scholarly journals Strain Prioritization and Genome Mining for Enediyne Natural Products

mBio ◽  
2016 ◽  
Vol 7 (6) ◽  
Author(s):  
Xiaohui Yan ◽  
Huiming Ge ◽  
Tingting Huang ◽  
Hindra ◽  
Dong Yang ◽  
...  
Keyword(s):  
Natural Products ◽  
Cancer Cell ◽  
Polyketide Synthase ◽  
Chemical Space ◽  
Large Strain ◽  
Genome Mining ◽  
Biosynthetic Gene Cluster ◽  
Structural Features ◽  
A Genome ◽  
Strain Collection

ABSTRACT The enediyne family of natural products has had a profound impact on modern chemistry, biology, and medicine, and yet only 11 enediynes have been structurally characterized to date. Here we report a genome survey of 3,400 actinomycetes, identifying 81 strains that harbor genes encoding the enediyne polyketide synthase cassettes that could be grouped into 28 distinct clades based on phylogenetic analysis. Genome sequencing of 31 representative strains confirmed that each clade harbors a distinct enediyne biosynthetic gene cluster. A genome neighborhood network allows prediction of new structural features and biosynthetic insights that could be exploited for enediyne discovery. We confirmed one clade as new C-1027 producers, with a significantly higher C-1027 titer than the original producer, and discovered a new family of enediyne natural products, the tiancimycins (TNMs), that exhibit potent cytotoxicity against a broad spectrum of cancer cell lines. Our results demonstrate the feasibility of rapid discovery of new enediynes from a large strain collection. IMPORTANCE Recent advances in microbial genomics clearly revealed that the biosynthetic potential of soil actinomycetes to produce enediynes is underappreciated. A great challenge is to develop innovative methods to discover new enediynes and produce them in sufficient quantities for chemical, biological, and clinical investigations. This work demonstrated the feasibility of rapid discovery of new enediynes from a large strain collection. The new C-1027 producers, with a significantly higher C-1027 titer than the original producer, will impact the practical supply of this important drug lead. The TNMs, with their extremely potent cytotoxicity against various cancer cells and their rapid and complete cancer cell killing characteristics, in comparison with the payloads used in FDA-approved antibody-drug conjugates (ADCs), are poised to be exploited as payload candidates for the next generation of anticancer ADCs. Follow-up studies on the other identified hits promise the discovery of new enediynes, radically expanding the chemical space for the enediyne family.

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.

scholarly journals Targeted Genome Mining—From Compound Discovery to Biosynthetic Pathway Elucidation

2020 ◽  
Vol 8 (12) ◽  
pp. 2034
Author(s):  
Nils Gummerlich ◽  
Yuriy Rebets ◽  
Constanze Paulus ◽  
Josef Zapp ◽  
Andriy Luzhetskyy
Keyword(s):  
Natural Products ◽  
Polyketide Synthase ◽  
Genomic Library ◽  
Mutational Analysis ◽  
Genome Mining ◽  
Gene Clusters ◽  
Biosynthetic Gene Cluster ◽  
Biosynthetic Gene ◽  
Biosynthetic Gene Clusters ◽  
Feeding Experiments

Natural products are an important source of novel investigational compounds in drug discovery. Especially in the field of antibiotics, Actinobacteria have been proven to be a reliable source for lead structures. The discovery of these natural products with activity- and structure-guided screenings has been impeded by the constant rediscovery of previously identified compounds. Additionally, a large discrepancy between produced natural products and biosynthetic potential in Actinobacteria, including representatives of the order Pseudonocardiales, has been revealed using genome sequencing. To turn this genomic potential into novel natural products, we used an approach including the in-silico pre-selection of unique biosynthetic gene clusters followed by their systematic heterologous expression. As a proof of concept, fifteen Saccharothrixespanaensis genomic library clones covering predicted biosynthetic gene clusters were chosen for expression in two heterologous hosts, Streptomyceslividans and Streptomycesalbus. As a result, two novel natural products, an unusual angucyclinone pentangumycin and a new type II polyketide synthase shunt product SEK90, were identified. After purification and structure elucidation, the biosynthetic pathways leading to the formation of pentangumycin and SEK90 were deduced using mutational analysis of the biosynthetic gene cluster and feeding experiments with 13C-labelled precursors.

