Approaches to in-vitro lead generation for fungicide invention†

Corran, Andrew J.; Renwick, Annabel; Dunbar, Stuart J.

doi:10.1002/(sici)1096-9063(199812)54:4<338::aid-ps824>3.0.co;2-k

Cited by 16 publications

(12 citation statements)

References 22 publications

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“…These antibiotics act on distinct molecular targets in eukaryotes. Thus, oligomycin inhibits mitochondrial F0F1-ATP synthase, whereas polyene macrolides bind irreversibly to fungal cell membranes, altering their permeability (39,40). It seems possible that oligomycin and polyene macrolides could act synergistically against fungi and that a persistent fungal competitor in the natural environment of S. avermitilis has selected for the coproduction of these compounds.…”

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confidence: 99%

Synergy and contingency as driving forces for the evolution of multiple secondary metabolite production by Streptomyces species

Challis

Hopwood

2003

Proc. Natl. Acad. Sci. U.S.A.

544

398

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In this article we briefly review theories about the ecological roles of microbial secondary metabolites and discuss the prevalence of multiple secondary metabolite production by strains of Streptomyces, highlighting results from analysis of the recently sequenced Streptomyces coelicolor and Streptomyces avermitilis genomes. We address this question: Why is multiple secondary metabolite production in Streptomyces species so commonplace? We argue that synergy or contingency in the action of individual metabolites against biological competitors may, in some cases, be a powerful driving force for the evolution of multiple secondary metabolite production. This argument is illustrated with examples of the coproduction of synergistically acting antibiotics and contingently acting siderophores: two well-known classes of secondary metabolite. We focus, in particular, on the coproduction of ␤-lactam antibiotics and ␤-lactamase inhibitors, the coproduction of type A and type B streptogramins, and the coregulated production and independent uptake of structurally distinct siderophores by species of Streptomyces. Possible mechanisms for the evolution of multiple synergistic and contingent metabolite production in Streptomyces species are discussed. It is concluded that the production by Streptomyces species of two or more secondary metabolites that act synergistically or contingently against biological competitors may be far more common than has previously been recognized, and that synergy and contingency may be common driving forces for the evolution of multiple secondary metabolite production by these sessile saprophytes.

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confidence: 99%

Synergy and contingency as driving forces for the evolution of multiple secondary metabolite production by Streptomyces species

Challis

Hopwood

2003

Proc. Natl. Acad. Sci. U.S.A.

544

398

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“…For scientific reasons and because of the needs of the pharmaceutical and agrochemical industries, there is much current interest in detecting the site or mode of interaction between an exogenous ligand and a cell or organism, especially in identifying new ones (6,44,45). Such studies are nowadays typically carried out by using high-throughput methods, but there is often an inverse relation between the speed of an assay (involving, e.g., a cloned receptor with a fluorescence readout) and the amount of information it contains (which in this case is restricted to the target of interest).…”

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confidence: 99%

Discrimination of Modes of Action of Antifungal Substances by Use of Metabolic Footprinting

