Interplay of Metallome and Metabolome in Amyotrophic Lateral Sclerosis: A Study on Cerebrospinal Fluid of Patients Carrying Disease-Related Gene Mutations.

Amyotrophic lateral sclerosis (ALS) is a lethal progressive neurodegenerative disease, characterized by a loss of function of upper and lower motor neurons. This study aimed to explore probable pathological alterations occurring in individuals with ALS compared to neurologically healthy controls through the analysis of cerebrospinal fluid (CSF), a medium, which directly interacts with brain parenchyma. A total of 7 ALS patients with disease-associated mutations (ATXN2, C9ORF72, FUS, SOD1, and TARDBP) and 13 controls were included in the study. Multiple analytical approaches were employed, including metabolomic and metallomics profiling, as well as genetic screening, using CSF samples obtained from the brain compartment. Data analysis involved the application of multivariate statistical methods. Advanced hyphenated selenium and redox metal (iron, copper, and manganese) speciation techniques and nontargeted Fourier transform ion cyclotron resonance mass spectrometry-based metabolomics were used for data acquisition. Nontargeted metabolomics showed reduced steroids, including sex hormones; additionally, copper and manganese species were found to be the most relevant features for ALS patients. This indicates a potential alteration of sex hormone pathways in the ALS-affected brain, as reflected in the CSF.

Large-Scale Interlaboratory DI-FT-ICR MS Comparability Study Employing Various Systems.

Ultrahigh resolution mass spectrometry (UHR-MS) coupled with direct infusion (DI) electrospray ionization offers a fast solution for accurate untargeted profiling. Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometers have been shown to produce a wealth of insights into complex chemical systems because they enable unambiguous molecular formula assignment even if the vast majority of signals is of unknown identity. Interlaboratory comparisons are required to apply this type of instrumentation in quality control (for food industry or pharmaceuticals), large-scale environmental studies, or clinical diagnostics. Extended comparisons employing different FT-ICR MS instruments with qualitative direct infusion analysis are scarce since the majority of detected compounds cannot be quantified. The extent to which observations can be reproduced by different laboratories remains unknown. We set up a preliminary study which encompassed a set of 17 laboratories around the globe, diverse in instrumental characteristics and applications, to analyze the same sets of extracts from commercially available standard human blood plasma and Standard Reference Material (SRM) for blood plasma (SRM1950), which were delivered at different dilutions or spiked with different concentrations of pesticides. The aim of this study was to assess the extent to which the outputs of differently tuned FT-ICR mass spectrometers, with different technical specifications, are comparable for setting the frames of a future DI-FT-ICR MS ring trial. We concluded that a cluster of five laboratories, with diverse instrumental characteristics, showed comparable and representative performance across all experiments, setting a reference to be used in a future ring trial on blood plasma.

Feature Selection Pipelines with Classification for Non-targeted Metabolomics Combining the Neural Network and Genetic Algorithm.

Non-targeted metabolomics via high-resolution mass spectrometry methods, such as direct infusion Fourier transform-ion cyclotron resonance mass spectrometry (DI-FT-ICR MS), produces data sets with thousands of features. By contrast, the number of samples is in general substantially lower. This disparity presents challenges when analyzing non-targeted metabolomics data sets and often requires custom methods to uncover information not always accessible via classical statistical techniques. In this work, we present a pipeline that combines a convolutional neural network with traditional statistical approaches and an adaptation of a genetic algorithm. The developed method was applied to a lifestyle intervention cohort data set, where subjects at risk of type 2 diabetes underwent an oral glucose tolerance test. Feature selection is the final result of the pipeline, achieved through classification of the data set via a neural network, with a precision-recall score of over 0.9 on the test set. The features most relevant for the described classification were then chosen via a genetic algorithm. The output of the developed pipeline encompasses approximately 200 features with high predictive scores, providing a fingerprint of the metabolic changes in the prediabetic class on the data set. Our framework presents a new approach which allows to apply complex modeling based on convolutional neural networks for the analysis of high-resolution mass spectrometric data.