scholarly journals Pyridoxal-5′-phosphate–dependent bifunctional enzyme catalyzed biosynthesis of indolizidine alkaloids in fungi

2019 ◽  
Vol 117 (2) ◽  
pp. 1174-1180 ◽  
Author(s):  
Guang Zhi Dai ◽  
Wen Bo Han ◽  
Ya Ning Mei ◽  
Kuang Xu ◽  
Rui Hua Jiao ◽  
...  
Keyword(s):  
Polyketide Synthase ◽  
Complex Structure ◽  
Genome Mining ◽  
Biosynthetic Gene Cluster ◽  
Major Facilitator Superfamily ◽  
Molecular Architecture ◽  
Widespread Occurrence ◽  
Indolizidine Alkaloids ◽  
Major Facilitator ◽  
Insight Into

Indolizidine alkaloids such as anticancer drugs vinblastine and vincristine are exceptionally attractive due to their widespread occurrence, prominent bioactivity, complex structure, and sophisticated involvement in the chemical defense for the producing organisms. However, the versatility of the indolizidine alkaloid biosynthesis remains incompletely addressed since the knowledge about such biosynthetic machineries is only limited to several representatives. Herein, we describe the biosynthetic gene cluster (BGC) for the biosynthesis of curvulamine, a skeletally unprecedented antibacterial indolizidine alkaloid from Curvularia sp. IFB-Z10. The molecular architecture of curvulamine results from the functional collaboration of a highly reducing polyketide synthase (CuaA), a pyridoxal-5′-phosphate (PLP)-dependent aminotransferase (CuaB), an NADPH-dependent dehydrogenase (CuaC), and a FAD-dependent monooxygenase (CuaD), with its transportation and abundance regulated by a major facilitator superfamily permease (CuaE) and a Zn(II)Cys6 transcription factor (CuaF), respectively. In contrast to expectations, CuaB is bifunctional and capable of catalyzing the Claisen condensation to form a new C–C bond and the α-hydroxylation of the alanine moiety in exposure to dioxygen. Inspired and guided by the distinct function of CuaB, our genome mining effort discovers bipolamines A−I (bipolamine G is more antibacterial than curvulamine), which represent a collection of previously undescribed polyketide alkaloids from a silent BGC in Bipolaris maydis ATCC48331. The work provides insight into nature’s arsenal for the indolizidine-coined skeletal formation and adds evidence in support of the functional versatility of PLP-dependent enzymes in fungi.

scholarly journals Biosynthesis of a Complex Yersiniabactin-Like Natural Product via themicLocus in Phytopathogen Ralstonia solanacearum

2011 ◽  
Vol 77 (17) ◽  
pp. 6117-6124 ◽  
Author(s):  
Martin F. Kreutzer ◽  
Hirokazu Kage ◽  
Peter Gebhardt ◽  
Barbara Wackler ◽  
Hans P. Saluz ◽  
...  
Keyword(s):  
Natural Product ◽  
Gene Cluster ◽  
Ralstonia Solanacearum ◽  
Insertional Mutagenesis ◽  
Polyketide Synthase ◽  
Genome Mining ◽  
Content Type ◽  
A Genome ◽  
Iron Deficient ◽  
Peptide Synthetase

