Explorative Solid-Phase Extraction (E-SPE) for Accelerated Microbial Natural Product Discovery, Dereplication, and Purification

Månsson, Maria; Phipps, Richard Kerry; Gram, Lone; Munro, Murray H. G.; Larsen, Thomas Ostenfeld; Nielsen, Kristian Fog

doi:10.1021/np100151y

Cited by 74 publications

(75 citation statements)

References 62 publications

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“…The mass spectrometer was calibrated using sodium formate automatically infused before each analytical run, providing a mass accuracy better than 1. (31,44) and dereplicated against the approximately 18,000 fungal metabolites listed in AntiBase 2010 by ultraviolet-visible (UV/Vis) spectra, retention time, adduct pattern, and high-resolution data (<1.5 ppm mass accuracy and isotope fit better than 40 using SigmaFit; Bruker Daltonics) (31,45).…”

Section: Methodsmentioning

confidence: 99%

Accurate prediction of secondary metabolite gene clusters in filamentous fungi

Andersen

Nielsen

Klitgaard

et al. 2012

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

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213

185

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Biosynthetic pathways of secondary metabolites from fungi are currently subject to an intense effort to elucidate the genetic basis for these compounds due to their large potential within pharmaceutics and synthetic biochemistry. The preferred method is methodical gene deletions to identify supporting enzymes for key synthases one cluster at a time. In this study, we design and apply a DNA expression array for Aspergillus nidulans in combination with legacy data to form a comprehensive gene expression compendium. We apply a guilt-by-association-based analysis to predict the extent of the biosynthetic clusters for the 58 synthases active in our set of experimental conditions. A comparison with legacy data shows the method to be accurate in 13 of 16 known clusters and nearly accurate for the remaining 3 clusters. Furthermore, we apply a data clustering approach, which identifies cross-chemistry between physically separate gene clusters (superclusters), and validate this both with legacy data and experimentally by prediction and verification of a supercluster consisting of the synthase AN1242 and the prenyltransferase AN11080, as well as identification of the product compound nidulanin A. We have used A. nidulans for our method development and validation due to the wealth of available biochemical data, but the method can be applied to any fungus with a sequenced and assembled genome, thus supporting further secondary metabolite pathway elucidation in the fungal kingdom.aspergilli | natural products | secondary metabolism | polyketide synthases N o other group of biochemical compounds holds as much promise for drug development as the secondary (nongrowth associated) metabolites (SMs). A review from 2012 (1) found that for small-molecule pharmaceuticals, 68% of the anticancer agents and 52% of the antiinfective agents are natural products, or derived from natural products. The fact that SMs are often synthesized as polymer backbones that are subsequently diversified greatly via the actions of tailoring enzymes sets the stage for combinatorial biochemistry (2), because their biosynthesis is modular.Major groups of SMs include polyketides (PKs) consisting of -CH 2 -(C = O)-units, ribosomal and nonribosomomal peptides (NRPs), and terpenoids made from C 5 isoprene units. These polymer backbones are, with the exception of ribosomal peptides, made by synthases or synthetases and are modified by a plethora of tailoring enzymes, including (de)hydratases, oxygenases, hydrolases, methylases, and others.In fungi, these biosynthetic genes of secondary metabolism are organized in discrete clusters around the synthase genes. Although quite accurate algorithms are available for identification of possible SM biosynthetic genes, particularly PK synthases (PKSs), NRP synthetases (NRPSs), and dimethylallyl tryptophan synthases (DMATSs) (3, 4), the assignment and prediction of the members of the individual clusters solely from the genome sequence have not been accurate. Relevant protein domains can be predicted for some of the genes (e....

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Section: Methodsmentioning

confidence: 99%

Accurate prediction of secondary metabolite gene clusters in filamentous fungi

Andersen

Nielsen

Klitgaard

et al. 2012

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

Self Cite

213

185

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“…Several antibacterial compounds have been isolated from fungi, with penicillin, produced by Penicillium and Aspergillus species, being the most famous [1]. In the search for novel antibacterial secondary metabolites, several bio-guided approaches can be applied such as fractionating the secondary metabolome by reverse-phase liquid chromatography [2,3] or by explorative solid-phase extraction [4,5]. The fractions are then often assessed for bioactivity through traditional techniques (e.g., spectrophotometry).…”

Section: Introductionmentioning

confidence: 99%

Fast Screening of Antibacterial Compounds from Fusaria

Søndergaard

Fredborg

Christensen

et al. 2016

Toxins

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Bio-guided screening is an important method to identify bioactive compounds from fungi. In this study we applied a fast digital time-lapse microscopic method for assessment of the antibacterial properties of secondary metabolites from the fungal genus Fusarium. Here antibacterial effects could be detected for antibiotic Y, aurofusarin, beauvericin, enniatins and fusaric acid after six hours of cultivation. The system was then used in a bio-guided screen of extracts from 14 different Fusarium species, which had been fractionated by HPLC. In this screen, fractions containing the red pigments aurofusarin and bikaverin showed effects against strains of Lactobacillus and Bifidobacterium. The IC50 for aurofusarin against Lactobacillus acidophilus was 8 µM, and against Bifidobacterium breve it was 64 µM. Aurofusarin only showed an effect on probiotic bacteria, leading to the speculation that only health-promoting bacteria with a positive effect in the gut system are affected.

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“…71-73 For natural product separations, reversed phase LC, and hydrophobic interaction chromatography are most commonly employed with a water-acetonitrile, or water-methanol gradient. 74 This is typically performed on the basis of hydropathy, where reversed phase LC (RPLC) and hydrophobic interaction chromatography (HILIC) are most commonly utilized, 75 and column retention will be affected by the ionization of these groups. Mobile phase pH can thereby significantly affect the separation efficiency for natural product extracts.…”

Section: Methods Of Generating Inventories Of Microbial Metabolitesmentioning

confidence: 99%

Comparative mass spectrometry-based metabolomics strategies for the investigation of microbial secondary metabolites

2017

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The labor-intensive process of microbial natural product discovery is contingent upon identifying discrete secondary metabolites of interest within complex biological extracts, which contain inventories of all extractable small molecules produced by an organism or consortium. Historically, compound isolation prioritization has been driven by observed biological activity and/or relative metabolite abundance and followed by dereplication via accurate mass analysis. Decades of discovery using variants of these methods has generated the natural pharmacopeia but also contributes to recent high rediscovery rates. However, genomic sequencing reveals substantial untapped potential in previously mined organisms, and can provide useful prescience of potentially new secondary metabolites that ultimately enables isolation. Recently, advances in comparative metabolomics analyses have been coupled to secondary metabolic predictions to accellerate bioactivity and abundance-independent discovery work flows. In this review we will discuss the various analytical and computational techniques that enable MS-based metabolomic applications to natural product discovery and discuss the future prospects for comparative metabolomics in natural product discovery.

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Explorative Solid-Phase Extraction (E-SPE) for Accelerated Microbial Natural Product Discovery, Dereplication, and Purification

Cited by 74 publications

References 62 publications

Accurate prediction of secondary metabolite gene clusters in filamentous fungi

Accurate prediction of secondary metabolite gene clusters in filamentous fungi

Fast Screening of Antibacterial Compounds from Fusaria

Comparative mass spectrometry-based metabolomics strategies for the investigation of microbial secondary metabolites

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