Mycotoxins, secondary metabolites of a variety of microfungi, are capable of causing disease and death in humans and animals. Trichothecenes, a large class of mycotoxins, are particularly toxic and have been suspected of being components of biological warfare efforts. They share a common tricyclic, sesquiterpenoid-based core and are classified into 4 groups (types A-D) depending on structural similarities. Screening and quantitative mass spectrometric (MS) methods for a wide variety of trichothecenes are abundant in the literature. Due to there being over 150 members in this class of compounds, however, methods that rely exclusively on reference standards for confirmation can lack flexibility, particularly when confronted with an “unknown” variant. <br/>Mycotoxin exposure, whether natural or nefarious, required methods for rapid and confident identification of the specific toxin. Reference standards, however, may not be available for all trichothecenes on acceptable timescales/cost or synthetic practicality. The goal of this work is to develop mass spectrometric methods for mycotoxin identification that do not require reference standards. This work combines data from standard MS and MSMS experiments (both GC- and LC-based) and analysis by a variety of machine learning tools to identify distinct and specific fingerprints using common trichothecenes. These signatures reveal significant insight into the importance of various MSMS fragments that discriminate amongst related compounds. The data mining tools we use are selected such that their results are informed by and ultimately connected to real chemical information (fragments, fragmentation chemistries, substitution patterns, etc). They are also developed so that the method is applicable across a variety of MS techniques and instruments. After analysis of the known toxin panel, the resultant spectral signature libraries were used to derive the structures of a variety of “unknown” trichothecenes. Successes, limitations, and paths forward from these results will be discussed.<br/>

Towards standardless identification of trichothecene-based mycotoxins

2019 Fall National Meeting

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