Cell phenotypic changes of mouse connective tissue fibroblasts (L-929) to poly(ethylene glycol)-based gels

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.Cellular responses to various gels fabricated by photoinitiated crosslinking using acrylated linear and multi-arm poly(ethylene glycol) (PEG)-based and poly(propylene glycol)-b-poly(ethylene glycol) precursors were investigated. While no protein adsorption and cell adhesion were observed on the hydrophilic PEG-based gels, protein adsorption and cell adhesion did occur on the more hydrophobic gel generated from the block copolymer precursor. Murine fibroblast viability on the poly(ethylene glycol)-based gels was studied in the course of 72 h and the results indicated no cytotoxicity. In a systematic study, extra- and intracellular metabolites of the murine fibroblasts cultured on these PEG-based gels were examined by GC-MS. Distinct intra- and extracellular changes in primary metabolism, namely amino acid metabolism, glycolysis and fatty acid metabolism, were observed. Cells cultured on the polymeric gels induced more intense intracellular changes in the metabolite profile by means of higher metabolite intensities with time in comparison to cells cultured on the reference substrate (tissue culture polystyrene). In contrast, extracellular changes of metabolite intensities were comparable.DFG, EXC 314, Unifying Concepts in Catalysi

Supporting non-target identification by adding hydrogen deuterium exchange MS/MS capabilities to MetFrag

Liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) is increasingly popular for the non-targeted exploration of complex samples, where tandem mass spectrometry (MS/MS) is used to characterize the structure of unknown compounds. However, mass spectra do not always contain sufficient information to unequivocally identify the correct structure. This study investigated how much additional information can be gained using hydrogen deuterium exchange (HDX) experiments. The exchange of “easily exchangeable” hydrogen atoms (connected to heteroatoms), with predominantly [M+D]+ ions in positive mode and [M-D]− in negative mode was observed. To enable high-throughput processing, new scoring terms were incorporated into the in silico fragmenter MetFrag. These were initially developed on small datasets and then tested on 762 compounds of environmental interest. Pairs of spectra (normal and deuterated) were found for 593 of these substances (506 positive mode, 155 negative mode spectra). The new scoring terms resulted in 29 additional correct identifications (78 vs 49) for positive mode and an increase in top 10 rankings from 80 to 106 in negative mode. Compounds with dual functionality (polar head group, long apolar tail) exhibited dramatic retention time (RT) shifts of up to several minutes, compared with an average 0.04 min RT shift. For a smaller dataset of 80 metabolites, top 10 rankings improved from 13 to 24 (positive mode, 57 spectra) and from 14 to 31 (negative mode, 63 spectra) when including HDX information. The results of standard measurements were confirmed using targets and tentatively identified surfactant species in an environmental sample collected from the river Danube near Novi Sad (Serbia). The changes to MetFrag have been integrated into the command line version available at http://c-ruttkies.github.io/MetFrag and all resulting spectra and compounds are available in online resources and in the Electronic Supplementary Material (ESM)

Decision tree supported substructure prediction of metabolites from GC-MS profiles

Gas chromatography coupled to mass spectrometry (GC-MS) is one of the most widespread routine technologies applied to the large scale screening and discovery of novel metabolic biomarkers. However, currently the majority of mass spectral tags (MSTs) remains unidentified due to the lack of authenticated pure reference substances required for compound identification by GC-MS. Here, we accessed the information on reference compounds stored in the Golm Metabolome Database (GMD) to apply supervised machine learning approaches to the classification and identification of unidentified MSTs without relying on library searches. Non-annotated MSTs with mass spectral and retention index (RI) information together with data of already identified metabolites and reference substances have been archived in the GMD. Structural feature extraction was applied to sub-divide the metabolite space contained in the GMD and to define the prediction target classes. Decision tree (DT)-based prediction of the most frequent substructures based on mass spectral features and RI information is demonstrated to result in highly sensitive and specific detections of sub-structures contained in the compounds. The underlying set of DTs can be inspected by the user and are made available for batch processing via SOAP (Simple Object Access Protocol)-based web services. The GMD mass spectral library with the integrated DTs is freely accessible for non-commercial use at http://gmd.mpimp-golm.mpg.de/. All matching and structure search functionalities are available as SOAP-based web services. A XML + HTTP interface, which follows Representational State Transfer (REST) principles, facilitates read-only access to data base entities