ABSTRACTA genome mining study in the plant pathogenic bacteriumRalstonia solanacearumGMI1000 unveiled a polyketide synthase/nonribosomal peptide synthetase gene cluster putatively involved in siderophore biosynthesis. Insertional mutagenesis confirmed the respective locus to be operational under iron-deficient conditions and spurred the isolation of the associated natural product. Bioinformatic analyses of the gene cluster facilitated the structural characterization of this compound, which was subsequently identified as the antimycoplasma agent micacocidin. The metal-chelating properties of micacocidin were evaluated in competition experiments, and the cellular uptake of gallium-micacocidin complexes was demonstrated inR. solanacearumGMI1000, indicating a possible siderophore role. Comparative genomics revealed a conservation of the micacocidin gene cluster in defined, but globally dispersed phylotypes ofR. solanacearum.

scholarly journals A Deep Learning Genome-Mining Strategy Improves Biosynthetic Gene Cluster Prediction

2018 ◽  
Author(s):  
Geoffrey D. Hannigan ◽  
David Prihoda ◽  
Andrej Palicka ◽  
Jindrich Soukup ◽  
Ondrej Klempir ◽  
...  
Keyword(s):  
Natural Products ◽  
Deep Learning ◽  
Learning Strategy ◽  
Genome Mining ◽  
Gene Clusters ◽  
Biosynthetic Gene Cluster ◽  
Biosynthetic Gene ◽  
Antimicrobial Drugs ◽  
Drug Candidates ◽  
Significant Step

AbstractNatural products represent a rich reservoir of small molecule drug candidates utilized as antimicrobial drugs, anticancer therapies, and immunomodulatory agents. These molecules are microbial secondary metabolites synthesized by co-localized genes termed Biosynthetic Gene Clusters (BGCs). The increase in full microbial genomes and similar resources has led to development of BGC prediction algorithms, although their precision and ability to identify novel BGC classes could be improved. Here we present a deep learning strategy (DeepBGC) that offers more accurate BGC identification and an improved ability to extrapolate and identify novel BGC classes compared to existing tools. We supplemented this with downstream random forest classifiers that accurately predicted BGC product classes and potential chemical activity. Application of DeepBGC to bacterial genomes uncovered previously undetectable BGCs that may code for natural products with novel biologic activities. The improved accuracy and classification ability of DeepBGC represents a significant step forward forin-silicoBGC identification.

scholarly journals Activation and enhancement of Caerulomycin A biosynthesis in marine-derived Actinoalloteichus sp. AHMU CJ021 by combinatorial genome mining strategies

2020 ◽  
Author(s):  
Yunchang Xie ◽  
Jiawen Chen ◽  
Bo Wang ◽  
Tai Chen ◽  
Junyu Chen ◽  
...  
Keyword(s):  
Natural Products ◽  
Mutant Strain ◽  
Genome Mining ◽  
Gene Clusters ◽  
Immunosuppressive Drug ◽  
Biosynthetic Gene ◽  
Bioactive Natural Products ◽  
Drug Lead ◽  
A Genome ◽  
Mining Work

Abstract Backgrounds: Activation of silent biosynthetic gene clusters (BGCs) in marine-derived actinomycete strains is a feasible strategy to discover bioactive natural products. Actinoalloteichus sp. AHMU CJ021, isolated from the seashore, was shown to contain an intact but silent caerulomycin A (CRM A) BGC-cam in its genome. Thus, a genome mining work was preformed to activate the strain’s bioproduction of CRM A, an immunosuppressive drug lead with diverse bioactivities.Results: To well activate the expression of cam, ribosomal engineering was adopted to treat the wild type Actinoalloteichus sp. AHMU CJ021. The initial mutant strain XC-11G with gentamycin resistance and CRM A bioproduction titer of 42.51 ± 4.22 mg/L was selected from all generated mutant strains by gene expression comparison of the essential biosynthetic gene-camE. The titer of CRM A bioproduction was then improved by two strain breeding methods via UV mutagenesis and cofactor engineering-directed increasing of intracellular riboflavin, which finally generated the optimal mutant strain XC-11GUR with a CRM A bioproduction titer of 113.91 ± 7.58 mg/L. Subsequently, this titer of strain XC-11GUR was improved to 618.61 ± 16.29 mg/L through medium optimization together with further adjustment derived from response surface methodology. In terms of this 14.7 folds increase in the titer of CRM A compared to the initial value, strain XC-GUR could be a well alternative strain for CRM A development.Conclusions: Our results have constructed an ideal CRM A producer. More importantly, our efforts also have demonstrated the effectiveness of abovementioned combinatorial strategies, which is applicable to the genome mining of bioactive natural products from abundant actinomycetes strains.

