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Activity-Independent Discovery of Secondary Metabolites Using Chemical Elicitation and Cheminformatic Inference.

ABSTRACT: Most existing antibiotics were discovered through screens of environmental microbes, particularly the streptomycetes, for the capacity to prevent the growth of pathogenic bacteria. This "activity-guided screening" method has been largely abandoned because it repeatedly rediscovers those compounds that are highly expressed during laboratory culture. Most of these metabolites have already been biochemically characterized. However, the sequencing of streptomycete genomes has revealed a large number of "cryptic" secondary metabolic genes that are either poorly expressed in the laboratory or that have biological activities that cannot be discovered through standard activity-guided screens. Methods that reveal these uncharacterized compounds, particularly methods that are not biased in favor of the highly expressed metabolites, would provide direct access to a large number of potentially useful biologically active small molecules. To address this need, we have devised a discovery method in which a chemical elicitor called Cl-ARC is used to elevate the expression of cryptic biosynthetic genes. We show that the resulting change in product yield permits the direct discovery of secondary metabolites without requiring knowledge of their biological activity. We used this approach to identify three rare secondary metabolites and find that two of them target eukaryotic cells and not bacterial cells. In parallel, we report the first paired use of cheminformatic inference and chemical genetic epistasis in yeast to identify the target. In this way, we demonstrate that oxohygrolidin, one of the eukaryote-active compounds we identified through activity-independent screening, targets the V1 ATPase in yeast and human cells and secondarily HSP90.

SUBMITTER: Pimentel-Elardo SM

PROVIDER: S-EPMC4658348 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

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Activity-Independent Discovery of Secondary Metabolites Using Chemical Elicitation and Cheminformatic Inference.

Pimentel-Elardo Sheila M SM Sørensen Dan D Ho Louis L Ziko Mikaela M Bueler Stephanie A SA Lu Stella S Tao Joe J Moser Arvin A Lee Richard R Agard David D Fairn Greg G Rubinstein John L JL Shoichet Brian K BK Nodwell Justin R JR

ACS chemical biology 20150918 11

Most existing antibiotics were discovered through screens of environmental microbes, particularly the streptomycetes, for the capacity to prevent the growth of pathogenic bacteria. This "activity-guided screening" method has been largely abandoned because it repeatedly rediscovers those compounds that are highly expressed during laboratory culture. Most of these metabolites have already been biochemically characterized. However, the sequencing of streptomycete genomes has revealed a large number ...[more]

PMID: 26352211

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Project description:The central aim in ecometabolomics and chemical ecology is to pinpoint chemical features that explain molecular functioning. The greatest challenge is the identification of compounds due to the lack of constitutive reference spectra, the large number of completely unknown compounds, and bioinformatic methods to analyze the big data. In this study we present an interdisciplinary methodological framework that extends ultra-performance liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC/ESI-QTOF-MS) with data-dependent acquisition (DDA-MS) and the automated in silico classification of fragment peaks into compound classes. We synthesize findings from a prior study that explored the influence of seasonal variations on the chemodiversity of secondary metabolites in nine bryophyte species. Here we reuse and extend the representative dataset with DDA-MS data. Hierarchical clustering, heatmaps, dbRDA, and ANOVA with post-hoc Tukey HSD were used to determine relationships of the study factors species, seasons, and ecological characteristics. The tested bryophytes showed species-specific metabolic responses to seasonal variations (50% vs. 5% of explained variation). Marchantia polymorpha, Plagiomnium undulatum, and Polytrichum strictum were biochemically most diverse and unique. Flavonoids and sesquiterpenoids were upregulated in all bryophytes in the growing seasons. We identified ecological functioning of compound classes indicating light protection (flavonoids), biotic and pathogen interactions (sesquiterpenoids, flavonoids), low temperature and desiccation tolerance (glycosides, sesquiterpenoids, anthocyanins, lactones), and moss growth supporting anatomic structures (few methoxyphenols and cinnamic acids as part of proto-lignin constituents). The reusable bioinformatic framework of this study can differentiate species based on automated compound classification. Our study allows detailed insights into the ecological roles of biochemical constituents of bryophytes with regard to seasonal variations. We demonstrate that compound classification can be improved with adding constitutive reference spectra to existing spectral libraries. We also show that generalization on compound classes improves our understanding of molecular ecological functioning and can be used to generate new research hypotheses.

Dataset Information

Activity-Independent Discovery of Secondary Metabolites Using Chemical Elicitation and Cheminformatic Inference.

Publications

Activity-Independent Discovery of Secondary Metabolites Using Chemical Elicitation and Cheminformatic Inference.

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