scholarly journals Multiplexed CRISPR/Cas9- and TAR-Mediated Promoter Engineering of Natural Product Biosynthetic Gene Clusters in Yeast

2016 ◽  
Vol 5 (9) ◽  
pp. 1002-1010 ◽  
Author(s):  
Hahk-Soo Kang ◽  
Zachary Charlop-Powers ◽  
Sean F. Brady
Keyword(s):  
Natural Product ◽  
Gene Clusters ◽  
Biosynthetic Gene ◽  
Biosynthetic Gene Clusters ◽  
Promoter Engineering

scholarly journals Yeast homologous recombination-based promoter engineering for the activation of silent natural product biosynthetic gene clusters

2015 ◽  
Vol 112 (29) ◽  
pp. 8953-8958 ◽  
Author(s):  
Daniel Montiel ◽  
Hahk-Soo Kang ◽  
Fang-Yuan Chang ◽  
Zachary Charlop-Powers ◽  
Sean F. Brady
Keyword(s):  
Homologous Recombination ◽  
Natural Product ◽  
Gene Cluster ◽  
Large Scale ◽  
Gene Clusters ◽  
Marker Genes ◽  
Biosynthetic Gene ◽  
Biosynthetic Gene Clusters ◽  
Silent Gene ◽  
Promoter Engineering

Large-scale sequencing of prokaryotic (meta)genomic DNA suggests that most bacterial natural product gene clusters are not expressed under common laboratory culture conditions. Silent gene clusters represent a promising resource for natural product discovery and the development of a new generation of therapeutics. Unfortunately, the characterization of molecules encoded by these clusters is hampered owing to our inability to express these gene clusters in the laboratory. To address this bottleneck, we have developed a promoter-engineering platform to transcriptionally activate silent gene clusters in a model heterologous host. Our approach uses yeast homologous recombination, an auxotrophy complementation-based yeast selection system and sequence orthogonal promoter cassettes to exchange all native promoters in silent gene clusters with constitutively active promoters. As part of this platform, we constructed and validated a set of bidirectional promoter cassettes consisting of orthogonal promoter sequences, Streptomyces ribosome binding sites, and yeast selectable marker genes. Using these tools we demonstrate the ability to simultaneously insert multiple promoter cassettes into a gene cluster, thereby expediting the reengineering process. We apply this method to model active and silent gene clusters (rebeccamycin and tetarimycin) and to the silent, cryptic pseudogene-containing, environmental DNA-derived Lzr gene cluster. Complete promoter refactoring and targeted gene exchange in this “dead” cluster led to the discovery of potent indolotryptoline antiproliferative agents, lazarimides A and B. This potentially scalable and cost-effective promoter reengineering platform should streamline the discovery of natural products from silent natural product biosynthetic gene clusters.

scholarly journals Global analysis of adenylate-forming enzymes reveals β-lactone biosynthesis pathway in pathogenic Nocardia

2020 ◽  
Vol 295 (44) ◽  
pp. 14826-14839
Author(s):  
Serina L. Robinson ◽  
Barbara R. Terlouw ◽  
Megan D. Smith ◽  
Sacha J. Pidot ◽  
Timothy P. Stinear ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Product ◽  
Global Analysis ◽  
Gene Clusters ◽  
Divergent Evolution ◽  
Acid Activation ◽  
Biosynthetic Gene ◽  
Biosynthetic Gene Clusters ◽  
Predictive Tool ◽  
Wide Range

