Diagnostic fragmentation filtering for the discovery of new chaetoglobosins and cytochalasins


Walsh, J.P., Renaud, J.B., Hoogstra, S., McMullin, D.R., Ibrahim, A., Visagie, C.M., Tanney, J.B., Yeung, K.K.C., Sumarah, M.W. (2019). Diagnostic fragmentation filtering for the discovery of new chaetoglobosins and cytochalasins. Rapid Communications in Mass Spectrometry (RCM), [online] 33(1), 133-139. http://dx.doi.org/10.1002/rcm.8306

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Mass spectrometry is a powerful tool that allows a researcher to measure the mass of individual compounds to determine their identity. Natural products are often produced by bacteria, fungi or plants as a family of compounds. In this study we developed a method for the determination of all members of these families based on their shared properties. We developed open source software that allows users to rapidly screen a sample for all compounds families of interest. This is important for the discovery of new compounds and to determine the risk of contamination for food safety.


Rationale: Microbial natural products are often biosynthesized as classes of structurally related compounds that have similar tandem mass spectrometry (MS/MS) fragmentation patterns. Mining MS/MS datasets for precursor ions that share diagnostic or common features enables entire chemical classes to be identified, including novel derivatives that have previously been unreported. Analytical data analysis tools that can facilitate a class-targeted approach to rapidly dereplicate known compounds and identify structural variants within complex matrices would be useful for the discovery of new natural products. Methods: A diagnostic fragmentation filtering (DFF) module was developed for MZmine to enable the efficient screening of MS/MS datasets for class-specific product ions(s) and/or neutral loss(es). This approach was applied to series of the structurally related chaetoglobosin and cytochalasin classes of compounds. These were identified from the culture filtrates of three fungal genera: Chaetomium globosum, a putative new species of Penicillium (called here P. cf. discolor: closely related to P. discolor), and Xylaria sp. Extracts were subjected to LC/MS/MS analysis under positive electrospray ionization and operating in a data-dependent acquisition mode, performed using a Thermo Q-Exactive mass spectrometer. All MS/MS datasets were processed using the DFF module and screened for diagnostic product ions at m/z 130.0648 and 185.0704 for chaetoglobosins, and m/z 120.0808 and 146.0598 for cytochalasins. Results: Extracts of C. globosum and P. cf. discolor strains revealed different mixtures of chaetoglobosins, whereas the Xylaria sp. produced only cytochalasins; none of the strains studied produced both classes of compounds. The dominant chaetoglobosins produced by both C. globosum and P. cf. discolor were chaetoglobosins A, C, and F. Tetrahydrochaetoglobosin A was identified from P. cf. discolor extracts and is reported here for the first time as a natural product. The major cytochalasins produced by the Xylaria sp. were cytochalasin D and epoxy cytochalasin D. A larger unknown “cytochalasin-like” molecule with the molecular formula C38H47NO10 was detected from Xylaria sp. culture filtrate extracts and is a current target for isolation and structural characterization. Conclusions: DFF is an effective LC/MS data analysis approach for rapidly identifying entire classes of compounds from complex mixtures. DFF has proved useful in the identification of new natural products and allowing for their partial characterization without the need for isolation.

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