Piriformospora indica Stimulates Root Metabolism of Arabidopsis thaliana

Piriformospora indica is a root-colonizing fungus, which interacts with a variety of plants including Arabidopsis thaliana. This interaction has been considered as mutualistic leading to growth promotion of the host. So far, only indolic glucosinolates and phytohormones have been identified as key players. In a comprehensive non-targeted metabolite profiling study, we analyzed Arabidopsis thaliana’s roots, root exudates, and leaves of inoculated and non-inoculated plants by ultra performance liquid chromatography/electrospray ionization quadrupole-time-of-flight mass spectrometry (UPLC/(ESI)-QTOFMS) and gas chromatography/electron ionization quadrupole mass spectrometry (GC/EI-QMS), and identified further biomarkers. Among them, the concentration of nucleosides, dipeptides, oligolignols, and glucosinolate degradation products was affected in the exudates. In the root profiles, nearly all metabolite levels increased upon co-cultivation, like carbohydrates, organic acids, amino acids, glucosinolates, oligolignols, and flavonoids. In the leaf profiles, we detected by far less significant changes. We only observed an increased concentration of organic acids, carbohydrates, ascorbate, glucosinolates and hydroxycinnamic acids, and a decreased concentration of nitrogen-rich amino acids in inoculated plants. These findings contribute to the understanding of symbiotic interactions between plant roots and fungi of the order of Sebacinales and are a valid source for follow-up mechanistic studies, because these symbioses are particular and clearly different from interactions of roots with mycorrhizal fungi or dark septate endophytes

Perturbations in the Primary Metabolism of Tomato and Arabidopsis thaliana Plants Infected with the Soil-Borne Fungus Verticillium dahliae

The hemibiotrophic soil-borne fungus Verticillium dahliae is a major pathogen of a number of economically important crop species. Here, the metabolic response of both tomato and Arabidopsis thaliana to V. dahliae infection was analysed by first using non-targeted GC-MS profiling. The leaf content of both major cell wall components glucuronic acid and xylose was reduced in the presence of the pathogen in tomato but enhanced in A. thaliana. The leaf content of the two tricarboxylic acid cycle intermediates fumaric acid and succinic acid was increased in the leaf of both species, reflecting a likely higher demand for reducing equivalents required for defence responses. A prominent group of affected compounds was amino acids and based on the targeted analysis in the root, it was shown that the level of 12 and four free amino acids was enhanced by the infection in, respectively, tomato and A. thaliana, with leucine and histidine being represented in both host species. The leaf content of six free amino acids was reduced in the leaf tissue of diseased A. thaliana plants, while that of two free amino acids was raised in the tomato plants. This study emphasizes the role of primary plant metabolites in adaptive responses when the fungus has colonized the plant

Plant-to-Plant Variability in Root Metabolite Profiles of 19 Arabidopsis thaliana Accessions Is Substance-Class-Dependent

Natural variation of secondary metabolism between different accessions of Arabidopsis thaliana (A. thaliana) has been studied extensively. In this study, we extended the natural variation approach by including biological variability (plant-to-plant variability) and analysed root metabolic patterns as well as their variability between plants and naturally occurring accessions. To screen 19 accessions of A. thaliana, comprehensive non-targeted metabolite profiling of single plant root extracts was performed using ultra performance liquid chromatography/electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC/ESI-QTOF-MS) and gas chromatography/electron ionization quadrupole mass spectrometry (GC/EI-QMS). Linear mixed models were applied to dissect the total observed variance. All metabolic profiles pointed towards a larger plant-to-plant variability than natural variation between accessions and variance of experimental batches. Ratios of plant-to-plant to total variability were high and distinct for certain secondary metabolites. None of the investigated accessions displayed a specifically high or low biological variability for these substance classes. This study provides recommendations for future natural variation analyses of glucosinolates, flavonoids, and phenylpropanoids and also reference data for additional substance classes

Stress-Related Mitogen-Activated Protein Kinases Stimulate the Accumulation of Small Molecules and Proteins in Arabidopsis thaliana Root Exudates

A delicate balance in cellular signaling is required for plants to respond to microorganisms or to changes in their environment. Mitogen-activated protein kinase (MAPK) cascades are one of the signaling modules that mediate transduction of extracellular microbial signals into appropriate cellular responses. Here, we employ a transgenic system that simulates activation of two pathogen/stress-responsive MAPKs to study release of metabolites and proteins into root exudates. The premise is based on our previous proteomics study that suggests upregulation of secretory processes in this transgenic system. An advantage of this experimental set-up is the direct focus on MAPK-regulated processes without the confounding complications of other signaling pathways activated by exposure to microbes or microbial molecules. Using non-targeted metabolomics and proteomics studies, we show that MAPK activation can indeed drive the appearance of dipeptides, defense-related metabolites and proteins in root apoplastic fluid. However, the relative levels of other compounds in the exudates were decreased. This points to a bidirectional control of metabolite and protein release into the apoplast. The putative roles for some of the identified apoplastic metabolites and proteins are discussed with respect to possible antimicrobial/defense or allelopathic properties. Overall, our findings demonstrate that sustained activation of MAPKs alters the composition of apoplastic root metabolites and proteins, presumably to influence the plant-microbe interactions in the rhizosphere. The reported metabolomics and proteomics data are available via Metabolights (Identifier: MTBLS441) and ProteomeXchange (Identifier: PXD006328), respectively