Soluble organic matter Molecular atlas of Ryugu reveals cold hydrothermalism on C-type asteroid parent body.

The sample from the near-Earth carbonaceous asteroid (162173) Ryugu is analyzed in the context of carbonaceous meteorites soluble organic matter. The analysis of soluble molecules of samples collected by the Hayabusa2 spacecraft shines light on an extremely high molecular diversity on the C-type asteroid. Sequential solvent extracts of increasing polarity of Ryugu samples are analyzed using mass spectrometry with complementary ionization methods and structural information confirmed by nuclear magnetic resonance spectroscopy. Here we show a continuum in the molecular size and polarity, and no organomagnesium molecules are detected, reflecting a low temperature and water-rich environment on the parent body approving earlier mineralogical and chemical data. High abundance of sulfidic and nitrogen rich compounds as well as high abundance of ammonium ions confirm the water processing. Polycyclic aromatic hydrocarbons are also detected in a structural continuum of carbon saturations and oxidations, implying multiple origins of the observed organic complexity, thus involving generic processes such as earlier carbonization and serpentinization with successive low temperature aqueous alteration.

Blue-green fluorescence during hypersensitive cell death arises from phenylpropanoid deydrodimers.

Infection of Arabidopsis with avirulent Pseudomonas syringae and exposure to nitrogen dioxide (NO2) both trigger hypersensitive cell death (HCD) that is characterized by the emission of bright blue-green (BG) autofluorescence under UV illumination. The aim of our current work was to identify the BG fluorescent molecules and scrutinize their biosynthesis, localization, and functions during the HCD. Compared with wild-type (WT) plants, the phenylpropanoid-deficient mutant fah1 developed normal HCD except for the absence of BG fluorescence. Ultrahigh resolution metabolomics combined with mass difference network analysis revealed that WT but not fah1 plants rapidly accumulate dehydrodimers of sinapic acid, sinapoylmalate, 5-hydroxyferulic acid, and 5-hydroxyferuloylmalate during the HCD. FAH1-dependent BG fluorescence appeared exclusively within dying cells of the upper epidermis as detected by microscopy. Saponification released dehydrodimers from cell wall polymers of WT but not fah1 plants. Collectively, our data suggest that HCD induction leads to the formation of free BG fluorescent dehydrodimers from monomeric sinapates and 5-hydroxyferulates. The formed dehydrodimers move from upper epidermis cells into the apoplast where they esterify cell wall polymers. Possible functions of phenylpropanoid dehydrodimers are discussed.

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.

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.

Hidden in its color: A molecular-level analysis of the beer's Maillard reaction network.

We here report a comprehensive non-targeted analytical approach to describe the Maillard reaction in beer. By Fourier-transform ion cyclotron mass spectrometry (FT-ICR-MS), we were able to assign thousands of unambiguous molecular formulae to the mass signals and thus directly proceed to the compositional space of 250 analyzed beer samples. Statistical data analyses of the annotated compositions showed that the Maillard reaction is one of the driving forces of beer's molecular diversity leading to key compositional changes in the beer metabolome. Different visualization methods allowed us to map the systematic nature of Maillard reaction derived compounds. The typical molecular pattern, validated by an experimental Maillard reaction model system, pervades over 2,800 (40%) of the resolved small molecules. The major compositional changes were investigated by mass difference network analysis. We were able to reveal general reaction sequences that could be assigned to successive Maillard intermediate phase reactions by shortest path analysis.

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.

Impact of ß-glycerophosphate on the bioenergetic profile of vascular smooth muscle cells.

In chronic kidney disease, hyperphosphatemia is a key pathological factor promoting medial vascular calcification, a common complication associated with cardiovascular events and mortality. This active pathophysiological process involves osteo-/chondrogenic transdifferentiation of vascular smooth muscle cells (VSMCs) via complex intracellular mechanisms that are still incompletely understood. Little is known about the effects of phosphate on the bioenergetic profile of VSMCs during the onset of this process. Therefore, the present study explored the effects of the phosphate donor ß-glycerophosphate on cellular bioenergetics of VSMCs. Mitochondrial and glycolytic functions were determined utilizing extracellular flux analysis in primary human aortic VSMCs following exposure to ß-glycerophosphate. In VSMCs, ß-glycerophosphate increased basal respiration, mitochondrial ATP production as well as proton leak and decreased spare respiratory capacity and coupling efficiency, but did not modify non-mitochondrial or maximal respiration. ß-Glycerophosphate-treated VSMCs had higher ability to increase mitochondrial glutamine and long-chain fatty acid usage as oxidation substrates to meet their energy demand. ß-Glycerophosphate did not modify glycolytic function or basal and glycolytic proton efflux rate. In contrast, ß-glycerophosphate increased non-glycolytic acidification. ß-Glycerophosphate-treated VSMCs had a more oxidative and less glycolytic phenotype, but a reduced ability to respond to stressed conditions via mitochondrial respiration. Moreover, compounds targeting components of mitochondrial respiration modulated ß-glycerophosphate-induced oxidative stress, osteo-/chondrogenic signalling and mineralization of VSMCs. In conclusion, ß-glycerophosphate modifies key parameters of mitochondrial function and cellular bioenergetics in VSMCs that may contribute to the onset of phenotypical transdifferentiation and calcification. These observations advance the understanding of the role of energy metabolism in VSMC physiology and pathophysiology of vascular calcification during hyperphosphatemia. KEY MESSAGES: ß-Glycerophosphate modifies key parameters of mitochondrial respiration in VSMCs. ß-Glycerophosphate induces changes in mitochondrial fuel choice in VSMCs. ß-Glycerophosphate promotes a more oxidative and less glycolytic phenotype of VSMCs. ß-Glycerophosphate triggers mitochondrial-dependent oxidative stress in VSMCs. Bioenergetics impact ß-glycerophosphate-induced VSMC calcification.