Gfi1 Loss Protects against Two Models of Induced Diabetes.

Background: Although several approaches have revealed much about individual factors that regulate pancreatic development, we have yet to fully understand their complicated interplay during pancreas morphogenesis. Gfi1 is transcription factor specifically expressed in pancreatic acinar cells, whose role in pancreas cells fate identity and specification is still elusive. Methods: In order to gain further insight into the function of this factor in the pancreas, we generated animals deficient for Gfi1 specifically in the pancreas. Gfi1 conditional knockout animals were phenotypically characterized by immunohistochemistry, RT-qPCR, and RNA scope. To assess the role of Gfi1 in the pathogenesis of diabetes, we challenged Gfi1-deficient mice with two models of induced hyperglycemia: long-term high-fat/high-sugar feeding and streptozotocin injections. Results: Interestingly, mutant mice did not show any obvious deleterious phenotype. However, in depth analyses demonstrated a significant decrease in pancreatic amylase expression, leading to a diminution in intestinal carbohydrates processing and thus glucose absorption. In fact, Gfi1-deficient mice were found resistant to diet-induced hyperglycemia, appearing normoglycemic even after long-term high-fat/high-sugar diet. Another feature observed in mutant acinar cells was the misexpression of ghrelin, a hormone previously suggested to exhibit anti-apoptotic effects on ß-cells in vitro. Impressively, Gfi1 mutant mice were found to be resistant to the cytotoxic and diabetogenic effects of high-dose streptozotocin administrations, displaying a negligible loss of ß-cells and an imperturbable normoglycemia. Conclusions: Together, these results demonstrate that Gfi1 could turn to be extremely valuable for the development of new therapies and could thus open new research avenues in the context of diabetes research.

Linking the FTO obesity rs1421085 variant circuitry to cellular, metabolic, and organismal phenotypes in vivo.

Variants in FTO have the strongest association with obesity; however, it is still unclear how those noncoding variants mechanistically affect whole-body physiology. We engineered a deletion of the rs1421085 conserved cis-regulatory module (CRM) in mice and confirmed in vivo that the CRM modulates Irx3 and Irx5 gene expression and mitochondrial function in adipocytes. The CRM affects molecular and cellular phenotypes in an adipose depot-dependent manner and affects organismal phenotypes that are relevant for obesity, including decreased high-fat diet-induced weight gain, decreased whole-body fat mass, and decreased skin fat thickness. Last, we connected the CRM to a genetically determined effect on steroid patterns in males that was dependent on nutritional challenge and conserved across mice and humans. Together, our data establish cross-species conservation of the rs1421085 regulatory circuitry at the molecular, cellular, metabolic, and organismal level, revealing previously unknown contextual dependence of the variant's action.

An Enhanced Isotopic Fine Structure Method for Exact Mass Analysis in Discovery Metabolomics: FIA-CASI-FTMS.

A major bottleneck in metabolomics is the annotation of a molecular formula as a first step to a tentative structure assignment of known and unknown metabolites. The direct observation of an isotopic fine structure (IFS) provides the ability to confidently assign an unknown's molecular formula out of a complex mass spectrum. However, the majority of mass spectrometers deployed for metabolomic studies do not have sufficient resolving power and high-fidelity isotope ratios in the mass range of interest to determine molecular formulas from IFS data. To increase the number of unknowns for which IFS can be determined, a segmented "boxcar" approach using a selection quadrupole as a broadband mass filter is used. In this longer, enhanced dynamic range discovery experiment, selected ions in a specific mass range are accumulated before detection by the analyzer cell. The mass filter window is then moved across the entire mass range resulting in a composite mass spectrum covering the m/z range of interest for phenomics research. The effectiveness of the FIA-CASI-FTMS workflow utilizing IFS for molecular formula assignment is realized with the implementation of the dynamically harmonized cell, which distinguishes the approach from other segmented workflows because of the analytical properties of the cell. The discovery approach was applied to a human plasma sample to confidently assign an unknown molecular formula as part of the quest to illuminate its metabolic "dark matter" via high-fidelity IFS ratio determinations. The FIA-CASI-FTMS workflow showed a 2.6-fold increase in both matching with the Human Metabolome Database and an increase in the IFS pattern.

