Computational metabolite annotation in untargeted profiling aims at uncovering neutral molecular masses of underlying metabolites and assign those with putative identities. Existing annotation strategies rely on the observation and annotation of adducts to determine metabolite neutral masses. However, a significant fraction of features usually detected in untargeted experiments remains unannotated, which limits our ability to determine neutral molecular masses. Despite the availability of tools to annotate, relatively few of them benefit from the inherent presence of in-source fragments in liquid chromatography-electrospray ionization-mass spectrometry. In this study, we introduce a strategy to annotate in-source fragments in untargeted data using low energy tandem MS spectra from the METLIN library. Our algorithm, MISA (METLIN-guided in-source annotation), compares detected features against low energy fragments from MS/MS spectra, enabling robust annotation and putative identification of metabolic features based on low energy spectral matching, The algorithm was evaluated through an annotation analysis of a total 140 metabolites across three different sets of biological samples analyzed with liquid chromatography-mass spectrometry. Results showed that in cases where adducts were not formed or detected, MISA was able to uncover neutral molecular masses by in-source fragment matching. MISA was also able to provide putative metabolite identities via two annotation scores, These scores take into account the number of in-source fragments matched and the relative intensity similarity between the experimental data and the reference low energy MS/MS spectra. Overall, results showed that in-source fragmentation is a highly frequent phenomena that should be considered for comprehensive feature annotation, Thus, combined with adduct annotation, this strategy adds a complementary annotation layer, enabling in-source fragments to be annotated and increasing putative identification confidence. The algorithm is integrated into the XCMS Online platform and is freely available at http://xcmsonline.scripps.edu.
Polystyrene (PS) is one of the main polymer types of plastic wastes and is known to be resistant to biodegradation, resulting in PS waste persistence in the environment. Although previous studies have reported that some microorganisms can degrade PS, enzymes and mechanisms of microorganism PS biodegradation are still unknown. In this study, we summarized microbial species that have been identified to degrade PS. By screening the available genome information of microorganisms that have been reported to degrade PS for enzymes with functional potential to depolymerize PS, we predicted target PS-degrading enzymes. We found that cytochrome P4500s, alkane hydroxylases and monooxygenases ranked as the top potential enzyme classes that can degrade PS since they can break C–C bonds. Ring-hydroxylating dioxygenases may be able to break the side-chain of PS and oxidize the aromatic ring compounds generated from the decomposition of PS. These target enzymes were distributed in Proteobacteria, Actinobacteria, Bacteroidetes, and Firmicutes, suggesting a broad potential for PS biodegradation in various earth environments and microbiomes. Our results provide insight into the enzymatic degradation of PS and suggestions for realizing the biodegradation of this recalcitrant plastic.
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