Allen

Davey

Broadhurst

et al. 2004

Appl Environ Microbiol

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Diploid cells of Saccharomyces cerevisiae were grown under controlled conditions with a Bioscreen instrument, which permitted the essentially continuous registration of their growth via optical density measurements. Some cultures were exposed to concentrations of a number of antifungal substances with different targets or modes of action (sterol biosynthesis, respiratory chain, amino acid synthesis, and the uncoupler). Culture supernatants were taken and analyzed for their "metabolic footprints" by using direct-injection mass spectrometry. Discriminant function analysis and hierarchical cluster analysis allowed these antifungal compounds to be distinguished and classified according to their modes of action. Genetic programming, a rule-evolving machine learning strategy, allowed respiratory inhibitors to be discriminated from others by using just two masses. Metabolic footprinting thus represents a rapid, convenient, and information-rich method for classifying the modes of action of antifungal substances.For scientific reasons and because of the needs of the pharmaceutical and agrochemical industries, there is much current interest in detecting the site or mode of interaction between an exogenous ligand and a cell or organism, especially in identifying new ones (6,44,45). Such studies are nowadays typically carried out by using high-throughput methods, but there is often an inverse relation between the speed of an assay (involving, e.g., a cloned receptor with a fluorescence readout) and the amount of information it contains (which in this case is restricted to the target of interest). However, array-based methods are showing promise in this regard (3,20,34,37,46). Genome-wide screens can also provide such information (9, 10, 19) but require numerous strains or cell lines to be studied in parallel. Methods with high information content would combine the virtues of screening just a small number of strains with a high-dimensional readout similar to that provided by the array-based methods.Metabolic control analysis (see, e.g., references 7, 15, 16, 21, and 29) tells us that while changes in the activity of a target enzyme tend to have only small effects on the flux through metabolic pathways, they can and do have substantial effects on the concentrations of metabolic intermediates. Since the use of metabolomics, especially in the form of "metabolic fingerprinting," has a much higher throughput and is much cheaper than are, say, transcriptomics and proteomics (8,14,26,42), it makes an attractive candidate for mode-of-action studies (18) and has been used to advantage by Ott and colleagues (2, 38), where the metabolic fingerprints of cells or tissues exposed to specific substances with known targets were analyzed for their modes of binding or action by using pattern recognition techniques.Normally, metabolomics measures (or seeks to measure) the concentrations of all the small molecules within a cell or tissue; and for purposes of functional genomics, we and others have exploited such strategies in the analysis of ge...

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“…cerevisiae is a fast-growing fungus that is sensitive to several fungicides. This yeast could be used as an indicator strain to search for candidate fungicides (9). In this study, we assessed the utility of S. cerevisiae in selecting clones with antifungal activity from a metagenome, and we succeeded in screening clones for such activity.…”

Section: Discussionmentioning

confidence: 99%

“…This fungus is fast growing and is suitable for screening the metagenome library using the double-agar-layer method. Corran et al (9) reported that 67% of tested fungicides exhibited S. cerevisiae growth inhibition at 5-to 100-g/ml concentrations and proposed that S. cerevisiae may be an excellent indicator organism to search for candidate fungicidal compounds. Therefore, it may be feasible to use S. cerevisiae as a model fungus to screen for antifungal activity from metagenomes.…”

mentioning

confidence: 99%

Forest Soil Metagenome Gene Cluster Involved in Antifungal Activity Expression in Escherichia coli

Chung

Lim

Kim

et al. 2008

Appl Environ Microbiol

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Using two forest soils, we previously constructed two fosmid libraries containing 113,700 members in total. The libraries were screened to select active antifungal clones using Saccharomyces cerevisiae as a target fungus. One clone from the Yuseong pine tree rhizosphere soil library, pEAF66, showed S. cerevisiae growth inhibition. Despite an intensive effort, active chemicals were not isolated. DNA sequence analysis and transposon mutagenesis of pEAF66 revealed 39 open reading frames (ORFs) and indicated that eight ORFs, probably in one transcriptional unit, might be directly involved in the expression of antifungal activity in Escherichia coli. The deduced amino acid sequences of eight ORFs were similar to those of the core genes encoding type II family polyketide synthases, such as the acyl carrier protein (ACP), ACP synthases, aminotransferase, and ACP reductase. The gene cluster involved in antifungal activity was similar in organization to the putative antibiotic production locus of Pseudomonas putida KT2440, although we could not select a similar active clone from the KT2440 genomic DNA library in E. coli. ORFs encoding ATP binding cassette transporters and membrane proteins were located at both ends of the antifungal gene cluster. Upstream ORFs encoding an IclR family response regulator and a LysR family response regulator were involved in the positive regulation of antifungal gene expression. Our results suggested the metagenomic approach as an alternative to search for novel antifungal antibiotics from unculturable soil bacteria. This is the first report of an antifungal gene cluster obtained from a soil metagenome using S. cerevisiae as a target fungus.

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Approaches to in-vitro lead generation for fungicide invention†

Cited by 16 publications

References 22 publications

Synergy and contingency as driving forces for the evolution of multiple secondary metabolite production by Streptomyces species

Synergy and contingency as driving forces for the evolution of multiple secondary metabolite production by Streptomyces species

Discrimination of Modes of Action of Antifungal Substances by Use of Metabolic Footprinting

Forest Soil Metagenome Gene Cluster Involved in Antifungal Activity Expression in Escherichia coli

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