Mass Difference Maps and Their Application for the Recalibration of Mass Spectrometric Data in Nontargeted Metabolomics.

Modern high-resolution mass spectrometry provides the great potential to analyze exact masses of thousands of molecules in one run. In addition, the high instrumental mass accuracy allows for high-precision formula assignments narrowing down tremendously the chemical space of unknown compounds. The adequate values for a mass accuracy are normally achieved by a proper calibration procedure that usually implies using known internal or external standards. This approach might not always be sufficient in cases when systematic error is highly prevalent. Therefore, additional recalibration steps are required. In this work, the concept of mass difference maps (MDiMs) is introduced with a focus on the visualization and investigation of all the pairwise differences between considered masses. Given an adequate reference list of sufficient size, MDiMs can facilitate the detection of a systematic error component. Such a property can be potentially applied for spectral recalibration. Consequently, a novel approach to describe the process of the correction of experimentally derived masses is presented. The method is based on the estimation of the density of data points on MDiMs using Gaussian kernels followed by a curve fitting with an adapted version of the particle swarm optimization algorithm. The described recalibration procedure is examined on simulated as well as real mass spectrometric data. For the latter case, blood plasma samples were analyzed by Fourier transform ion cyclotron resonance mass spectrometry. Nevertheless, due to its inherent flexibility, the method can be easily extended to other low- and high-resolution platforms and/or sample types.

Metabolomic investigations in cerebrospinal fluid of Parkinson's disease.

The underlying mechanisms of Parkinson´s disease are not completely revealed. Especially, early diagnostic biomarkers are lacking. To characterize early pathophysiological events, research is focusing on metabolomics. In this case-control study we investigated the metabolic profile of 31 Parkinson´s disease-patients in comparison to 95 neurologically healthy controls. The investigation of metabolites in CSF was performed by a 12 Tesla SolariX Fourier transform-ion cyclotron resonance-mass spectrometer (FT-ICR-MS). Multivariate statistical analysis sorted the most important biomarkers in relation to their ability to differentiate Parkinson versus control. The affected metabolites, their connection and their conversion pathways are described by means of network analysis. The metabolic profiling by FT-ICR-MS in CSF yielded in a good group separation, giving insights into the disease mechanisms. A total number of 243 metabolites showed an affected intensity in Parkinson´s disease, whereas 15 of these metabolites seem to be the main biological contributors. The network analysis showed a connection to the tricarboxylic cycle (TCA cycle) and therefore to mitochondrial dysfunction and increased oxidative stress within mitochondria. The metabolomic analysis of CSF in Parkinson´s disease showed an association to pathways which are involved in lipid/ fatty acid metabolism, energy metabolism, glutathione metabolism and mitochondrial dysfunction.

Digging into the low molecular weight peptidome with the OligoNet web server.

Bioactive peptides play critical roles in regulating many biological processes. Recently, natural short peptides biomarkers are drawing significant attention and are considered as "hidden treasure" of drug candidates. High resolution and high mass accuracy provided by mass spectrometry (MS)-based untargeted metabolomics would enable the rapid detection and wide coverage of the low-molecular-weight peptidome. However, translating unknown masses (<1 500 Da) into putative peptides is often limited due to the lack of automatic data processing tools and to the limit of peptide databases. The web server OligoNet responds to this challenge by attempting to decompose each individual mass into a combination of amino acids out of metabolomics datasets. It provides an additional network-based data interpretation named "Peptide degradation network" (PDN), which unravels interesting relations between annotated peptides and generates potential functional patterns. The ab initio PDN built from yeast metabolic profiling data shows a great similarity with well-known metabolic networks, and could aid biological interpretation. OligoNet allows also an easy evaluation and interpretation of annotated peptides in systems biology, and is freely accessible at https://daniellyz200608105.shinyapps.io/OligoNet/ .

