Biosynthesis of the polyene antifungal antibiotic nystatin in Streptomyces noursei ATCC 11455: analysis of the gene cluster and deduction of the biosynthetic pathway

BACKGROUND: The polyene macrolide antibiotic nystatin produced by Streptomyces noursei ATCC 11455 is an important antifungal agent. The nystatin molecule contains a polyketide moiety represented by a 38-membered macrolactone ring to which the deoxysugar mycosamine is attached. Molecular cloning and characterization of the genes governing the nystatin biosynthesis is of considerable interest because this information can be used for the generation of new antifungal antibiotics. RESULTS: A DNA region of 123,580 base pairs from the S. noursei ATCC 11455 genome was isolated, sequenced and shown by gene disruption to be involved in nystatin biosynthesis. Analysis of the DNA sequence resulted in identification of six genes encoding a modular polyketide synthase (PKS), genes for thioesterase, deoxysugar biosynthesis, modification, transport and regulatory proteins. One of the PKS-encoding genes, nysC, was found to encode the largest (11,096 amino acids long) modular PKS described to date. Analysis of the deduced gene products allowed us to propose a model for the nystatin biosynthetic pathway in S. noursei. CONCLUSIONS: A complete set of genes responsible for the biosynthesis of the antifungal polyene antibiotic nystatin in S. noursei ATCC 11455 has been cloned and analyzed. This represents the first example of the complete DNA sequence analysis of a polyene antibiotic biosynthetic gene cluster. Manipulation of the genes identified within the cluster may potentially lead to the generation of novel polyketides and yield improvements in the production strains.     

THE MITRA‐WAN FORESTRY MODEL: A DISCRETE‐TIME OPTIMAL CONTROL PROBLEM

In this paper, we present some new properties of the Mitra‐Wan forestry model written as a discrete‐time optimal control problem. For this problem, the set of stationary states is characterized. For the optimal long‐run management, we consider the following optimality criteria: average optimality, good control policies, bias optimality, and overtaking optimality. We establish relationships between these criteria and show that the value of average optimal policies is constant and equals the value in the optimal stationary state.