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 A Single Biosynthetic Gene Cluster Is Responsible for the Production of Bagremycin Antibiotics and Ferroverdin Iron Chelators

mBio ◽  
2019 ◽  
Vol 10 (4) ◽  
Author(s):  
Loïc Martinet ◽  
Aymeric Naômé ◽  
Benoit Deflandre ◽  
Marta Maciejewska ◽  
Déborah Tellatin ◽  
...  
Keyword(s):  
Natural Product ◽  
Gene Cluster ◽  
Biosynthetic Pathway ◽  
Genome Mining ◽  
Gene Clusters ◽  
Bioactive Molecules ◽  
Bioactive Metabolites ◽  
Biosynthetic Gene ◽  
Single Family ◽  
Biosynthetic Gene Clusters

ABSTRACT Biosynthetic gene clusters (BGCs) are organized groups of genes involved in the production of specialized metabolites. Typically, one BGC is responsible for the production of one or several similar compounds with bioactivities that usually only vary in terms of strength and/or specificity. Here we show that the previously described ferroverdins and bagremycins, which are families of metabolites with different bioactivities, are produced from the same BGC, whereby the fate of the biosynthetic pathway depends on iron availability. Under conditions of iron depletion, the monomeric bagremycins are formed, representing amino-aromatic antibiotics resulting from the condensation of 3-amino-4-hydroxybenzoic acid with p-vinylphenol. Conversely, when iron is abundantly available, the biosynthetic pathway additionally produces a molecule based on p-vinylphenyl-3-nitroso-4-hydroxybenzoate, which complexes iron to form the trimeric ferroverdins that have anticholesterol activity. Thus, our work shows a unique exception to the concept that BGCs should only produce a single family of molecules with one type of bioactivity and that in fact different bioactive molecules may be produced depending on the environmental conditions. IMPORTANCE Access to whole-genome sequences has exposed the general incidence of the so-called cryptic biosynthetic gene clusters (BGCs), thereby renewing their interest for natural product discovery. As a consequence, genome mining is the often first approach implemented to assess the potential of a microorganism for producing novel bioactive metabolites. By revealing a new level of complexity of natural product biosynthesis, we further illustrate the difficulty of estimation of the panel of molecules associated with a BGC based on genomic information alone. Indeed, we found that the same gene cluster is responsible for the production of compounds which differ in terms of structure and bioactivity. The production of these different compounds responds to different environmental triggers, which suggests that multiplication of culture conditions is essential for revealing the entire panel of molecules made by a single BGC.

scholarly journals Comparative Genomics and Metabolomics in the Genus Nocardia

mSystems ◽  
2020 ◽  
Vol 5 (3) ◽  
Author(s):  
Daniel Männle ◽  
Shaun M. K. McKinnie ◽  
Shrikant S. Mantri ◽  
Katharina Steinke ◽  
Zeyin Lu ◽  
...  
Keyword(s):  
Natural Product ◽  
Gene Cluster ◽  
Gene Clusters ◽  
Chemical Diversity ◽  
Biosynthetic Gene ◽  
Compound Identification ◽  
Biosynthetic Gene Clusters ◽  
Metabolomics Data ◽  
Similarity Networks ◽  
Gene Similarity

ABSTRACT Using automated genome analysis tools, it is often unclear to what degree genetic variability in homologous biosynthetic pathways relates to structural variation. This hampers strain prioritization and compound identification and can lead to overinterpretation of chemical diversity. Here, we assessed the metabolic potential of Nocardia, an underinvestigated actinobacterial genus that is known to comprise opportunistic human pathogens. Our analysis revealed a plethora of putative biosynthetic gene clusters of various classes, including polyketide, nonribosomal peptide, and terpenoid pathways. Furthermore, we used the highly conserved biosynthetic pathway for nocobactin-like siderophores to investigate how gene cluster differences correlate to structural differences in the produced compounds. Sequence similarity networks generated by BiG-SCAPE (Biosynthetic Gene Similarity Clustering and Prospecting Engine) showed the presence of several distinct gene cluster families. Metabolic profiling of selected Nocardia strains using liquid chromatography-mass spectrometry (LC-MS) metabolomics data, nuclear magnetic resonance (NMR) spectroscopy, and GNPS (Global Natural Product Social molecular networking) revealed that nocobactin-like biosynthetic gene cluster (BGC) families above a BiG-SCAPE threshold of 70% can be assigned to distinct structural types of nocobactin-like siderophores. IMPORTANCE Our work emphasizes that Nocardia represent a prolific source for natural products rivaling better-characterized genera such as Streptomyces or Amycolatopsis. Furthermore, we showed that large-scale analysis of biosynthetic gene clusters using similarity networks with high stringency allows the distinction and prediction of natural product structural variations. This will facilitate future genomics-driven drug discovery campaigns.