Challenges of metabolomics in human gut microbiota research.

The review highlights the role of metabolomics in studying human gut microbial metabolism. Microbial communities in our gut exert a multitude of functions with huge impact on human health and disease. Within the meta-omics discipline, gut microbiome is studied by (meta)genomics, (meta)transcriptomics, (meta)proteomics and metabolomics. The goal of metabolomics research applied to fecal samples is to perform their metabolic profiling, to quantify compounds and classes of interest, to characterize small molecules produced by gut microbes. Nuclear magnetic resonance spectroscopy and mass spectrometry are main technologies that are applied in fecal metabolomics. Metabolomics studies have been increasingly used in gut microbiota related research regarding health and disease with main focus on understanding inflammatory bowel diseases. The elucidated metabolites in this field are summarized in this review. We also addressed the main challenges of metabolomics in current and future gut microbiota research. The first challenge reflects the need of adequate analytical tools and pipelines, including sample handling, selection of appropriate equipment, and statistical evaluation to enable meaningful biological interpretation. The second challenge is related to the choice of the right animal model for studies on gut microbiota. We exemplified this using NMR spectroscopy for the investigation of cross-species comparison of fecal metabolite profiles. Finally, we present the problem of variability of human gut microbiota and metabolome that has important consequences on the concepts of personalized nutrition and medicine.

Solutions for low and high accuracy mass spectrometric data matching: a data-driven annotation strategy in nontargeted metabolomics.

Ultra high pressure liquid chromatography coupled to mass spectrometry (UHPLC-MS) has become a widespread analytical technique in metabolomics investigations, however the benefit of high-performance chromatographic separation is often blunted due to insufficient mass spectrometric accuracy. A strategy that allows for the matching of UHPLC-MS data to highly accurate direct infusion electrospray ionization (DI-ESI) Fourier transform ion cyclotron resonance/mass spectrometry (FTICR/MS) data is developed in this manuscript. Mass difference network (MDiN) based annotation of FTICR/MS data and matching to unique UHPLC-MS peaks enables the consecutive annotation of the chromatographic data set. A direct comparison of experimental m/z values provided no basis for the matching of both platforms. The matching of annotation-based exact neutral masses finally enabled the integration of platform specific multivariate statistical evaluations, minimizing the danger to compare artifacts generated on either platform. The approach was developed on a non-alcoholic fatty liver disease (NAFLD) data set.

The compositional space of exhaled breath condensate and its link to the human breath volatilome.

Breath analysis is commonly understood to target gaseous or volatile organic compounds (VOCs) for the characterization of different pathologies. Targeted analysis is most effective if a working hypothesis can be based on a plethora of data. The recently published volatilome builds an optimal basis for organizing powerful target sets. However, the origin and pathways of biosynthesis of many VOCs are not known, which complicates the formulation of useful hypotheses. To find the missing link between VOCs and their origin, it is necessary to analyze their precursor fluids themselves. In order to provide condensation nuclei for the generation of future hypotheses, we provide the compositional space over 23 samples of the unperturbed human exhaled breath condensate (EBC) metabolome. We propose a way to connect the compositional spaces of both VOCs and EBC so as to gain insight into the most probable form of VOC precursors. In a way analogous to tandem MS it is possible to create a mass difference network over compositional data by linking compositions with mass differences that are designed to mimic biochemical reactions. We propose to use mass difference enrichment analysis (MDEA) in order to mine probable relations between VOCs and their precursor fluids. We have found 2691 EBC compositions and linked them to 235 breath VOC compositions that correspond to 848 individual compounds. We found that VOCs are likely to be found as hexose conjugates or as amino acid conjugates with Glutamine or Asparagine playing a major role. Furthermore, we found that dicarboxylic acid mass differences may be more indicative for oxidative stress than oxygenation-hydrogenation sequences.

