title: Multi-Organ Contribution to the Metabolic Plasma Profile Using Hierarchical Modelling.
abstract: Hierarchical modelling was applied in order to identify the organs that contribute to the levels of metabolites in plasma. Plasma and organ samples from gut, kidney, liver, muscle and pancreas were obtained from mice. The samples were analysed using gas chromatography time-of-flight mass spectrometry (GC TOF-MS) at the Swedish Metabolomics centre, Umeå University, Sweden. The multivariate analysis was performed by means of principal component analysis (PCA) and orthogonal projections to latent structures (OPLS). The main goal of this study was to investigate how each organ contributes to the metabolic plasma profile. This was performed using hierarchical modelling. Each organ was found to have a unique metabolic profile. The hierarchical modelling showed that the gut, kidney and liver demonstrated the greatest contribution to the metabolic pattern of plasma. For example, we found that metabolites were absorbed in the gut and transported to the plasma. The kidneys excrete branched chain amino acids (BCAAs) and fatty acids are transported in the plasma to the muscles and liver. Lactic acid was also found to be transported from the pancreas to plasma. The results indicated that hierarchical modelling can be utilized to identify the organ contribution of unknown metabolites to the metabolic profile of plasma.
authors.affiliation: Computational Life Science Cluster (CLiC), Department of Chemistry, Umeå University, Umeå, Sweden; Karlsruhe Institute of Technology, Karlsruhe, Germany.
authors.forename: Frida
authors.initials: F
authors.lastname: Torell
authors.affiliation: AcureOmics AB, Umeå, Sweden.
authors.forename: Kate
authors.initials: K
authors.lastname: Bennett
authors.affiliation: CNRS, UMR7622, 75005, Paris, France; Sorbonne Universités, UPMC, UMR7622, 75005, Paris, France; Inserm U-1156, Paris, France.
authors.forename: Silvia
authors.initials: S
authors.lastname: Cereghini
authors.affiliation: AcureOmics AB, Umeå, Sweden.
authors.forename: Stefan
authors.initials: S
authors.lastname: Rännar
authors.affiliation: AcureOmics AB, Umeå, Sweden.
authors.forename: Katrin
authors.initials: K
authors.lastname: Lundstedt-Enkel
authors.affiliation: AcureOmics AB, Umeå, Sweden.
authors.forename: Thomas
authors.initials: T
authors.lastname: Moritz
authors.affiliation: CNRS, UMR7622, 75005, Paris, France; Sorbonne Universités, UPMC, UMR7622, 75005, Paris, France; Inserm U-1156, Paris, France.
authors.forename: Cecile
authors.initials: C
authors.lastname: Haumaitre
authors.affiliation: Computational Life Science Cluster (CLiC), Department of Chemistry, Umeå University, Umeå, Sweden.
authors.forename: Johan
authors.initials: J
authors.lastname: Trygg
authors.affiliation: AcureOmics AB, Umeå, Sweden.
authors.forename: Torbjörn
authors.initials: T
authors.lastname: Lundstedt
mesh_terms: D000818:Animals; D008401:Gas Chromatography-Mass Spectrometry / Q000379:methods; D041981:Gastrointestinal Tract / Q000378:metabolism*; D007668:Kidney / Q000378:metabolism*; D008099:Liver / Q000378:metabolism*; D055442:Metabolome; D055432:Metabolomics / Q000379:methods*; D051379:Mice; D008962:Models, Theoretical; D018482:Muscle, Skeletal / Q000378:metabolism*; D010179:Pancreas / Q000378:metabolism*