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

Maria Suarez-Diez*, Jonathan Adam, Jerzy Adamski, Styliani A. Chasapi, Claudio Luchinat, Annette Peters, Cornelia Prehn, Claudio Santucci, Alexandros Spyridonidis, Georgios A. Spyroulias, Leonardo Tenori, Rui Wang-Sattler, Edoardo Saccenti

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

30 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)2547-2559
JournalJournal of Proteome Research
Volume16
Issue number7
DOIs
Publication statusPublished - 2017

Keywords

  • blood
  • correlations
  • differential network analysis
  • low molecular weight metabolites
  • mutual information
  • network inference
  • network topology
  • plasma
  • serum

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