Systems chemical analytics: introduction to the challenges of chemical complexity analysis.

Understanding complex (bio/geo)systems is a pivotal challenge in modern sciences that fuels a constant development of modern analytical technology, finding innovative solutions to resolve and analyse. In this introductory paper to the Faraday Discussion "Challenges in the analysis of complex natural systems", we aim to present concepts of complexity, and complex chemistry in systems subjected to biotic and abiotic transformations, and introduce the analytical possibilities to disentangle chemical complexity into its elementary parts (i.e. compositional and structural resolution) as a global integrated approach termed systems chemical analytics.

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.

Metabotype variation in a field population of tansy plants influences aphid host selection.

It is well known that plant volatiles influence herbivores in their selection of a host plant; however, less is known about how the nonvolatile metabolome affects herbivore host selection. Metabolic diversity between intraspecific plants can be characterized using non-targeted mass spectrometry that gives us a snapshot overview of all metabolic processes occurring within a plant at a particular time. Here, we show that non-targeted metabolomics can be used to reveal links between intraspecific chemical diversity and ecological processes in tansy (Tanacetum vulgare). First, we show that tansy plants can be categorized into five subgroups based up on their metabolic profiles, and that these "metabotypes" influenced natural aphid colonization in the field. Second, this grouping was not due to induced metabolomic changes within the plant due to aphid feeding but rather resulted from constitutive differences in chemical diversity between plants. These findings highlight the importance of intraspecific chemical diversity within one plant population and provide the first report of a non-targeted metabolomic field study in chemical ecology.

Mycorrhiza-Triggered Transcriptomic and Metabolomic Networks Impinge on Herbivore Fitness.

Symbioses between plants and mycorrhizal fungi are ubiquitous in ecosystems and strengthen the plants' defense against aboveground herbivores. Here, we studied the underlying regulatory networks and biochemical mechanisms in leaves induced by ectomycorrhizae that modify herbivore interactions. Feeding damage and oviposition by the widespread poplar leaf beetle Chrysomela populi were reduced on the ectomycorrhizal hybrid poplar Populus × canescens Integration of transcriptomics, metabolomics, and volatile emission patterns via mass difference networks demonstrated changes in nitrogen allocation in the leaves of mycorrhizal poplars, down-regulation of phenolic pathways, and up-regulation of defensive systems, including protease inhibitors, chitinases, and aldoxime biosynthesis. Ectomycorrhizae had a systemic influence on jasmonate-related signaling transcripts. Our results suggest that ectomycorrhizae prime wounding responses and shift resources from constitutive phenol-based to specialized protective compounds. Consequently, symbiosis with ectomycorrhizal fungi enabled poplars to respond to leaf beetle feeding with a more effective arsenal of defense mechanisms compared with nonmycorrhizal poplars, thus demonstrating the importance of belowground plant-microbe associations in mitigating aboveground biotic stress.

Monitoring chemical changes during food sterilisation using ultrahigh resolution mass spectrometry.

Sterilised food products undergo chemical changes during processing that ultimately determine the product quality. To provide detailed information on the chemistry of each stage of a pet-food sterilisation process, a laboratory-scale system was developed, which allowed sampling under the high temperatures and pressures associated with sterilisation. Products from the laboratory-scale system were representative of the factory process. Sample extracts were analysed by Fourier Transform-Ion Cyclotron Resonance-Mass Spectrometry (FT-ICR-MS), which delivered the molecular formulae and ion intensities of the compounds present. Data were examined to determine the coverage of this method, the degree of chemical change occurring during pet food thermal processing, and the level of identification possible with FT-ICR-MS. Data visualisation and statistical analysis identified significant chemical changes in pet food as a result of processing, and allowed tentative identification of the compounds involved. Insights generated using FT-ICR-MS analysis can be confirmed and further explored using conventional, targeted analyses.