Sensitivity improvement in hydrophilic interaction chromatography negative mode electrospray ionization mass spectrometry using 2-(2-methoxyethoxy)ethanol as a post-column modifier for non-targeted metabolomics.

The application of ammonia acetate buffered liquid chromatography (LC) eluents is known to concomitantly lead to ion suppression when electrospray ionization mass spectrometry (ESI-MS) detection is used. In negative ESI mode, post column infusion of 2-(2-methoxyethoxy)ethanol (2-MEE) was shown in the literature to help to compensate this adverse effect occurring in reversed phase liquid chromatography mass spectrometry (RP-LC-MS) analyses. Here a setup of direct infusion and hydrophilic interaction chromatography (HILIC) post-column infusion experiments was established in order to investigate systematically the beneficial effects of 2-MEE. We demonstrate that, 2-MEE can help to improve ESI-MS sensitivity in HILIC too and reveal analyte structure specific behaviors. Our study indicates that 2-MEE especially improves ESI response for small and polar molecules. The ESI response of stable isotope labeled amino acids spiked into biological matrices increases up to 50-fold (i.e. D5-l-glutamic acid) when post column infusion of 2-MEE is applied. A non-targeted analysis of a pooled urine sample via HILIC-ESI-QTOF-MS supports this hypothesis. In direct infusion, the combined application of an ammonia acetate buffered solution together with 2-MEE results in an improved ESI response compared to a non-buffered solution. We observed up to 60-fold increased ESI response of l-lysine. We propose this effect is putatively caused by the formation of smaller ESI droplets and stripping of positive charge from ESI droplets due to evaporation of acetic acid anions. In summary, post-column infusion of 2-MEE especially enhances ESI response of small and polar molecules. Therefore it can be regarded as a valuable add-on in targeted or non-targeted metabolomic HILIC-MS studies since this method sets a focus on this molecule category.

Dimer ion formation and intermolecular fragmentation of 1,2-diacylglycerols revealed by electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry for more comprehensive lipid analysis.

RATIONALE: The ionization of neutral diacylglycerols (DAGs) by electrospray ionization mass spectrometry (ESI-MS) is challenging compared with other lipid classes which possess ionic head group conjugations. Although ESI-MS is the method of choice in lipidomic analysis, it is questionable whether all lipid classes can be efficiently ionized by this method. Actually, various lipids were not efficiently detected (due to poor ionization) in many studies which claimed to comprehensively describe lipid profiles. Since neutral lipids are precursors for the biosynthesis of most other lipid classes, the necessity for improved or alternative ionization and identification schemes becomes obvious. METHODS: We identified the 1,2-diacylglycerol (DAG) dimer ion formation in the gas phase by ultra-high-resolution Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) in negative electrospray ionization ((-)ESI) mode. The geometry of the dimer ion was investigated by accurate density functional theory (DFT) calculations at the B3LYP/6-311+G(d)//B3LYP/LANL2DZ level of theory. Fragmentation of the dimer ions of many investigated DAGs has been achieved via collision-induced dissociation (CID) experiments with several elevated collision energies (0-12 eV). RESULTS: We revealed the possibility to ionize neutral DAGs as dimer ions in the negative ESI mode. Quantum mechanical calculations revealed a polar head-to-head intermolecular interaction between one charged DAG and one DAG neutral. This represents an energy minimum structure for the DAG dimer ions. We could furthermore detect CID fragmentation product ions that can only result from intermolecular reactions in this head-to-head conformation (SN2 nucleophilic substitution reactions inside the dimer DAG ion). CONCLUSIONS: Here, we present for the first time the opportunity to ionize and identify DAGs as dimer ions. This new finding provides a new alternative for investigations of important diacylglycerol lipids and provides the opportunity to obtain complementary and more comprehensive results in future lipidomic studies.

The root extract of the medicinal plant Pelargonium sidoides is a potent HIV-1 attachment inhibitor.

