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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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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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