Isotopologue ratio normalization for non-targeted metabolomics

in Journal of Chromatography. A (2015), 1389

Robust quantification of analytes is a prerequisite for meaningful metabolomics experiments. In non-targeted metabolomics it is still hard to compare measurements across multiple batches or instruments ... [more ▼]

Robust quantification of analytes is a prerequisite for meaningful metabolomics experiments. In non-targeted metabolomics it is still hard to compare measurements across multiple batches or instruments. For targeted analyses isotope dilution mass spectrometry is used to provide a robust normalization reference. Here, we present an approach that allows for the automated semi-quantification of metabolites relative to a fully stable isotope-labeled metabolite extract. Unlike many previous approaches, we include both identified and unidentified compounds in the data analysis. The internal standards are detected in an automated manner using the non-targeted tracer fate detection algorithm. The ratios of the light and heavy form of these compounds serve as a robust measure to compare metabolite levels across different mass spectrometric platforms. As opposed to other methods which require high resolution mass spectrometers, our methodology works with low resolution mass spectrometers as commonly used in gas chromatography electron impact mass spectrometry (GC–EI-MS)-based metabolomics. We demonstrate the validity of our method by analyzing compound levels in different samples and show that it outperforms conventional normalization approaches in terms of intra- and inter-instrument reproducibility. We show that a labeled yeast metabolite extract can also serve as a reference for mammalian metabolite extracts where complete stable isotope labeling is hard to achieve. [less ▲]

Role of HIF-1α in Oncostatin M induced metabolic reprogramming of human hepatocytes

Bachelor/master dissertation (2015)

Metabolome-wide analysis of stable isotope labeling - Is it worth the effort?

in Frontiers in Physiology (2015), 6(344),

Fragment Formula Calculator (FFC): Determination of chemical formulas for fragment ions in mass spectrometric data

in Analytical Chemistry (2014), 86(4), 22212228

The accurate determination of mass isotopomer distributions (MID) is of great significance for stable isotope-labeling experiments. Most commonly, MIDs are derived from gas chromatography/electron ... [more ▼]

The accurate determination of mass isotopomer distributions (MID) is of great significance for stable isotope-labeling experiments. Most commonly, MIDs are derived from gas chromatography/electron ionization mass spectrometry (GC/EI-MS) measurements. The analysis of fragment ions formed during EI, which contain only specific parts of the original molecule can provide valuable information on the positional distribution of the label. The chemical formula of a fragment ion is usually applied to derive the correction matrix for accurate MID calculation. Hence, the correct assignment of chemical formulas to fragment ions is of crucial importance for correct MIDs. Moreover, the positional distribution of stable isotopes within a fragment ion is of high interest for stable isotope-assisted metabolomics techniques. For example, 13C-metabolic flux analyses (13C-MFA) are dependent on the exact knowledge of the number and position of retained carbon atoms of the unfragmented molecule. Fragment ions containing different carbon atoms are of special interest, since they can carry different flux information. However, the process of mass spectral fragmentation is complex, and identifying the substructures and chemical formulas for these fragment ions is nontrivial. For that reason, we developed an algorithm, based on a systematic bond cleavage, to determine chemical formulas and retained atoms for EI derived fragment ions. Here, we present the fragment formula calculator (FFC) algorithm that can calculate chemical formulas for fragment ions where the chemical bonding (e.g., Lewis structures) of the intact molecule is known. The proposed algorithm is able to cope with general molecular rearrangement reactions occurring during EI in GC/MS measurements. The FFC algorithm is able to integrate stable isotope labeling experiments into the analysis and can automatically exclude candidate formulas that do not fit the observed labeling patterns.1 We applied the FFC algorithm to create a fragment ion repository that contains the chemical formulas and retained carbon atoms of a wide range of trimethylsilyl and tert-butyldimethylsilyl derivatized compounds. In total, we report the chemical formulas and backbone carbon compositions for 160 fragment ions of 43 alkylsilyl-derivatives of primary metabolites. Finally, we implemented the FFC algorithm in an easy-to-use graphical user interface and made it publicly available at http://www.ffc.lu. [less ▲]

Immune-responsive gene 1 protein links metabolism to immunity by catalyzing itaconic acid production

in Proceedings of the National Academy of Sciences of the United States of America (2013)