Enzymes that cleave ATP to activate carboxylic acids play essential roles in primary and secondary metabolism in all domains of life. Class I adenylate-forming enzymes share a conserved structural fold but act on a wide range of substrates to catalyze reactions involved in bioluminescence, nonribosomal peptide biosynthesis, fatty acid activation, and β-lactone formation. Despite their metabolic importance, the substrates and functions of the vast majority of adenylate-forming enzymes are unknown without tools available to accurately predict them. Given the crucial roles of adenylate-forming enzymes in biosynthesis, this also severely limits our ability to predict natural product structures from biosynthetic gene clusters. Here we used machine learning to predict adenylate-forming enzyme function and substrate specificity from protein sequences. We built a web-based predictive tool and used it to comprehensively map the biochemical diversity of adenylate-forming enzymes across >50,000 candidate biosynthetic gene clusters in bacterial, fungal, and plant genomes. Ancestral phylogenetic reconstruction and sequence similarity networking of enzymes from these clusters suggested divergent evolution of the adenylate-forming superfamily from a core enzyme scaffold most related to contemporary CoA ligases toward more specialized functions including β-lactone synthetases. Our classifier predicted β-lactone synthetases in uncharacterized biosynthetic gene clusters conserved in >90 different strains of Nocardia. To test our prediction, we purified a candidate β-lactone synthetase from Nocardia brasiliensis and reconstituted the biosynthetic pathway in vitro to link the gene cluster to the β-lactone natural product, nocardiolactone. We anticipate that our machine learning approach will aid in functional classification of enzymes and advance natural product discovery.

mpCRISTAR: Multiple Plasmid Approach for CRISPR/Cas9 and TAR-Mediated Multiplexed Refactoring of Natural Product Biosynthetic Gene Clusters

2019 ◽  
Vol 9 (1) ◽  
pp. 175-180 ◽  
Author(s):  
Hiyoung Kim ◽  
Chang-Hun Ji ◽  
Hyun-Woo Je ◽  
Jong-Pyung Kim ◽  
Hahk-Soo Kang
Keyword(s):  
Natural Product ◽  
Gene Clusters ◽  
Biosynthetic Gene ◽  
Biosynthetic Gene Clusters

scholarly journals Challenges and Advances in Genome Editing Technologies in Streptomyces

Biomolecules ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 734 ◽  
Author(s):  
Yawei Zhao ◽  
Guoquan Li ◽  
Yunliang Chen ◽  
Yinhua Lu
Keyword(s):  
Natural Product ◽  
Genome Editing ◽  
Genetic Manipulation ◽  
Chemical Compounds ◽  
Gene Clusters ◽  
Future Research ◽  
Biosynthetic Gene ◽  
Biosynthetic Gene Clusters ◽  
Research Focus ◽  
Experimental Conditions

The genome of Streptomyces encodes a high number of natural product (NP) biosynthetic gene clusters (BGCs). Most of these BGCs are not expressed or are poorly expressed (commonly called silent BGCs) under traditional laboratory experimental conditions. These NP BGCs represent an unexplored rich reservoir of natural compounds, which can be used to discover novel chemical compounds. To activate silent BGCs for NP discovery, two main strategies, including the induction of BGCs expression in native hosts and heterologous expression of BGCs in surrogate Streptomyces hosts, have been adopted, which normally requires genetic manipulation. So far, various genome editing technologies have been developed, which has markedly facilitated the activation of BGCs and NP overproduction in their native hosts, as well as in heterologous Streptomyces hosts. In this review, we summarize the challenges and recent advances in genome editing tools for Streptomyces genetic manipulation with a focus on editing tools based on clustered regularly interspaced short palindrome repeat (CRISPR)/CRISPR-associated protein (Cas) systems. Additionally, we discuss the future research focus, especially the development of endogenous CRISPR/Cas-based genome editing technologies in Streptomyces.

scholarly journals Genomic Assemblies of Members of Burkholderia and Related Genera as a Resource for Natural Product Discovery

2020 ◽  
Vol 9 (42) ◽  
Author(s):  
Alex J. Mullins ◽  
Cerith Jones ◽  
Matthew J. Bull ◽  
Gordon Webster ◽  
Julian Parkhill ◽  
...  
Keyword(s):  
Natural Product ◽  
Genome Mining ◽  
Genomic Analysis ◽  
Gene Clusters ◽  
Biosynthetic Gene ◽  
Biosynthetic Gene Clusters ◽  
Natural Product Discovery

ABSTRACT The genomes of 450 members of Burkholderiaceae, isolated from clinical and environmental sources, were sequenced and assembled as a resource for genome mining. Genomic analysis of the collection has enabled the identification of multiple metabolites and their biosynthetic gene clusters, including the antibiotics gladiolin, icosalide A, enacyloxin, and cepacin A.

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