Feature-Based Molecular Networking—An Exciting Tool to Spot Species of the Genus Cortinarius with Hidden Photosensitizers

Abstract

Fungi have developed a wide array of defense strategies to overcome mechanical injuries and pathogen infections. Recently, photoactivity has been discovered by showing that pigments isolated from Cortinarius uliginosus produce singlet oxygen under irradiation. To test if this phenomenon is limited to dermocyboid Cortinarii, six colourful Cortinarius species belonging to different classical subgenera (i.e., Dermocybe, Leprocybe, Myxacium, Phlegmacium, and Telamonia) were investigated. Fungal extracts were explored by the combination of in vitro photobiological methods, UHPLC coupled to high-resolution tandem mass spectrometry (UHPLC-HRMS2), feature-based molecular networking (FBMN), and metabolite dereplication techniques. The fungi C. rubrophyllus (Dermocybe) and C. xanthophyllus (Phlegmacium) exhibited promising photobiological activity in a low concentration range (1–7 µg/mL). Using UHPLC-HRMS2-based metabolomic tools, the underlying photoactive principle was investigated. Several monomeric and dimeric anthraquinones were annotated as compounds responsible for the photoactivity. Furthermore, the results showed that light-induced activity is not restricted to a single subgenus, but rather is a trait of Cortinarius species of different phylogenetic lineages and is linked to the presence of fungal anthraquinones. This study highlights the genus Cortinarius as a promising source for novel photopharmaceuticals. Additionally, we showed that putative dereplication of natural photosensitizers can be done by FBMN.

Charting the Cannabis plant chemical space with computational metabolomics

“…Feature-based molecular networking (FBMN) enabled the semi-automated putative annotation of metabolites with confidence level 2 (or level 3) annotations as defined in the proposed minimum reporting standards of the metabolomics standards initiatives (MSI) (Sumner et al, 2007) ( Figure 1 ). FBMN represents a computational strategy that facilitates the visualization and compound annotation of complex, high-resolution untargeted LC-MS/MS metabolite data from natural extracts (Hammerle et al, 2021). This type of MN is designed to distinguish structural isomers by incorporating features such as chromatographic retention times which enhances metabolite annotation and thus the dereplication of metabolites whilst also retaining semi-quantitative information to perform statistical analyses (Quinn et al, 2017; Nothias et al, 2018; Nothias et al, 2020).…”

Highlighting the Phototherapeutical Potential of Fungal Pigments in Various Fruiting Body Extracts with Informed Feature-Based Molecular Networking

“…To spot features capable of absorbing light in the visible spectral range, i.e., colored compounds or pigments, the filtering variable “VIS-Signal” was added as an informational layer [ 30 ]. First, the peaks of all chromatograms (λ det = 468 nm) were analyzed to generate a set of peak lists containing the start and end time point of each peak.…”

“…Exploration of the underlying photocytotoxic principle was done by combining a FBMN-layer highlighting features from photocytotoxic extracts (i.e., active features were given with a black ring) with the layer of the “VIS-Signal” variable. The filter variable “VIS-Signal” is imperative for the detection of photosensitizing compounds, as it visualizes the main requirement of a photosensitizer according to the first law of photochemistry (i.e., the Grotthus-Draper law), namely the absorption of light [ 30 ]. Several clusters with features of the AMP with a detectable absorption in the visible spectral range were identified.…”

“…Subsequent annotation of these clusters is possible through mass spectral matching against experimental (e.g., Global Natural Products Social Molecular Networking (GNPS) library [ 27 ]), in silico generated databases (e.g., ISDB-DNP [ 28 ]), or via an elaborated approach anticipating unknown features from any identified compounds based on in silico generated metabolization [ 29 ]. In our recent study [ 30 ], the power of FBMN for annotating photoactive compounds was demonstrated by using a photophysical filter variable (i.e., the “VIS-Signal” variable).…”

Automated Composition Assessment of Natural Extracts: Untargeted Mass Spectrometry-Based Metabolite Profiling Integrating Semiquantitative Detection

“…We expect such visualization to facilitate downstream tasks such as targeted isolation and full structural elucidation of the relevant features. This strategy was already used in a collaborative study to target the isolation of photoactive pigments by taking into account some particular UV signals . While the majority of preparative-scale instruments are hyphenated with UV detectors, the use of more generic detectors such as ELSD or CAD will facilitate the isolation of new NPs without chromophores.…”