scholarly journals Identification of Putative Biosynthetic Gene Clusters for Tolyporphins in Multiple Filamentous Cyanobacteria

Life ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 758
Author(s):  
Xiaohe Jin ◽  
Yunlong Zhang ◽  
Ran Zhang ◽  
Kathy-Uyen Nguyen ◽  
Jonathan S. Lindsey ◽  
...  
Keyword(s):  
Gene Cluster ◽  
Gene Clusters ◽  
Biosynthetic Gene Cluster ◽  
Filamentous Cyanobacterium ◽  
Biosynthetic Gene ◽  
Filamentous Cyanobacteria ◽  
Biosynthetic Gene Clusters ◽  
Common Component ◽  
Homology Searching ◽  
Similar Gene

Tolyporphins A–R are unusual tetrapyrrole macrocycles produced by the non-axenic filamentous cyanobacterium HT-58-2. A putative biosynthetic gene cluster for biosynthesis of tolyporphins (here termed BGC-1) was previously identified in the genome of HT-58-2. Here, homology searching of BGC-1 in HT-58-2 led to identification of similar BGCs in seven other filamentous cyanobacteria, including strains Nostoc sp. 106C, Nostoc sp. RF31YmG, Nostoc sp. FACHB-892, Brasilonema octagenarum UFV-OR1, Brasilonema octagenarum UFV-E1, Brasilonema sennae CENA114 and Oculatella sp. LEGE 06141, suggesting their potential for tolyporphins production. A similar gene cluster (BGC-2) also was identified unexpectedly in HT-58-2. Tolyporphins BGCs were not identified in unicellular cyanobacteria. Phylogenetic analysis based on 16S rRNA and a common component of the BGCs, TolD, points to a close evolutionary history between each strain and their respective tolyporphins BGC. Though identified with putative tolyporphins BGCs, examination of pigments extracted from three cyanobacteria has not revealed the presence of tolyporphins. Overall, the identification of BGCs and potential producers of tolyporphins presents a collection of candidate cyanobacteria for genetic and biochemical analysis pertaining to these unusual tetrapyrrole macrocycles.

scholarly journals Refactoring the formicamycin biosynthetic gene cluster to make high-level producing strains and new molecules

2020 ◽  
Author(s):  
Rebecca Devine ◽  
Hannah McDonald ◽  
Zhiwei Qin ◽  
Corinne Arnold ◽  
Katie Noble ◽  
...  
Keyword(s):  
Gene Cluster ◽  
Large Scale ◽  
Liquid Culture ◽  
Gene Clusters ◽  
Biosynthetic Gene Cluster ◽  
Resistant Bacteria ◽  
Drug Resistant ◽  
Biosynthetic Gene ◽  
Biosynthetic Genes

AbstractThe formicamycins are promising antibiotics with potent activity against Gram-positive pathogens including VRE and MRSA and display a high barrier to selection of resistant isolates. They were first identified in Streptomyces formicae KY5, which produces the formicamycins at low levels on solid agar but not in liquid culture, thus hindering further investigation of these promising antibacterial compounds. We hypothesised that by understanding the organisation and regulation of the for biosynthetic gene cluster, we could rationally refactor the cluster to increase production levels. Here we report that the for biosynthetic gene cluster consists of 24 genes expressed on nine transcripts. Seven of these transcripts, including those containing all the major biosynthetic genes, are repressed by the MarR-regulator ForJ which also controls the expression of the ForGF two-component system that initiates biosynthesis. A third cluster-situated regulator, ForZ, autoregulates and controls production of the putative MFS transporter ForAA. Consistent with these findings, deletion of forJ increased formicamycin biosynthesis 5-fold, while over-expression of forGF in the ΔforJ background increased production 10-fold compared to the wild-type. De-repression by deleting forJ also switched on biosynthesis in liquid-culture and induced the production of two novel formicamycin congeners. By combining mutations in regulatory and biosynthetic genes, six new biosynthetic precursors with antibacterial activity were also isolated. This work demonstrates the power of synthetic biology for the rational redesign of antibiotic biosynthetic gene clusters both to engineer strains suitable for fermentation in large scale bioreactors and to generate new molecules.ImportanceAntimicrobial resistance is a growing threat as existing antibiotics become increasingly ineffective against drug resistant pathogens. Here we determine the transcriptional organisation and regulation of the gene cluster encoding biosynthesis of the formicamycins, promising new antibiotics with activity against drug resistant bacteria. By exploiting this knowledge, we construct stable mutant strains which over-produce these molecules in both liquid and solid culture whilst also making some new compound variants. This will facilitate large scale purification of these molecules for further study including in vivo experiments and the elucidation of their mechanism of action. Our work demonstrates that understanding the regulation of natural product biosynthetic pathways can enable rational improvement of the producing strains.

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.

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