Exploratory GC/MS-Based Metabolomics of Body Fluids

Dengue and metabolomics in humans

BACKGROUND

Dengue virus causes dengue fever (DF)disease, transmitted by the mosquito Aedes aegypti. The symptoms could be severe and disable the affected individuals for weeks. The severe form, dengue hemorrhagic fever (DHF), can lead to death if not adequately attended to. Due to global warming, the vector mosquito will advance over new areas and expose more people to this disease over the next decades. Despite the severity, there are no treatments nor efficient vaccines available. Metabolomic studies have shown a new perspective to understand this disease better at a new molecular level.

AIM OF REVIEW

Many published works rely on samples obtained from animal studies. This review will mainly focus on human samples and cell culture experiments to view how the dengue virus affects the metabolomic profile.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The review compiles the sample sources, metabolomic techniques used, the detected compounds, and how they behave in different DF stages. This disease causes a significant change in many metabolites, but some results are still conflicting between studies. The results gathered here show that metabolomic approaches prove to be an excellent and viable way to expand knowledge about DF.

A Metabolomics Workflow for Analyzing Complex Biological Samples Using a Combined Method of Untargeted and Target-List Based Approaches

In the highly dynamic field of metabolomics, we have developed a method for the analysis of hydrophilic metabolites in various biological samples. Therefore, we used hydrophilic interaction chromatography (HILIC) for separation, combined with a high-resolution mass spectrometer (MS) with the aim of separating and analyzing a wide range of compounds. We used 41 reference standards with different chemical properties to develop an optimal chromatographic separation. MS analysis was performed with a set of pooled biological samples human cerebrospinal fluid (CSF), and human plasma. The raw data was processed in a first step with Compound Discoverer 3.1 (CD), a software tool for untargeted metabolomics with the aim to create a list of unknown compounds. In a second step, we combined the results obtained with our internally analyzed reference standard list to process the data along with the Lipid Data Analyzer 2.6 (LDA), a software tool for a targeted approach. In order to demonstrate the advantages of this combined target-list based and untargeted approach, we not only compared the relative standard deviation (%RSD) of the technical replicas of pooled plasma samples (n = 5) and pooled CSF samples (n = 3) with the results from CD, but also with XCMS Online, a well-known software tool for untargeted metabolomics studies. As a result of this study we could demonstrate with our HILIC-MS method that all standards could be either separated by chromatography, including isobaric leucine and isoleucine or with MS by different mass. We also showed that this combined approach benefits from improved precision compared to well-known metabolomics software tools such as CD and XCMS online. Within the pooled plasma samples processed by LDA 68% of the detected compounds had a %RSD of less than 25%, compared to CD and XCMS online (57% and 55%). The improvements of precision in the pooled CSF samples were even more pronounced, 83% had a %RSD of less than 25% compared to CD and XCMS online (28% and 8% compounds detected). Particularly for low concentration samples, this method showed a more precise peak area integration with its 3D algorithm and with the benefits of the LDAs graphical user interface for fast and easy manual curation of peak integration. The here-described method has the advantage that manual curation for larger batch measurements remains minimal due to the target list containing the information obtained by an untargeted approach.