In continuation of previous studies on glandular exudates of Primula, we analyzed eleven so far unstudied species and several populations for exudate composition. Unsubstituted flavone and unusually substituted flavones, normally predominant in Primula exudates, were not detected in all of the analyzed samples. Instead, some species exhibited regular substituted flavonoids, and in some cases, no flavonoids could be detected at all. The detection of a diterpene (1) in P. minima exudates is new to Primula. On basis of MS and NMR, 1 was structurally characterized as ent-kaur-16-en-19-oic acid. Comparative profiling of exudates as performed by HPLC and TLC against authentic markers indicated further the presence of the benzoquinone primin and derivatives in some exudates. Thus, exudates of newly studied species contrast markedly with those analyzed so far. The significance of observed exudate diversification is discussed in view of the phylogeny of derived lineages in European alpine regions.
Specialized metabolite (SM) diversification is a core process to plants adaptation to diverse ecological niches. Here we implemented a computational mass spectrometry (MS)-based metabolomics approach to explore SM diversification in tissues of 20 species covering Nicotiana phylogenetics sections. To drastically increase metabolite annotation, we created a large in silico fragmentation database, comprising more than 1 million structures, and scripts for connecting class prediction to consensus substructures. Altogether, the approach provides an unprecedented cartography of SM diversity and section-specific innovations in this genus. As a case-study, and in combination with NMR and MS imaging, we explored the distribution of N-acyl nornicotines, alkaloids predicted to be specific to Repandae allopolyploids, and revealed their prevalence in the genus, albeit at much lower magnitude, as well as a greater structural diversity than previously thought. Altogether, the novel data integration approaches provided here should act as a resource for future research in plant SM evolution.
Modern mass spectrometry-based metabolomics generates vast amounts of mass spectral data as part of the chemical inventory of biospecimens. Annotation of the resulting MS/MS spectra remains a challenging task that mostly relies on database interrogations,in silicoprediction and interpretation of diagnostic fragmentation schemes and/or expert knowledge-based manual interpretations. A key limitation is additionally that these approaches typically leave a vast proportion of the (bio)chemical space unannotated. Here we report a deep neural network method to predict chemical structures solely from high-resolution MS/MS spectra. This novel approach initially relies on the encoding of SMILES strings from chemical structures using a continuous chemical descriptor space that had been previously implemented for molecule design. The deep neural network was trained on 83,358 natural product-derived MS/MS spectra of the GNPS library and of the NIST HRMS database with addition of the calculated neutral losses for those spectra. After this training and parameter optimization phase, the deep neural network approach was then used to predict structures from MS/MS spectra not included in the training data-set. Our current version, implemented in the Python programming language, accurately predicted 7 structures from 744 validation structures and the following 14 structures had aTanimotosimilarity score above 0.9 when compared to the true structure. It was also able to correctly identify two structures from the CASMI 2022 international contest. On average the Tanimoto similarity is of 0.40 for data of the CASMI 2022 international contest and of 0.39 for the validation data-set. Finally, our deep neural network is also able to predict the number of 60 functional groups as well as the molecular formula of chemical structures and adduct type for the analyzed MS/MS spectra. Importantly, this deep neural network approach is extremely fast, in comparison to currently available methods, making it suitable to predict on regular computers structures for all substances within large metabolomics datasets.
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