Integration of metabolomics, lipidomics and clinical data using a machine learning method

BMC Bioinformatics

Research

Zsuzsanna Ament¹  James A. West¹  Elizabeth Stanley¹  Animesh Acharjee²  Julian L. Griffin² 

[1] Medical Research Council, Elsie Widdowson Laboratory, 120 Fulbourn Road, CB1 9NL, Cambridge, UK;Medical Research Council, Elsie Widdowson Laboratory, 120 Fulbourn Road, CB1 9NL, Cambridge, UK;The Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, 80 Tennis Court Road, CB2 1GA, Cambridge, UK;

关键词: Random Forest;    Eicosanoid;    Ultra Performance Liquid Chromatography;    Random Forest Model;    Relative Liver Weight;   

DOI  :  10.1186/s12859-016-1292-2

来源: Springer

【 摘 要 】

BackgroundThe recent pandemic of obesity and the metabolic syndrome (MetS) has led to the realisation that new drug targets are needed to either reduce obesity or the subsequent pathophysiological consequences associated with excess weight gain. Certain nuclear hormone receptors (NRs) play a pivotal role in lipid and carbohydrate metabolism and have been highlighted as potential treatments for obesity. This realisation started a search for NR agonists in order to understand and successfully treat MetS and associated conditions such as insulin resistance, dyslipidaemia, hypertension, hypertriglyceridemia, obesity and cardiovascular disease. The most studied NRs for treating metabolic diseases are the peroxisome proliferator-activated receptors (PPARs), PPAR-α, PPAR-γ, and PPAR-δ. However, prolonged PPAR treatment in animal models has led to adverse side effects including increased risk of a number of cancers, but how these receptors change metabolism long term in terms of pathology, despite many beneficial effects shorter term, is not fully understood. In the current study, changes in male Sprague Dawley rat liver caused by dietary treatment with a PPAR-pan (PPAR-α, −γ, and –δ) agonist were profiled by classical toxicology (clinical chemistry) and high throughput metabolomics and lipidomics approaches using mass spectrometry.ResultsIn order to integrate an extensive set of nine different multivariate metabolic and lipidomics datasets with classical toxicological parameters we developed a hypotheses free, data driven machine learning approach. From the data analysis, we examined how the nine datasets were able to model dose and clinical chemistry results, with the different datasets having very different information content.ConclusionsWe found lipidomics (Direct Infusion-Mass Spectrometry) data the most predictive for different dose responses. In addition, associations with the metabolic and lipidomic data with aspartate amino transaminase (AST), a hepatic leakage enzyme to assess organ damage, and albumin, indicative of altered liver synthetic function, were established. Furthermore, by establishing correlations and network connections between eicosanoids, phospholipids and triacylglycerols, we provide evidence that these lipids function as a key link between inflammatory processes and intermediary metabolism.

【 授权许可】

CC BY   
© The Author(s). 2016