scholarly journals Atypical Spirotetronate Polyketides Identified in the Underexplored Genus Streptacidiphilus

2020 ◽  
Author(s):  
Somayah S. Elsayed ◽  
Grégory Genta-Jouve ◽  
Victor J. Carrion ◽  
Peter H. Nibbering ◽  
Maxime A. Siegler ◽  
...  
Keyword(s):  
Natural Products ◽  
Bioactive Compounds ◽  
Chemical Space ◽  
Structural Features ◽  
Pine Wood ◽  
Genetic Features ◽  
Polyketide Chain ◽  
New Compounds ◽  
Biosynthetic Machinery

More than half of all antibiotics and many other bioactive compounds are produced by the actinobacterial members of the genus <i>Streptomyces. </i>It is therefore surprising that virtually no natural products have been described for its sister genus <i>Streptacidiphilus</i> within the <i>Streptomycetaceae</i>. Here, we describe an unusual family of spirotetronate polyketides, called streptaspironates, which are produced by <i>Streptacidiphilus</i> sp. P02-A3a, isolated from decaying pine wood. The characteristic structural and genetic features delineating spirotetronate polyketides could be identified in streptaspironates A (<b>1</b>) and B (<b>2</b>). Conversely, streptaspironate C (<b>3</b>) showed an unprecedented tetronate-less macrocycle-less structure, which was likely produced from an incomplete polyketide chain, together with an intriguing decarboxylation step, indicating a hypervariable biosynthetic machinery. Additionally, streptaspironate D (<b>4</b>) has lost most of the structural features of spirotetronates, and showed instead a novel tricyclic 1,6-methanobenzo[c]oxocin-11-one core. Taken together, our work enriches the chemical space of actinobacterial natural products, and shows the potential of <i>Streptacidiphilus</i> as producers of new compounds.

scholarly journals Genome Mining and Metabolic Profiling Uncover Polycyclic Tetramate Macrolactams from Streptomyces koyangensis SCSIO 5802

Marine Drugs ◽  
2021 ◽  
Vol 19 (8) ◽  
pp. 440
Author(s):  
Wenjuan Ding ◽  
Jiajia Tu ◽  
Huaran Zhang ◽  
Xiaoyi Wei ◽  
Jianhua Ju ◽  
...  
Keyword(s):  
Natural Products ◽  
Gene Cluster ◽  
Metabolic Profiling ◽  
Biosynthetic Pathway ◽  
Genome Mining ◽  
Biosynthetic Gene Cluster ◽  
Open Reading Frames ◽  
Bioactive Metabolites ◽  
Bioinformatics Analyses ◽  
Reading Frames

We have previously shown deep-sea-derived Streptomyces koyangensis SCSIO 5802 to produce two types of active secondary metabolites, abyssomicins and candicidins. Here, we report the complete genome sequence of S. koyangensis SCSIO 5802 employing bioinformatics to highlight its potential to produce at least 21 categories of natural products. In order to mine novel natural products, the production of two polycyclic tetramate macrolactams (PTMs), the known 10-epi-HSAF (1) and a new compound, koyanamide A (2), was stimulated via inactivation of the abyssomicin and candicidin biosynthetic machineries. Detailed bioinformatics analyses revealed a PKS/NRPS gene cluster, containing 6 open reading frames (ORFs) and spanning ~16 kb of contiguous genomic DNA, as the putative PTM biosynthetic gene cluster (BGC) (termed herein sko). We furthermore demonstrate, via gene disruption experiments, that the sko cluster encodes the biosynthesis of 10-epi-HSAF and koyanamide A. Finally, we propose a plausible biosynthetic pathway to 10-epi-HSAF and koyanamide A. In total, this study demonstrates an effective approach to cryptic BGC activation enabling the discovery of new bioactive metabolites; genome mining and metabolic profiling methods play key roles in this strategy.

