Mass spectral libraries are collections of reference spectra, usually associated with specific analytes from which the spectra were generated, that are used for further downstream analysis of new spectra. There are many different formats used for encoding spectral libraries, but none have undergone a standardization process to ensure broad applicability to many applications. As part of the Human Proteome Organization Proteomics Standards Initiative (PSI), we have developed a standardized format for encoding spectral libraries, called mzSpecLib (https://psidev.info/mzSpecLib). It is primarily a data model that flexibly encodes metadata about the library entries using the extensible PSI-MS controlled vocabulary and can be encoded in and converted between different serialization formats. We have also developed a standardized data model and serialization for fragment ion peak annotations, called mzPAF (https://psidev.info/mzPAF). It is defined as a separate standard, since it may be used for other applications besides spectral libraries. The mzSpecLib and mzPAF standards are compatible with existing PSI standards such as ProForma 2.0 and the Universal Spectrum Identifier. The mzSpecLib and mzPAF standards have been primarily defined for peptides in proteomics applications with basic small molecule support. They could be extended in the future to other fields that need to encode spectral libraries for nonpeptidic analytes.

Three previously undescribed azepino-indole alkaloids, named purpurascenines A−C (1−3), together with the new-to-nature 7-hydroxytryptophan (4) as well as two known compounds, adenosine (5) and riboflavin (6), were isolated from fruiting bodies of Cortinarius purpurascens Fr. (Cortinariaceae). The structures of 1−3 were elucidated based on spectroscopic analyses and ECD calculations. Furthermore, the biosynthesis of purpurascenine A (1) was investigated by in vivo experiments using 13C-labeled sodium pyruvate, alanine, and sodium acetate incubated with fruiting bodies of C. purpurascens. The incorporation of 13C into 1 was analyzed using 1D NMR and HRESIMS methods. With [3-13C]-pyruvate, a dramatic enrichment of 13C was observed, and hence a biosynthetic route via a direct Pictet−Spengler reaction between α-keto acids and 7-hydroxytryptophan (4) is suggested for the biosynthesis of purpurascenines A−C (1−3). Compound 1 exhibits no antiproliferative or cytotoxic effects against human prostate (PC-3), colorectal (HCT-116), and breast (MCF-7) cancer cells. An in silico docking study confirmed the hypothesis that purpurascenine A (1) could bind to the 5-HT2A serotonin receptor’s active site. A new functional 5-HT2A receptor activation assay showed no functional agonistic but some antagonistic effects of 1 against the 5-HT-dependent 5-HT2A activation and likely antagonistic effects on putative constitutive activity of the 5-HT2A receptor.

Metabolic fingerprinting is a powerful analytical technique, giving access to high-throughput identification and relative quantification of multiple metabolites. Because of short analysis times, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) is the preferred instrumental platform for fingerprinting, although its power in analysis of free fatty acids (FFAs) is limited. However, these metabolites are the biomarkers of human pathologies and indicators of food quality. Hence, a high-throughput method for their fingerprinting is required. Therefore, here we propose a MALDI-TOF-MS method for identification and relative quantification of FFAs in biological samples of different origins. Our approach relies on formation of monomolecular Langmuir films (LFs) at the interphase of aqueous barium acetate solution, supplemented with low amounts of 2,5-dihydroxybenzoic acid, and hexane extracts of biological samples. This resulted in detection limits of 10–13–10–14 mol and overall method linear dynamic range of at least 4 orders of magnitude with accuracy and precision within 2 and 17%, respectively. The method precision was verified with eight sample series of different taxonomies, which indicates a universal applicability of our approach. Thereby, 31 and 22 FFA signals were annotated by exact mass and identified by tandem MS, respectively. Among 20 FFAs identified in Fucus algae, 14 could be confirmed by gas chromatography-mass spectrometry.

