Accurate prediction of secondary metabolite gene clusters in filamentous fungi

Abstract

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....

Production of valuable compounds by molds and yeasts

“…A method to predict secondary metabolite gene clusters in filamentous fungi has recently been devised. 8 Interestingly, microbial production of secondary metabolites is limited to a very low level by certain regulatory mechanisms. Despite this, the extent of such production is sufficient for the microbe to compete with other organisms or maintain a commensal/mutual relationship with other species.…”

Sirtuin A regulates secondary metabolite production by <i>Aspergillus nidulans</i>

“…Culture in liquid and solid media affected SM gene expression, which is often found during the production of other SM (Andersen et al, 2013), and the present findings indicated that A. nidulans SirA discriminates culture conditions upon expression of the AUS/ DAUS synthetic genes. An increase in cellular NAD + , which is a substrate for sirtuin-dependent histone deacetylation, stimulates the activity of mammalian and yeast sirtuins (Haigis and Guarente, 2006;Kato and Lin, 2014).…”

“…The latter contained 32 genes associated with the production of the secondary metabolites used for synthesizing SM, such as ST, PN, xanthone and shamixanthone (XPT), the fungal siderophore (SID), emericellamide (EAS), austinol and dehydroaustinol (AUS/DAUS), aspernidine A (ASP), cichorine (CIC), ent-pimara-8(14),15-diene (EPD) (Andersen et al, 2013), the aromatic amino acid, fumiquinazoline C, ergot alkaloid and an unknown polyketide (Table S2). Other genes in Set 2 were assigned to a "monodictyphenone (MDP) metabolic process" (P < 1 ¥ 10 -5 ), and a "MDP biosynthetic process" (P < 1 ¥ pounds migrating at 12.6 min were m /z = 457.…”

Time-Dependent Production of the Bioactive Peptides Endolides A and B and the Polyketide Mariline A from the Sponge-Derived Fungus Stachylidium bicolor 293K04

“…In addition, a select group of these compounds have been developed in the clinic with important applications such as antibiotics, immunosuppressors, and other chemotherapeutic agents. Microbial genome mining efforts to explore fungal biosynthetic potential have shown that the fungal secondary metabolome has been substantially underestimated and that a large majority of fungal secondary metabolite biosynthetic gene clusters may remain silent under laboratory cultivation conditions [1,2].…”