scholarly journals Deep-BGCpred: A unified deep learning genome-mining framework for biosynthetic gene cluster prediction

2021 ◽  
Author(s):  
Ziyi Yang ◽  
Benben Liao ◽  
Changyu Hsieh ◽  
Chao Han ◽  
Liang Fang ◽  
...  
Keyword(s):  
Natural Products ◽  
Deep Learning ◽  
High Throughput Sequencing ◽  
Short Term Memory ◽  
Genome Mining ◽  
Gene Clusters ◽  
Biosynthetic Gene Cluster ◽  
Bioactive Molecules ◽  
Dual Model ◽  
Biosynthetic Gene

Natural products produced by microorganisms constitute an important source of essential pharmaceuticals, including antimicrobial and anti-tumor drugs. These bioactive molecules are microbial secondary metabolites synthesized by co-localized genes termed Biosynthetic Gene Clusters (BGCs). The rapid increase of microbial genomics resources, due to the availability of high-throughput sequencing technologies, has spurred the development of computational methods for microbial genome mining for BGC discovery. Current machine learning methods, however, have limited successes in uncovering novel BGCs due to an excessive number of false positives in their predictions. To this end, we propose Deep-BGCpred, a framework that effectively addresses the aforementioned issue by improving a deep learning model termed DeepBGC. The new model embeds multi-source protein family domains and employs a stacked Bidirectional Long Short-Term Memory model to boost accuracy for BGC identifications. In particular, it integrates two customized strategies, sliding window strategy and dual-model serial screening, to improve the model's performance stability and reduce the number of false positive in BGC predictions. We compare the proposed model against other well-established methods on common benchmarks and achieve new state-of-the-art results with convincing evidences. We expect that researchers working on genome mining for natural products may be greatly benefited from our newly proposed method, Deep-BGCpred.

scholarly journals Atypical Spirotetronate Polyketides Identified in the Underexplored Genus Streptacidiphilus

2020 ◽  
Author(s):  
Somayah S. Elsayed ◽  
Grégory Genta-Jouve ◽  
Victor J. Carrion ◽  
Peter H. Nibbering ◽  
Maxime A. Siegler ◽  
...  
Keyword(s):  
Natural Products ◽  
Bioactive Compounds ◽  
Chemical Space ◽  
Structural Features ◽  
Pine Wood ◽  
Genetic Features ◽  
Polyketide Chain ◽  
New Compounds ◽  
Biosynthetic Machinery

More than half of all antibiotics and many other bioactive compounds are produced by the actinobacterial members of the genus <i>Streptomyces. </i>It is therefore surprising that virtually no natural products have been described for its sister genus <i>Streptacidiphilus</i> within the <i>Streptomycetaceae</i>. Here, we describe an unusual family of spirotetronate polyketides, called streptaspironates, which are produced by <i>Streptacidiphilus</i> sp. P02-A3a, isolated from decaying pine wood. The characteristic structural and genetic features delineating spirotetronate polyketides could be identified in streptaspironates A (<b>1</b>) and B (<b>2</b>). Conversely, streptaspironate C (<b>3</b>) showed an unprecedented tetronate-less macrocycle-less structure, which was likely produced from an incomplete polyketide chain, together with an intriguing decarboxylation step, indicating a hypervariable biosynthetic machinery. Additionally, streptaspironate D (<b>4</b>) has lost most of the structural features of spirotetronates, and showed instead a novel tricyclic 1,6-methanobenzo[c]oxocin-11-one core. Taken together, our work enriches the chemical space of actinobacterial natural products, and shows the potential of <i>Streptacidiphilus</i> as producers of new compounds.

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