Mass spectrometry (MS) is one of the primary techniques used for large-scale analysis of small molecules in metabolomics studies. To date, there has been little data format standardization in this field, as different software packages export results in different formats represented in XML or plain text, making data sharing, database deposition, and reanalysis highly challenging. Working within the consortia of the Metabolomics Standards Initiative, Proteomics Standards Initiative, and the Metabolomics Society, we have created mzTab-M to act as a common output format from analytical approaches using MS on small molecules. The format has been developed over several years, with input from a wide range of stakeholders. mzTab-M is a simple tab-separated text format, but importantly, the structure is highly standardized through the design of a detailed specification document, tightly coupled to validation software, and a mandatory controlled vocabulary of terms to populate it. The format is able to represent final quantification values from analyses, as well as the evidence trail in terms of features measured directly from MS (e.g., LC-MS, GC-MS, DIMS, etc.) and different types of approaches used to identify molecules. mzTab-M allows for ambiguity in the identification of molecules to be communicated clearly to readers of the files (both people and software). There are several implementations of the format available, and we anticipate widespread adoption in the field.

NMR is a widely used analytical technique with a growing number of repositories available. As a result, demands for a vendor-agnostic, open data format for long-term archiving of NMR data have emerged with the aim to ease and encourage sharing, comparison, and reuse of NMR data. Here we present nmrML, an open XML-based exchange and storage format for NMR spectral data. The nmrML format is intended to be fully compatible with existing NMR data for chemical, biochemical, and metabolomics experiments. nmrML can capture raw NMR data, spectral data acquisition parameters, and where available spectral metadata, such as chemical structures associated with spectral assignments. The nmrML format is compatible with pure-compound NMR data for reference spectral libraries as well as NMR data from complex biomixtures, i.e., metabolomics experiments. To facilitate format conversions, we provide nmrML converters for Bruker, JEOL and Agilent/Varian vendor formats. In addition, easy-to-use Web-based spectral viewing, processing, and spectral assignment tools that read and write nmrML have been developed. Software libraries and Web services for data validation are available for tool developers and end-users. The nmrML format has already been adopted for capturing and disseminating NMR data for small molecules by several open source data processing tools and metabolomics reference spectral libraries, e.g., serving as storage format for the MetaboLights data repository. The nmrML open access data standard has been endorsed by the Metabolomics Standards Initiative (MSI), and we here encourage user participation and feedback to increase usability and make it a successful standard.

The identification of metabolites by mass spectrometry constitutes a major bottleneck which considerably limits the throughput of metabolomics studies in biomedical or plant research. Here, we present a novel approach to analyze metabolomics data from untargeted, data-independent LC-MS/MS measurements. By integrated analysis of MS1 abundances and MS/MS spectra, the identification of regulated metabolite families is achieved. This approach offers a global view on metabolic regulation in comparative metabolomics. We implemented our approach in the web application “MetFamily”, which is freely available at http://msbi.ipb-halle.de/MetFamily/. MetFamily provides a dynamic link between the patterns based on MS1-signal intensity and the corresponding structural similarity at the MS/MS level. Structurally related metabolites are annotated as metabolite families based on a hierarchical cluster analysis of measured MS/MS spectra. Joint examination with principal component analysis of MS1 patterns, where this annotation is preserved in the loadings, facilitates the interpretation of comparative metabolomics data at the level of metabolite families. As a proof of concept, we identified two trichome-specific metabolite families from wild-type tomato Solanum habrochaites LA1777 in a fully unsupervised manner and validated our findings based on earlier publications and with NMR.

Pseudohygrophorones A(12) (1) and B(12) (2), the first naturally occurring alkyl cyclohexenones from a fungal source, and the recently reported hygrophorone B(12) (3) have been isolated from fruiting bodies of the basidiomycete Hygrophorus abieticola Krieglst. ex Gröger & Bresinsky. Their structures were assigned on the basis of extensive one- and two-dimensional NMR spectroscopic analysis as well as ESI-HRMS measurements. The absolute configuration of the three stereogenic centers in the diastereomeric compounds 1 and 2 was established with the aid of (3)JH,H and (4)JH,H coupling constants, NOE interactions, and conformational analysis in conjunction with quantum chemical CD calculations. It was concluded that pseudohygrophorone A(12) (1) is 4S,5S,6S configured, while pseudohygrophorone B(12) (2) was identified as the C-6 epimer of 1, corresponding to the absolute configuration 4S,5S,6R. In addition, the mass spectrometric fragmentation behavior of 1-3 obtained by the higher energy collisional dissociation method allows a clear distinction between the pseudohygrophorones (1 and 2) and hygrophorone B(12) (3). The isolated compounds 1-3 exhibited pronounced activity against phytopathogenic organisms.

