b00106 Text en 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 (http://pubs.acs.org/page/policy/authorchoice_ccbyncnd_termsofuse.html) , which permits copying and redistribution of the article, and creation of adaptations, all for non-commercial purposes. |
spellingShingle | Suarez-Diez, Maria Adam, Jonathan Adamski, Jerzy Chasapi, Styliani A. Luchinat, Claudio Peters, Annette Prehn, Cornelia Santucci, Claudio Spyridonidis, Alexandros Spyroulias, Georgios A. Tenori, Leonardo Wang-Sattler, Rui Saccenti, Edoardo Plasma and Serum Metabolite Association Networks: Comparability within and between Studies Using NMR and MS Profiling |
title | Plasma and Serum
Metabolite Association Networks:
Comparability within and between Studies Using NMR and MS Profiling |
title_full | Plasma and Serum
Metabolite Association Networks:
Comparability within and between Studies Using NMR and MS Profiling |
title_fullStr | Plasma and Serum
Metabolite Association Networks:
Comparability within and between Studies Using NMR and MS Profiling |
title_full_unstemmed | Plasma and Serum
Metabolite Association Networks:
Comparability within and between Studies Using NMR and MS Profiling |
title_short | Plasma and Serum
Metabolite Association Networks:
Comparability within and between Studies Using NMR and MS Profiling |
title_sort | plasma and serum
metabolite association networks:
comparability within and between studies using nmr and ms profiling |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5645760/ https://www.ncbi.nlm.nih.gov/pubmed/28517934 http://dx.doi.org/10.1021/acs.jproteome.7b00106 |
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