Bioinformatics Analysis of Targeted Metabolomics—Uncovering Old and New Tales of Diabetic Mice under Medication

Abstract

Metabolomics is a powerful tool for identifying both known and new disease-related perturbations in metabolic pathways. In preclinical drug testing, it has a high potential for early identification of drug off-target effects. Recent advances in high-precision high-throughput mass spectrometry have brought the metabolomic field to a point where quantitative, targeted, metabolomic measurements with ready-to-use kits allow for the automated in-house screening for hundreds of different metabolites in large sets of biological samples. Today, the field of metabolomics is, arguably, at a point where transcriptomics was about 5 yr ago. This being so, the field has a strong need for adapted bioinformatics tools and methods. In this paper we describe a systematic analysis of a targeted quantitative characterization of more than 800 metabolites in blood plasma samples from healthy and diabetic mice under rosiglitazone treatment. We show that known and new metabolic phenotypes of diabetes and medication can be recovered in a statistically objective manner. We find that concentrations of methylglutaryl carnitine are oppositely impacted by rosiglitazone treatment of both healthy and diabetic mice. Analyzing ratios between metabolite concentrations dramatically reduces the noise in the data set, allowing for the discovery of new potential biomarkers of diabetes, such as the N-hydroxyacyloylsphingosyl-phosphocholines SM(OH)28:0 and SM(OH)26:0. Using a hierarchical clustering technique on partial eta(2) values, we identify functionally related groups of metabolites, indicating a diabetes-related shift from lysophosphatidylcholine to phosphatidylcholine levels. The bioinformatics data analysis approach introduced here can be readily generalized to other drug testing scenarios and other medical disorders.

Questionnaire-based self-reported nutrition habits associate with serum metabolism as revealed by quantitative targeted metabolomics

“…As shown previously, the use of such ratios leads to a strong reduction in the overall variance and therefore improves the strength of association [13]. A high increase in the strength of association may also indicate that the two metabolites are linked by a metabolic pathway that is influenced by the respective (nutrition) factor.…”

“…The technique is described in detail in [27,28] published by the FDA (Food and Drug Administration), which implies proof of reproducibility within a given error range. This measurement platform has been successfully used in the past in different academic and industrial applications [12][13][14].…”

“…Using quantitative targeted metabolomics as an access to intermediate phenotypes (as described in detail in [12]) provides a much closer insight into the mechanisms that cause the clinical endpoint. Using high resolution electrospray ionization tandem mass spectrometry (ESI-MS/MS) it is, today, possible to measure hundreds of metabolites at the same time in a single sample [13]. Gieger et al [12] showed that associating genetic variants with changes in the homeostasis of metabolic compounds can yield larger effect sizes as well as access to the underlying molecular disease-causing mechanisms.…”

Current status on genome–metabolome-wide associations: an opportunity in nutrition research

“…Genetic variants that affect the efficiency of a specific enzyme should be best reflected in the ratio of this enzyme's relative substrates and products rather than in the absolute concentration of either of them. This metabolite ratio approach was introduced by Altmaier et al (2008), and its utility was demonstrated by a pronounced increase in the strength of the association that was observed for FADS1. Here, changes in the catalytic activity of FADS1 due to its polymorphism may alter the levels of eicosatrienoyl-CoA (C20:3) and arachidonyl-CoA (C20:4), which in turn translates into changes on the PC 36:3 and PC 36:4 concentrations.…”

Mass Spectrometry OMICS Approach to Study Medicinal Chemical Molecular Responses on Living Organisms

“…MS has been now established as the stateof-the-art analytical tool in which richest data from molecular systems can be acquired. In medicinal chemistry, three MS approaches might be highlighted as the most prominent: data dependent UPLC-ESI-HRMS metabolomics [3], data dependent nanoLC-nanoESI-HRMS proteomics [4] and MS imaging applied to the biomarker screening of tissues [5].Metabolomic MS screening can usually be categorized into untargeted [6] and targeted analysis [7]. In untargeted metabolomics, as many molecules as possible are identified in a sample.…”