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).

Fungal peroxygenases represent an exciting new enzyme class for stereo-selective hydroxylation reactions. They are capable of the oxyfunctionalisation of a large, diverse scope of substrates including alkanes and steroids as well as the heteroatoms sulfur and nitrogen. The outstanding activities and stabilities as well as their reliance on hydrogen peroxide as co-substrate renders it a highly interesting biocatalyst.

Ceramides (CERs) are the backbone of the intercellular lipid lamellae of the stratum corneum (SC), the outer layer of the skin. Skin diseases such as atopic dermatitis, psoriasis, and aged skin are characterized by dysfunctional skin barrier and dryness which are associated with reduced levels of CERs. Replenishing the depleted epidermal CERs with exogenous CERs has been shown to have beneficial effects in improving the skin barrier and hydration. The exogenous CERs such as phyto-derived CERs (PhytoCERs) can be delivered deep into the SC using novel topical formulations. This, however, requires investigating the rate and extent of skin permeation of CERs. In this study, an LC/APCI-MS method to detect and quantify PhytoCERs in different layers of the skin has been developed and validated. The method was used to investigate the skin permeation of PhytoCERs using Franz diffusion cells after applying an amphiphilic cream containing PhytoCERs to the surface of ex vivo human skin. As plant-specific CERs are not commercially available, well-characterized CERs isolated from oat (Avena abyssinica) were used as reference standards for the development and validation of the method. The method was linear over the range of 30–1050 ng/mL and sensitive with limit of detection and quantification of 10 and 30 ng/mL, respectively. The method was also selective, accurate, and precise with minimal matrix effect (with mean matrix factor around 100%). Even if more than 85% of oat CERs in the cream remained in the cream after the incubation periods of 30, 100, and 300 min, it was possible to quantify the small quantities of oat CERs distributed across the SC, epidermis, and dermis of the skin indicating the method’s sensitivity. Therefore, the method can be used to investigate the skin permeation of oat CERs from the various pharmaceutical and cosmeceutical products without any interference from the skin constituents such as the epidermal lipids.

In nontarget screening, structure elucidation of small molecules from high resolution mass spectrometry (HRMS) data is challenging, particularly the selection of the most likely candidate structure among the many retrieved from compound databases. Several fragmentation and retention prediction methods have been developed to improve this candidate selection. In order to evaluate their performance, we compared two in silico fragmenters (MetFrag and CFM-ID) and two retention time prediction models (based on the chromatographic hydrophobicity index (CHI) and on log D). A set of 78 known organic micropollutants was analyzed by liquid chromatography coupled to a LTQ Orbitrap HRMS with electrospray ionization (ESI) in positive and negative mode using two fragmentation techniques with different collision energies. Both fragmenters (MetFrag and CFM-ID) performed well for most compounds, with average ranking the correct candidate structure within the top 25% and 22 to 37% for ESI+ and ESI− mode, respectively. The rank of the correct candidate structure slightly improved when MetFrag and CFM-ID were combined. For unknown compounds detected in both ESI+ and ESI−, generally positive mode mass spectra were better for further structure elucidation. Both retention prediction models performed reasonably well for more hydrophobic compounds but not for early eluting hydrophilic substances. The log D prediction showed a better accuracy than the CHI model. Although the two fragmentation prediction methods are more diagnostic and sensitive for candidate selection, the inclusion of retention prediction by calculating a consensus score with optimized weighting can improve the ranking of correct candidates as compared to the individual methods.

The screening effort of large protein variant libraries renders the probability of coincidental discovering a new enzyme with non-natural activity to almost zero - the so-called numbers problem. Insights into the origin of life, evolution and enzymatic promiscuity, combined with the inspiration of methods from organic chemistry, offer solutions for this problem. With the newly discovered enzymes synthetic micro production units shall be established in a Leibniz Research Cluster where engineering and biotechnology are combined.

Advanced glycation end products (AGEs) are posttranslational modifications formed non-enzymatically from the reaction of carbohydrates and their degradation products with proteins. Accumulation of AGEs is associated with the progression of severe diabetic complications, for example, and elevated tissue levels of AGEs might even predict these pathologies. As AGE formation is often site-specific, mapping of these modification sites may reveal more sensitive and specific markers than the global tissue level. Here, 42 AGE modifications were identified in a bottom-up proteomic approach by tandem mass spectrometry, which corresponded to 36 sites in 22 high to medium abundant proteins in individual plasma samples obtained from type 2 diabetes mellitus (T2DM) patients with long disease duration (>10 years). Major modifications were glarg (11 modification sites) and carboxymethylation (5) of arginine and formylation (8), acetylation (7), and carboxymethylation (7) of lysine residues. Relative quantification of these sites in plasma samples obtained from normoglycemic individuals (n = 47) and patients with T2DM being newly diagnosed (n = 47) or of medium (2–5 years, n = 20) and long disease duration (>10 years, n = 20) did not reveal any significant differences.

