Heterologous expression of the biosynthetic gene clusters of coumermycin A1, clorobiocin and caprazamycins in genetically modified Streptomyces coelicolor strains

Biopolymers ◽  
2010 ◽  
Vol 93 (9) ◽  
pp. 823-832 ◽  
Author(s):  
Katrin Flinspach ◽  
Lucia Westrich ◽  
Leonard Kaysser ◽  
Stefanie Siebenberg ◽  
Juan Pablo Gomez-Escribano ◽  
...  
Keyword(s):  
Heterologous Expression ◽  
Streptomyces Coelicolor ◽  
Genetically Modified ◽  
Gene Clusters ◽  
Biosynthetic Gene ◽  
Biosynthetic Gene Clusters

scholarly journals Heterologous Expression of Novobiocin and Clorobiocin Biosynthetic Gene Clusters

2005 ◽  
Vol 71 (5) ◽  
pp. 2452-2459 ◽  
Author(s):  
Alessandra S. Eustáquio ◽  
Bertolt Gust ◽  
Ute Galm ◽  
Shu-Ming Li ◽  
Keith F. Chater ◽  
...  
Keyword(s):  
Heterologous Expression ◽  
Antibiotic Production ◽  
Streptomyces Coelicolor ◽  
Attachment Site ◽  
Gene Clusters ◽  
Biosynthetic Gene ◽  
Biosynthetic Gene Clusters ◽  
Gene Deletions ◽  
Or Gene ◽  
Genetically Manipulated

ABSTRACT A method was developed for the heterologous expression of biosynthetic gene clusters in different Streptomyces strains and for the modification of these clusters by single or multiple gene replacements or gene deletions with unprecedented speed and versatility. λ-Red-mediated homologous recombination was used for genetic modification of the gene clusters, and the attachment site and integrase of phage φC31 were employed for the integration of these clusters into the heterologous hosts. This method was used to express the gene clusters of the aminocoumarin antibiotics novobiocin and clorobiocin in the well-studied strains Streptomyces coelicolor and Streptomyces lividans, which, in contrast to the natural producers, can be easily genetically manipulated. S. coelicolor M512 derivatives produced the respective antibiotic in yields comparable to those of natural producer strains, whereas S. lividans TK24 derivatives were at least five times less productive. This method could also be used to carry out functional investigations. Shortening of the cosmids' inserts showed which genes are essential for antibiotic production.

scholarly journals Functional Genome Mining for Metabolites Encoded by Large Gene Clusters through Heterologous Expression of a Whole-Genome Bacterial Artificial Chromosome Library in Streptomyces spp.

2016 ◽  
Vol 82 (19) ◽  
pp. 5795-5805 ◽  
Author(s):  
Min Xu ◽  
Yemin Wang ◽  
Zhilong Zhao ◽  
Guixi Gao ◽  
Sheng-Xiong Huang ◽  
...  
Keyword(s):  
Heterologous Expression ◽  
Genome Mining ◽  
Gene Clusters ◽  
Artificial Chromosome ◽  
Functional Screening ◽  
Biosynthetic Pathways ◽  
Biosynthetic Gene ◽  
Biosynthetic Gene Clusters ◽  
Content Type ◽  
Bac Libraries

ABSTRACTGenome sequencing projects in the last decade revealed numerous cryptic biosynthetic pathways for unknown secondary metabolites in microbes, revitalizing drug discovery from microbial metabolites by approaches called genome mining. In this work, we developed a heterologous expression and functional screening approach for genome mining from genomic bacterial artificial chromosome (BAC) libraries inStreptomycesspp. We demonstrate mining from a strain ofStreptomyces rochei, which is known to produce streptothricins and borrelidin, by expressing its BAC library in the surrogate hostStreptomyces lividansSBT5, and screening for antimicrobial activity. In addition to the successful capture of the streptothricin and borrelidin biosynthetic gene clusters, we discovered two novel linear lipopeptides and their corresponding biosynthetic gene cluster, as well as a novel cryptic gene cluster for an unknown antibiotic fromS. rochei. This high-throughput functional genome mining approach can be easily applied to other streptomycetes, and it is very suitable for the large-scale screening of genomic BAC libraries for bioactive natural products and the corresponding biosynthetic pathways.IMPORTANCEMicrobial genomes encode numerous cryptic biosynthetic gene clusters for unknown small metabolites with potential biological activities. Several genome mining approaches have been developed to activate and bring these cryptic metabolites to biological tests for future drug discovery. Previous sequence-guided procedures relied on bioinformatic analysis to predict potentially interesting biosynthetic gene clusters. In this study, we describe an efficient approach based on heterologous expression and functional screening of a whole-genome library for the mining of bioactive metabolites fromStreptomyces. The usefulness of this function-driven approach was demonstrated by the capture of four large biosynthetic gene clusters for metabolites of various chemical types, including streptothricins, borrelidin, two novel lipopeptides, and one unknown antibiotic fromStreptomyces rocheiSal35. The transfer, expression, and screening of the library were all performed in a high-throughput way, so that this approach is scalable and adaptable to industrial automation for next-generation antibiotic discovery.

scholarly journals Recapitulation of the evolution of biosynthetic gene clusters reveals hidden chemical diversity on bacterial genomes

2015 ◽  
Author(s):  
Pablo Cruz-Morales ◽  
Christian E. Martínez-Guerrero ◽  
Marco A. Morales-Escalante ◽  
Luis Yáñez-Guerra ◽  
Johannes Florian Kopp ◽  
...  
Keyword(s):  
Natural Products ◽  
Chemical Space ◽  
Streptomyces Coelicolor ◽  
Genome Mining ◽  
Gene Clusters ◽  
Biosynthetic Gene Cluster ◽  
Chemical Diversity ◽  
Biosynthetic Gene ◽  
Bacterial Genomes ◽  
Biosynthetic Gene Clusters

AbstractNatural products have provided humans with antibiotics for millennia. However, a decline in the pace of chemical discovery exerts pressure on human health as antibiotic resistance spreads. The empirical nature of current genome mining approaches used for natural products research limits the chemical space that is explored. By integration of evolutionary concepts related to emergence of metabolism, we have gained fundamental insights that are translated into an alternative genome mining approach, termed EvoMining. As the founding assumption of EvoMining is the evolution of enzymes, we solved two milestone problems revealing unprecedented conversions. First, we report the biosynthetic gene cluster of the ‘orphan’ metabolite leupeptin in Streptomyces roseus. Second, we discover an enzyme involved in formation of an arsenic-carbon bond in Streptomyces coelicolor and Streptomyces lividans. This work provides evidence that bacterial chemical repertoire is underexploited, as well as an approach to accelerate the discovery of novel antibiotics from bacterial genomes.

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