SMURF: Genomic mapping of fungal secondary metabolite clusters

Khaldi, Nora; Seifuddin, Fayaz; Turner, Geoff; Haft, Daniel H.; Nierman, William C.; Wolfe, Kenneth H.; Fedorova, Natalie D.

doi:10.1016/j.fgb.2010.06.003

Cited by 680 publications

(602 citation statements)

References 50 publications

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“…Owing to the potential of antimicrobials against pathogenic and spoilage microorganisms, these secondary metabolites gain much importance for the application in food products [18][19][20]. They contain the properties of antimicrobials and antioxidants at the same time and so are considered as a beter option for food preservation as compared to synthetic preservatives [21].…”

Section: Natural Antimicrobial Agentsmentioning

confidence: 99%

Natural Antimicrobials, their Sources and Food Safety

Arshad¹,

Batool²

2017

Food Additives

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With consumer awareness about food safety and quality, there is a high demand for the preservative (synthetic)-free foods and use of natural products as preservatives. Natural antimicrobials from diferent sources are used to preserve food from spoilage and pathogenic microorganisms. Plants (herbs and spices, fruits and vegetables, seeds and leaves) are the main source of antimicrobials and contain many essential oils that have preservation efect against diferent microorganisms. Mainly, herb and spices contain many essential oils and the examples include rosemary, sage, basil, oregano, thyme, cardamom, and clove. These essential oils are very efective against many pathogenic and spoilage microorganisms like Salmonella, Escherichia coli, Listeria monocytogenes, Campylobacter spp., and Staphylococcus aureus and help to increase their quality and shelf stability. These antimicrobial compounds are also used in combination with edible food coatings and inhibit the ability of microorganisms to grow on the surface of food and food products.

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Section: Natural Antimicrobial Agentsmentioning

confidence: 99%

Natural Antimicrobials, their Sources and Food Safety

Arshad¹,

Batool²

2017

Food Additives

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“…Ten putative prenyltransferase genes of the DMATS superfamily have been identified in the genome sequence of Neosartorya fischeri NRRL 181 (Khaldi et al, 2010). Eight of these were confirmed biochemically (Chooi et al, 2012;Mundt et al, 2012;.…”

Section: Introductionmentioning

confidence: 98%

“…Bioinformatic analysis of available genome sequences revealed the presence of more than 200 putative prenyltransferases of the DMATS superfamily. More than 20 such genes have been characterized biochemically following overproduction and purification (Bonitz et al, 2011;Khaldi et al, 2010;Li, 2010;Yin et al, 2013). The characterized indole prenyltransferases have characteristic features, especially their high flexibility towards aromatic substrates and high regioselectivity regarding prenylation position at the indole ring and prenylation pattern.…”

Section: Introductionmentioning

confidence: 99%

CdpC2PT, a reverse prenyltransferase from Neosartorya fischeri with a distinct substrate preference from known C2-prenyltransferases

Mundt

2013

Microbiology

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A putative prenyltransferase gene, NFIA_043650, was amplified from Neosartorya fischeri NRRL 181 and cloned into the expression vector pQE60. The deduced polypeptide consisting of 445 amino acids with a molecular mass of 51 kDa was overproduced in Escherichia coli and purified as His 6 -tagged protein to near homogeneity. The purified soluble protein was subsequently assayed with potential aromatic substrates in the presence of dimethylallyl diphosphate. HPLC analysis of the reaction mixtures revealed acceptance of all tested tryptophan-containing cyclic dipeptides. Isolation and structural elucidation of enzyme products of five selected substrates indicated a reverse C2-prenylation on the indole nucleus, proving the enzyme to be a cyclic dipeptide C2-prenyltransferase (CdpC2PT). Differing significantly from two known brevianamide

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“…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.g., cytochrome P450 genes) (5); however, genes in identified clusters often have unknown functions, which makes predicting their inclusion impossible.…”

mentioning

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.

210

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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SMURF: Genomic mapping of fungal secondary metabolite clusters

Cited by 680 publications

References 50 publications

Natural Antimicrobials, their Sources and Food Safety

Natural Antimicrobials, their Sources and Food Safety

CdpC2PT, a reverse prenyltransferase from Neosartorya fischeri with a distinct substrate preference from known C2-prenyltransferases

Accurate prediction of secondary metabolite gene clusters in filamentous fungi

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