Passiflora incarnata as well as some other Passiflora species are reported to possess anxiolytic and sedative activity and to treat various CNS disorders. The medicinal use of only a few Passiflora species has been scientifically verified. There are over 400 species in the Passiflora genus worldwide, most of which have been little characterized in terms of phytochemical or pharmacological properties. Herein, large-scale multi-targeted metabolic profiling and fingerprinting techniques were utilized to help gain a broader insight into Passiflora species leaves’ chemical composition. Nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) spectra of extracted components derived from 17 Passiflora accessions and from different geographical origins were analyzed using multivariate data analyses. A total of 78 metabolites were tentatively identified, that is, 20 C-flavonoids, 8 O-flavonoids, 21 C, O-flavonoids, 2 cyanogenic glycosides, and 23 fatty acid conjugates, of which several flavonoid conjugates are for the first time to be reported in Passiflora spp. To the best of our knowledge, this study provides the most complete map for secondary metabolite distribution within that genus. Major signals in 1H-NMR and MS spectra contributing to species discrimination were assigned to those of C-flavonoids including isovitexin-2″-O-xyloside, luteolin-C-deoxyhexoside-O-hexoside, schaftoside, isovitexin, and isoorientin. P. incarnata was found most enriched in C-flavonoids, justifying its use as an official drug within that genus. Compared to NMR, LC-MS was found more effective in sample classification based on genetic and/ or geographical origin as revealed from derived multivariate data analyses. Novel insight on metabolite candidates to mediate for Passiflora CNS sedative effects is also presented.

Senna alexandrina Mill (Cassia acutifolia and Cassia angustifolia) are used for the laxative medicine Senna. Leaves and pods from two geographically different sources were distinguished from each other via proton nuclear magnetic resonance (1H-NMR) and ultra performance liquid chromatography-mass spectrometry (UPLC-MS) analysis. Under optimized conditions, we were able to simultaneously quantify and identify 107 metabolites including 21 anthraquinones, 24 bianthrones (including sennosides), 5 acetophenones, 25 C/O-flavonoid conjugates, 5 xanthones, 3 naphthalenes, 2 further phenolics, and 9 fatty acids. Principal component analysis (PCA) and hierarchical clustering analysis (HCA) were used to define both similarities and differences among samples. For sample classification based on tissue type (leaf and pod), both UPLC-MS and NMR were found to be more effective in separation than on geographical origin. Results reveal that the amounts of the major classes of bioactives in Senna, i.e., flavonoids and sennosides, varied significantly among organs. Leaves contained more flavonoids and ω-3 fatty acids but fewer benzophenone derivatives than pods. In contrast, pods were more enriched in bianthrones (sennosides). PCA analysis was found to be equally effective in predicting the origin of the commercial Senna preparation using NMR and/or UPLC-MS datasets. Furthermore, a selection of six so far uninvestigated Senna species were analyzed by UPLC-MS. Results revealed that the Senna alata leaf in terms of secondary metabolite composition is the most closely related species to S. alexandrina Mill, showing the highest levels of the anthraquinone “rhein” and flavonoid conjugates. To the best of our knowledge, this study provides the first approach utilizing both UPLC-MS and NMR to reveal secondary metabolite compositional differences among Senna species.

Liquid chromatography negative ion electrospray ionisation tandem mass spectrometry has been used for characterisation of naturally occurring prenylated fungal metabolites and synthetic derivatives. The fragmentation studies allow an elucidation of the decomposition pathways for these compounds. It could be shown, that the prenyl side chain is degraded by successive radical losses of C5 units. Both the benzoquinones and the phenolic derivatives display significant key ions comprising the aromatic ring. In some cases, the formation of significant oxygen-free key ions could be evidenced by high-resolution MS/MS measurements. Furthermore, the different types of basic skeletons, benzoquinones and phenol type as well as cyclic prenylated compounds, can be differentiated by their MS/MS behaviour.

In this paper, we describe data processing and metabolite identification approaches which lead to a rapid and semi-automated interpretation of metabolomics experiments. Data from metabolite fingerprinting using LC-ESI-Q-TOF/MS were processed with several open-source software packages, including XCMS and CAMERA to detect features and group features into compound spectra. Next, we describe the automatic scheduling of tandem mass spectrometry (MS) acquisitions to acquire a large number of MS/MS spectra, and the subsequent processing and computer-assisted annotation towards identification using the R packages MetShot, Rdisop, and the MetFusion application. We also implement a simple retention time prediction model using predicted lipophilicity logD, which predicts retention times within 42 s (6 min gradient) for most compounds in our setup. We putatively identified 44 common metabolites including several amino acids and phospholipids at metabolomics standards initiative (MSI) levels two and three and confirmed the majority of them by comparison with authentic standards at MSI level one. To aid both data integration within and data sharing between laboratories, we integrated data from two labs and mapped retention times between the chromatographic systems. Despite the different MS instrumentation and different chromatographic gradient programs, the mapped retention times agree within 26 s (20 min gradient) for 90 % of the mapped features.