Abstract
Fungi of the genus Penicillium are known to produce numerous bioactive metabolites, but their potential is not yet fully exploited. We studied four marine-derived strains of a new Penicillium species known to produce antiproliferative halogenated sesquiterpenes such as ligerin, in order to describe their metabolome. These strains exhibited clear morphotypic differences linked with their biological activities, and thorough investigations of their metabolic fingerprints were conducted by LC-HRMS/MS. Dereplication of fungal culture extracts and detection of new compounds were performed using a strategy combining independent experimental informations to improve compound annotation, the bottleneck step in metabolomics. We used in house R scripts to incorporate cosine similarity from MS/MS data processed with XCMS2 to previous probability ranking provided by ProbMetab package. The probability ranking was achieved using KEGG fungi related compounds combined with literature, and an in house Penicillium metabolite database. The probability ranking and MS/MS scores were then merged with massbank hits and exported as an information overlaid network. The biogenetic pathways showing the highest number of putative identities were automatically drawn with in house extensions of ProbMetab's drawing algorithm. The workflow described here can potentially allow one to identify more compounds by merging previous known pathways such as KEGG's and MS/MS fragmentation by compound substructure sharing, and results are presented in an informational overlaid graphical summary. With this new proposed ProbMetab module, we were able to map known biosynthetic pathways from fungi on our LC-MS/MS profiles, emphasizing interesting networks. This study shows the interest of LC-HRMS/MS in the metabolome analysis of marine-derived fungi for the rapid identification and subsequent targeted purification of new secondary metabolites belonging to bioactive chemical series.
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