Yeji Sung1, Konstantina Spagou2, Marina Kafeza1, Michael Kyriakides2, Brahman Dharmarajah1, Joseph Shalhoub1, Jose A Diaz3, Thomas W Wakefield3, Elaine Holmes2, Alun H Davies4. 1. Academic Section of Vascular Surgery, Department of Surgery and Cancer, Imperial College London, UK. 2. Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, UK. 3. Conrad Jobst Vascular Research Laboratories, University of Michigan Medical School, MI, USA. 4. Academic Section of Vascular Surgery, Department of Surgery and Cancer, Imperial College London, UK. Electronic address: a.h.davies@imperial.ac.uk.
Abstract
OBJECTIVES: Deep vein thrombosis (DVT) is a major health problem, responsible for significant morbidity and mortality. The identification of a simple and effective diagnostic biomarker of DVT remains a challenge. Metabolomics have recently emerged as a new powerful scientific tool to characterise metabolic phenotypes of complex diseases and investigate small molecules in biofluids. The aim of the study was to identify the blood and vein wall metabolomic signature of DVT in a murine experimental model. METHODS: An established inferior vena cava ligation mouse model of DVT (n=10) was used and compared with sham surgery controls (n=10). Comprehensive untargeted metabolic profiling of serum and vein wall extracts was undertaken using liquid chromatography coupled mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) spectroscopy. RESULTS: Multivariate and univariate statistical analysis demonstrated a differential metabolic profile when comparing DVT mice and control animals. Serum from DVT mice was characterised by differential concentrations of adenosine (decreased in DVT mice 9.6 fold), adenine (decreased 10.6 fold), and tricyclic acid cycle (TCA) intermediates, including citrate, succinate, and fumarate (1.5, 2.3, and 2.8 fold decreases, respectively). l-carnitine was found to be of greater abundance in the serum of DVT animals (67.0 fold change). A number of lipid moiety classes, including sphingomyelins, phosphatidylcholines, and triglycerides, were differentially abundant. Several metabolites were found in vein wall, including acetylcarnitine (increased in DVT mice 1.9 fold), adenosine (increased 2.2 fold), and ceramide (increased 2.7 fold). Correlation analysis illustrated the biochemical relationships between assigned metabolites, with the discriminatory molecules being highly correlated with each other, in both serum and vein wall. CONCLUSIONS: The present findings demonstrate that metabolic dysregulations in DVT centre on energy metabolism, sphingolipid, and adenosine metabolism, representing a DVT specific metabolite signature in a murine experimental model.
OBJECTIVES:Deep vein thrombosis (DVT) is a major health problem, responsible for significant morbidity and mortality. The identification of a simple and effective diagnostic biomarker of DVT remains a challenge. Metabolomics have recently emerged as a new powerful scientific tool to characterise metabolic phenotypes of complex diseases and investigate small molecules in biofluids. The aim of the study was to identify the blood and vein wall metabolomic signature of DVT in a murine experimental model. METHODS: An established inferior vena cava ligation mouse model of DVT (n=10) was used and compared with sham surgery controls (n=10). Comprehensive untargeted metabolic profiling of serum and vein wall extracts was undertaken using liquid chromatography coupled mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) spectroscopy. RESULTS: Multivariate and univariate statistical analysis demonstrated a differential metabolic profile when comparing DVT mice and control animals. Serum from DVT mice was characterised by differential concentrations of adenosine (decreased in DVT mice 9.6 fold), adenine (decreased 10.6 fold), and tricyclic acid cycle (TCA) intermediates, including citrate, succinate, and fumarate (1.5, 2.3, and 2.8 fold decreases, respectively). l-carnitine was found to be of greater abundance in the serum of DVT animals (67.0 fold change). A number of lipid moiety classes, including sphingomyelins, phosphatidylcholines, and triglycerides, were differentially abundant. Several metabolites were found in vein wall, including acetylcarnitine (increased in DVT mice 1.9 fold), adenosine (increased 2.2 fold), and ceramide (increased 2.7 fold). Correlation analysis illustrated the biochemical relationships between assigned metabolites, with the discriminatory molecules being highly correlated with each other, in both serum and vein wall. CONCLUSIONS: The present findings demonstrate that metabolic dysregulations in DVT centre on energy metabolism, sphingolipid, and adenosine metabolism, representing a DVT specific metabolite signature in a murine experimental model.