Global HIV-1 treatment would benefit greatly from safe herbal medicines with scientifically validated novel anti-HIV-1 activities. The root extract from the medicinal plant Pelargonium sidoides (PS) is licensed in Germany as the herbal medicine EPs®7630, with numerous clinical trials supporting its safety in humans. Here we provide evidence from multiple cell culture experiments that PS extract displays potent anti-HIV-1 activity. We show that PS extract protects peripheral blood mononuclear cells and macrophages from infection with various X4 and R5 tropic HIV-1 strains, including clinical isolates. Functional studies revealed that the extract from PS has a novel mode-of-action. It interferes directly with viral infectivity and blocks the attachment of HIV-1 particles to target cells, protecting them from virus entry. Analysis of the chemical footprint of anti-HIV activity indicates that HIV-1 inhibition is mediated by multiple polyphenolic compounds with low cytotoxicity and can be separated from other extract components with higher cytotoxicity. Based on our data and its excellent safety profile, we propose that PS extract represents a lead candidate for the development of a scientifically validated herbal medicine for anti-HIV-1 therapy with a mode-of-action different from and complementary to current single-molecule drugs.

Automated label-free quantification of metabolites from liquid chromatography-mass spectrometry data.

Liquid chromatography coupled to mass spectrometry (LC-MS) has become a standard technology in metabolomics. In particular, label-free quantification based on LC-MS is easily amenable to large-scale studies and thus well suited to clinical metabolomics. Large-scale studies, however, require automated processing of the large and complex LC-MS datasets. We present a novel algorithm for the detection of mass traces and their aggregation into features (i.e. all signals caused by the same analyte species) that is computationally efficient and sensitive and that leads to reproducible quantification results. The algorithm is based on a sensitive detection of mass traces, which are then assembled into features based on mass-to-charge spacing, co-elution information, and a support vector machine-based classifier able to identify potential metabolite isotope patterns. The algorithm is not limited to metabolites but is applicable to a wide range of small molecules (e.g. lipidomics, peptidomics), as well as to other separation technologies. We assessed the algorithm's robustness with regard to varying noise levels on synthetic data and then validated the approach on experimental data investigating human plasma samples. We obtained excellent results in a fully automated data-processing pipeline with respect to both accuracy and reproducibility. Relative to state-of-the art algorithms, ours demonstrated increased precision and recall of the method. The algorithm is available as part of the open-source software package OpenMS and runs on all major operating systems.

Liquid chromatography-mass spectrometry in metabolomics research: mass analyzers in ultra high pressure liquid chromatography coupling.

The present review gives an introduction into the concept of metabolomics and provides an overview of the analytical tools applied in non-targeted metabolomics with a focus on liquid chromatography (LC). LC is a powerful analytical tool in the study of complex sample matrices. A further development and configuration employing Ultra-High Pressure Liquid Chromatography (UHPLC) is optimized to provide the largest known liquid chromatographic resolution and peak capacity. Reasonably UHPLC plays an important role in separation and consequent metabolite identification of complex molecular mixtures such as bio-fluids. The most sensitive detectors for these purposes are mass spectrometers. Almost any mass analyzer can be optimized to identify and quantify small pre-defined sets of targets; however, the number of analytes in metabolomics is far greater. Optimized protocols for quantification of large sets of targets may be rendered inapplicable. Results on small target set analyses on different sample matrices are easily comparable with each other. In non-targeted metabolomics there is almost no analytical method which is applicable to all different matrices due to limitations pertaining to mass analyzers and chromatographic tools. The specifications of the most important interfaces and mass analyzers are discussed. We additionally provide an exemplary application in order to demonstrate the level of complexity which remains intractable up to date. The potential of coupling a high field Fourier Transform Ion Cyclotron Resonance Mass Spectrometer (ICR-FT/MS), the mass analyzer with the largest known mass resolving power, to UHPLC is given with an example of one human pre-treated plasma sample. This experimental example illustrates one way of overcoming the necessity of faster scanning rates in the coupling with UHPLC. The experiment enabled the extraction of thousands of features (analytical signals). A small subset of this compositional space could be mapped into a mass difference network whose topology shows specificity toward putative metabolite classes and retention time.