Immunoresponsive gene 1 (Irg1) is highly expressed in mammalian macrophages during inflammation, but its biological function has not yet been elucidated. Here, we identify Irg1 as the gene coding for an ... [more ▼]

Immunoresponsive gene 1 (Irg1) is highly expressed in mammalian macrophages during inflammation, but its biological function has not yet been elucidated. Here, we identify Irg1 as the gene coding for an enzyme producing itaconic acid (also known as methylenesuccinic acid) through the decarboxylation of cis-aconitate, a tricarboxylic acid cycle intermediate. Using a gain-and-loss-of-function approach in both mouse and human immune cells, we found Irg1 expression levels correlating with the amounts of itaconic acid, a metabolite previously proposed to have an antimicrobial effect. We purified IRG1 protein and identified its cis-aconitate decarboxylating activity in an enzymatic assay. Itaconic acid is an organic compound that inhibits isocitrate lyase, the key enzyme of the glyoxylate shunt, a pathway essential for bacterial growth under specific conditions. Here we show that itaconic acid inhibits the growth of bacteria expressing isocitrate lyase, such as Salmonella enterica and Mycobacterium tuberculosis. Furthermore, Irg1 gene silencing in macrophages resulted in significantly decreased intracellular itaconic acid levels as well as significantly reduced antimicrobial activity during bacterial infections. Taken together, our results demonstrate that IRG1 links cellular metabolism with immune defense by catalyzing itaconic acid production. [less ▲]

NTFD - A stand-alone application for the non-targeted detection of stable isotope labeled compounds in GC/MS data.

in Bioinformatics (2013), 29(9), 1226-8

SUMMARY: Most current stable isotope-based methodologies are targeted and focus only on the well-described aspects of metabolic networks. Here, we present NTFD (non-targeted tracer fate detection), a ... [more ▼]

SUMMARY: Most current stable isotope-based methodologies are targeted and focus only on the well-described aspects of metabolic networks. Here, we present NTFD (non-targeted tracer fate detection), a software for the non-targeted analysis of all detectable compounds derived from a stable isotope-labeled tracer present in a GC/MS dataset. In contrast to traditional metabolic flux analysis approaches, NTFD does not depend on any a priori knowledge or library information. To obtain dynamic information on metabolic pathway activity, NTFD determines mass isotopomer distributions for all detected and labeled compounds. These data provide information on relative fluxes in a metabolic network. The graphical user interface allows users to import GC/MS data in netCDF format and export all information into a tab-separated format. AVAILABILITY: NTFD is C++- and Qt4-based, and it is freely available under an open-source license. Pre-compiled packages for the installation on Debian- and Redhat-based Linux distributions, as well as Windows operating systems, along with example data, are provided for download at http://ntfd.mit.edu/. CONTACT: gregstep@mit.edu. [less ▲]

The Application of Stable Isotope Assisted Metabolomics in Biomedicine

in Current Biotechnology (2012), 1

During the last years, metabolomics has been established as a standard technique in biomedical research to analyze changes in metabolite levels. Currently, mass spectrometry (MS) and nuclear magnetic ... [more ▼]

During the last years, metabolomics has been established as a standard technique in biomedical research to analyze changes in metabolite levels. Currently, mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (NMR) are the two major technologies to acquire metabolomics data. These technologies have been proven to be invaluable tools for the detection of disease related metabolic biomarkers. However, the obtained data only describe static metabolite concentrations and do not provide information about the dynamics of the system. Based on stable-isotope assisted metabolomics experiments, metabolic flux analysis (MFA) intends to quantitatively analyze intracellular metabolite conversion rates, thus providing a readout of enzyme activities. Although many studies have been published about disease related metabolomics, only a few publications about stable-isotope assisted metabolomics related to biomedicine are available. Especially in the context of personalized medicine, stable-isotope assisted technologies will become more important, since they provide patient and disease specific information about the metabolic state of the patient. In the following review we will point out the importance of stable-isotope related technologies for biomedical sciences. First, we will introduce analytical techniques required for metabolomics and MFA. In the second part, two biomedicine related stable-isotope based studies are summarized. [less ▲]