Previously unknown class of metalorganic compounds revealed in meteorites.

The rich diversity and complexity of organic matter found in meteorites is rapidly expanding our knowledge and understanding of extreme environments from which the early solar system emerged and evolved. Here, we report the discovery of a hitherto unknown chemical class, dihydroxymagnesium carboxylates [(OH)2MgO2CR]-, in meteoritic soluble organic matter. High collision energies, which are required for fragmentation, suggest substantial thermal stability of these Mg-metalorganics (CHOMg compounds). This was corroborated by their higher abundance in thermally processed meteorites. CHOMg compounds were found to be present in a set of 61 meteorites of diverse petrological classes. The appearance of this CHOMg chemical class extends the previously investigated, diverse set of CHNOS molecules. A connection between the evolution of organic compounds and minerals is made, as Mg released from minerals gets trapped into organic compounds. These CHOMg metalorganic compounds and their relation to thermal processing in meteorites might shed new light on our understanding of carbon speciation at a molecular level in meteorite parent bodies.

Nanoparticle exposure reactivates latent herpesvirus and restores a signature of acute infection.

BACKGROUND: Inhalation of environmental (nano) particles (NP) as well as persistent herpesvirus-infection are potentially associated with chronic lung disease and as both are omnipresent in human society a coincidence of these two factors is highly likely. We hypothesized that NP-exposure of persistently herpesvirus-infected cells as a second hit might disrupt immune control of viral latency, provoke reactivation of latent virus and eventually lead to an inflammatory response and tissue damage. RESULTS: To test this hypothesis, we applied different NP to cells or mice latently infected with murine gammaherpesvirus 68 (MHV-68) which provides a small animal model for the study of gammaherpesvirus-pathogenesis in vitro and in vivo. In vitro, NP-exposure induced expression of the typically lytic viral gene ORF50 and production of lytic virus. In vivo, lytic viral proteins in the lung increased after intratracheal instillation with NP and elevated expression of the viral gene ORF50 could be detected in cells from bronchoalveolar lavage. Gene expression and metabolome analysis of whole lung tissue revealed patterns with striking similarities to acute infection. Likewise, NP-exposure of human cells latently infected with Epstein-Barr-Virus also induced virus production. CONCLUSIONS: Our results indicate that NP-exposure of persistently herpesvirus-infected cells - murine or human - restores molecular signatures found in acute virus infection, boosts production of lytic viral proteins, and induces an inflammatory response in the lung - a combination which might finally result in tissue damage and pathological alterations.

Characterization of poplar metabotypes via mass difference enrichment analysis.

Instrumentation technology for metabolomics has advanced drastically in recent years in terms of sensitivity and specificity. Despite these technical advances, data analytical strategies are still in their infancy in comparison with other 'omics'. Plants are known to possess an immense diversity of secondary metabolites. Typically, more than 70% of metabolomics data are not amenable to systems biological interpretation because of poor database coverage. Here, we propose a new general strategy for mass-spectrometry-based metabolomics that incorporates all exact mass features with known sum formulas into the evaluation and interpretation of metabolomics studies. We extend the use of mass differences, commonly used for feature annotation, by redefining them as variables that reflect the remaining 'omic' domains. The strategy uses exact mass difference network analyses exemplified for the metabolomic description of two grey poplar (Populus × canescens) genotypes that differ in their capability to emit isoprene. This strategy established a direct connection between the metabotype and the non-isoprene-emitting phenotype, as mass differences pertaining to prenylation reactions were over-represented in non-isoprene-emitting poplars. Not only was the analysis of mass differences able to grasp the known chemical biology of poplar, but it also improved the interpretability of yet unknown biochemical relationships.

Natural oxygenation of Champagne wine during ageing on lees: A metabolomics picture of hormesis.

The oxygenation of Champagne wine after 4 and 6 years of aging on lees in bottle was investigated by FTICR-MS and UPLC-Q-TOF-MS. Three levels of permeability were considered for the stoppers, ranging from 0.2 to 1.8 mg/L/year of oxygen transfer rate. Our results confirmed a good repeatability of ultra-high resolution FTICR-MS, both in terms of m/z and coefficient of variation of peak intensities among biological replicates. Vintages appeared to be the most discriminated features, and metabolite annotations suggested that the oldest wines (2006) were characterized by a higher sensitivity towards oxygenation. Within each vintage, the oxygenation mechanisms appeared to be different for low and high ingresses of oxygen, in agreement with the hormesis character of wine oxygenation. In the particular case of single variety wines and for a given level of stopper permeability, our results also showed that variety discrimination could be easily achieved among wines.

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.