The Chilean Sepedonium aff. chalcipori strain KSH 883, isolated from the endemic Boletus loyo Philippi, was studied in a polythetic approach based on chemical, molecular, and biological data. A taxonomic study of the strain using molecular data of the ITS, EF1-α, and RPB2 barcoding genes confirmed the position of the isolated strain within the S. chalcipori clade, but also suggested the separation of this clade into three different species. Two new linear 15-residue peptaibols, named chilenopeptins A (1) and B (2), together with the known peptaibols tylopeptins A (3) and B (4) were isolated from the semisolid culture of strain KSH 883. The structures of 1 and 2 were elucidated on the basis of HRESIMS(n) experiments in conjunction with comprehensive 1D and 2D NMR analysis. Thus, the sequence of chilenopeptin A (1) was identified as Ac-Aib(1)-Ser(2)-Trp(3)-Aib(4)-Pro(5)-Leu(6)-Aib(7)-Aib(8)-Gln(9)-Aib(10)-Aib(11)-Gln(12)-Aib(13)-Leu(14)-Pheol(15), while chilenopeptin B (2) differs from 1 by the replacement of Trp(3) by Phe(3). Additionally, the total synthesis of 1 and 2 was accomplished by a solid-phase approach, confirming the absolute configuration of all chiral amino acids as l. Both the chilenopeptins (1 and 2) and tylopeptins (3 and 4) were evaluated for their potential to inhibit the growth of phytopathogenic organisms.

The chemical investigation of the chloroform extract of Hypericum lanceolatum guided by 1H NMR, ESIMS, and TLC profiles led to the isolation of 11 new tricyclic acylphloroglucinol derivatives, named selancins A–I (1–9) and hyperselancins A and B (10 and 11), along with the known compound 3-O-geranylemodin (12), which is described for a Hypericum species for the first time. Compounds 8 and 9 are the first examples of natural products with a 6-acyl-2,2-dimethylchroman-4-one core fused with a dimethylpyran unit. The new compounds 1–9 are rare acylphloroglucinol derivatives with two fused dimethylpyran units. Compounds 10 and 11 are derivatives of polycyclic polyprenylated acylphloroglucinols related to hyperforin, the active component of St. John’s wort. Their structures were elucidated by UV, IR, extensive 1D and 2D NMR experiments, HRESIMS, and comparison with the literature data. The absolute configurations of 5, 8, 10, and 11 were determined by comparing experimental and calculated electronic circular dichroism spectra. Compounds 1 and 2 were synthesized regioselectively in two steps. The cytotoxicity of the crude extract (88% growth inhibition at 50 μg/mL) and of compounds 1–6, 8, 9, and 12 (no significant growth inhibition up to a concentration of 10 mM) against colon (HT-29) and prostate (PC-3) cancer cell lines was determined. No anthelmintic activity was observed for the crude extract.

Demands in research investigating small molecules by applying untargeted approaches have been a key motivator for the development of repositories for mass spectrometry spectra and automated tools to aid compound identification. Comparatively little attention has been afforded to using retention times (RTs) to distinguish compounds and for liquid chromatography there are currently no coordinated efforts to share and exploit RT information. We therefore present PredRet; the first tool that makes community sharing of RT information possible across laboratories and chromatographic systems (CSs). At http://predret.org, a database of RTs from different CSs is available and users can upload their own experimental RTs and download predicted RTs for compounds which they have not experimentally determined in their own experiments. For each possible pair of CSs in the database, the RTs are used to construct a projection model between the RTs in the two CSs. The number of compounds for which RTs can be predicted and the accuracy of the predictions are dependent upon the compound coverage overlap between the CSs used for construction of projection models. At the moment, it is possible to predict up to 400 RTs with a median error between 0.01 and 0.28 min depending on the CS and the median width of the prediction interval ranging from 0.08 to 1.86 min. By comparing experimental and predicted RTs, the user can thus prioritize which isomers to target for further characterization and potentially exclude some structures completely. As the database grows, the number and accuracy of predictions will increase.