Daniel Weindl

Complexity of dopamine metabolism

Parkinson’s disease (PD) coincides with a dramatic loss of dopaminergic neurons within the substantia nigra. A key player in the loss of dopaminergic neurons is oxidative stress. Dopamine (DA) metabolism itself is strongly linked to oxidative stress as its degradation generates reactive oxygen species (ROS) and DA oxidation can lead to endogenous neurotoxins whereas some DA derivatives show antioxidative effects. Therefore, DA metabolism is of special importance for neuronal redox-homeostasis and viability.In this review we highlight different aspects of dopamine metabolism in the context of PD and neurodegeneration. Since most reviews focus only on single aspects of the DA system, we will give a broader overview by looking at DA biosynthesis, sequestration, degradation and oxidation chemistry at the metabolic level, as well as at the transcriptional, translational and posttranslational regulation of all enzymes involved. This is followed by a short overview of cellular models currently used in PD research. Finally, we will address the topic from a medical point of view which directly aims to encounter PD.

Simultaneous extraction of proteins and metabolites from cells in culture

Graphical abstract Three-phase methanol–water–chloroform extraction for biological samples. Examples of components available from each phase are shown. These different phases can be then used for a variety of different analysis methods on different levels of cellular regulation.

How metabolites modulate metabolic flux

Adaptation to metabolic needs and changing environments is a basic requirement of every living system. These adaptations can be very quick and mild or slower but more drastic. In any case, cells have to constantly monitor their metabolic state and requirements. In this article we review general concepts as well as recent advances on how metabolites can regulate metabolic fluxes. We discuss how cells sense metabolite levels and how changing metabolite levels regulate metabolic enzymes on different levels, from specific allosteric regulation to global transcriptional regulation. We thereby focus on local metabolite sensing in mammalian cells and show that several major discoveries have only very recently been made.

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

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

Fragment Formula Calculator (FFC): Determination of Chemical Formulas for Fragment Ions in Mass Spectrometric Data

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, (13)C-metabolic flux analyses ((13)C-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 .

Isotopologue ratio normalization for non-targeted metabolomics

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.

Isotope Cluster-Based Compound Matching in Gas Chromatography/Mass Spectrometry for Non-Targeted Metabolomics

Gas chromatography coupled to mass spectrometry (GC/MS) has emerged as a powerful tool in metabolomics studies. A major bottleneck in current data analysis of GC/MS-based metabolomics studies is compound matching and identification, as current methods generate high rates of false positive and false-negative identifications. This is especially true for data sets containing a high amount of noise. In this work, a novel spectral similarity measure based on the specific fragmentation patterns of electron impact mass spectra is proposed. An important aspect of these algorithmic methods is the handling of noisy data. The performance of the proposed method compared to the dot product, the current gold standard, was evaluated on a complex biological data set. The analysis results showed significant improvements of the proposed method in compound matching and chromatogram alignment compared to the dot product.

Loss of DJ-1 impairs antioxidant response by altered glutamine and serine metabolism

The oncogene DJ-1 has been originally identified as a suppressor of PTEN. Further on, loss-of-function mutations have been described as a causative factor in Parkinsons disease (PD). DJ-1 has an important function in cellular antioxidant responses, but its role in central metabolism of neurons is still elusive. We applied stable isotope assisted metabolic profiling to investigate the effect of a functional loss of DJ-1 and show that DJ-1 deficient neuronal cells exhibit decreased glutamine influx and reduced serine biosynthesis. By providing precursors for GSH synthesis, these two metabolic pathways are important contributors to cellular antioxidant response. Down-regulation of these pathways, as a result of loss of DJ-1 leads to an impaired antioxidant response. Furthermore, DJ-1 deficient mouse microglia showed a weak but constitutive pro-inflammatory activation. The combined effects of altered central metabolism and constitutive activation of glia cells raise the susceptibility of dopaminergic neurons towards degeneration in patients harboring mutated DJ-1. Our work reveals metabolic alterations leading to increased cellular instability and identifies potential new intervention points that can further be studied in the light of novel translational medicine approaches.

MIA: non-targeted mass isotopolome analysis

Summary: MIA detects and visualizes isotopic enrichment in gas chromatography electron ionization mass spectrometry (GC–EI-MS) datasets in a non-targeted manner. It provides an easy-to-use graphical user interface that allows for visual mass isotopomer distribution analysis across multiple datasets. MIA helps to reveal changes in metabolic fluxes, visualizes metabolic proximity of isotopically enriched compounds and shows the fate of the applied stable isotope labeled tracer. Availability and Implementation: Linux and Windows binaries, documentation, and sample data are freely available for download at http://massisotopolomeanalyzer.lu. MIA is a stand-alone application implemented in C ++  and based on Qt5, NTFD and the MetaboliteDetector framework. Contact: karsten.hiller@uni.lu

PESTO: Parameter EStimation TOolbox

Abstract Summary PESTO is a widely applicable and highly customizable toolbox for parameter estimation in MathWorks MATLAB. It offers scalable algorithms for optimization, uncertainty and identifiability analysis, which work in a very generic manner, treating the objective function as a black box. Hence, PESTO can be used for any parameter estimation problem, for which the user can provide a deterministic objective function in MATLAB. Availability and implementation PESTO is a MATLAB toolbox, freely available under the BSD license. The source code, along with extensive documentation and example code, can be downloaded from https://github.com/ICB-DCM/PESTO/. Supplementary information Supplementary data are available at Bioinformatics online.