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

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.

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

10.21203/rs.3.rs-27100/v2 ◽

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 production of CRM A, an immunosuppressive drug lead with diverse bioactivities.Results: To well activate the expression of cam, ribosome engineering was adopted to treat the wild type Actinoalloteichus sp. AHMU CJ021. The initial mutant strain XC-11G with gentamycin resistance and CRM A production 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 production was then improved by two strain breeding methods via UV mutagenesis and cofactor engineering-directed increase of intracellular riboflavin, which finally generated the optimal mutant strain XC-11GUR with a CRM A production 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 14.6 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.

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

10.21203/rs.3.rs-27100/v3 ◽

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 production of CRM A, an immunosuppressive drug lead with diverse bioactivities.Results: To well activate the expression of cam , ribosome engineering was adopted to treat the wild type Actinoalloteichus sp. AHMU CJ021. The initial mutant strain XC-11G with gentamycin resistance and CRM A production 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 production was then improved by two strain breeding methods via UV mutagenesis and cofactor engineering-directed increase of intracellular riboflavin, which finally generated the optimal mutant strain XC-11GUR with a CRM A production 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 14.6 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.

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

Microbial Cell Factories ◽

10.1186/s12934-020-01418-w ◽

2020 ◽

Vol 19 (1) ◽

Cited By ~ 1

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 Background 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 production of CRM A, an immunosuppressive drug lead with diverse bioactivities. Results To well activate the expression of cam, ribosome engineering was adopted to treat the wild type Actinoalloteichus sp. AHMU CJ021. The initial mutant strain XC-11G with gentamycin resistance and CRM A production 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 production was then improved by two strain breeding methods via UV mutagenesis and cofactor engineering-directed increase of intracellular riboflavin, which finally generated the optimal mutant strain XC-11GUR with a CRM A production 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 14.6 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 had constructed an ideal CRM A producer. More importantly, our efforts also had demonstrated the effectiveness of abovementioned combinatorial strategies, which is applicable to the genome mining of bioactive natural products from abundant actinomycetes strains.

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Expanding the Natural Products Heterologous Expression Repertoire in the Model Cyanobacterium Anabaena sp. Strain PCC 7120: Production of Pendolmycin and Teleocidin B-4

10.26434/chemrxiv.11316098.v1 ◽

2019 ◽

Cited By ~ 1

Author(s):

Patrick Videau ◽

Kaitlyn Wells ◽

Arun Singh ◽

Jessie Eiting ◽

Philip Proteau ◽

...

Keyword(s):

Natural Products ◽

Genome Mining ◽

Gene Clusters ◽

Combinatorial Biosynthesis ◽

Test Case ◽

Biosynthetic Gene ◽

Biosynthetic Gene Clusters ◽

Cyanobacterium Anabaena ◽

Anabaena Sp ◽

Pcc 7120

Cyanobacteria are prolific producers of natural products and genome mining has shown that many orphan biosynthetic gene clusters can be found in sequenced cyanobacterial genomes. New tools and methodologies are required to investigate these biosynthetic gene clusters and here we present the use of <i>Anabaena </i>sp. strain PCC 7120 as a host for combinatorial biosynthesis of natural products using the indolactam natural products (lyngbyatoxin A, pendolmycin, and teleocidin B-4) as a test case. We were able to successfully produce all three compounds using codon optimized genes from Actinobacteria. We also introduce a new plasmid backbone based on the native <i>Anabaena</i>7120 plasmid pCC7120ζ and show that production of teleocidin B-4 can be accomplished using a two-plasmid system, which can be introduced by co-conjugation.

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

10.1101/500694 ◽

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.

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Whole-genome sequence of bioactive streptomycete derived from mangrove forest in Malaysia, Streptomyces sp. MUSC 14

Progress In Microbes & Molecular Biology ◽

10.36877/pmmb.a0000195 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Hooi-Leng Ser ◽

Loh Teng-Hern Tan ◽

Wen-Si Tan ◽

Wai-Fong Yin ◽

Kok-Gan Chan

Keyword(s):

East Coast ◽

Genetic Manipulation ◽

Genome Mining ◽

Gene Clusters ◽

Peninsular Malaysia ◽

Whole Genome Sequence ◽

Biosynthetic Gene ◽

Biosynthetic Gene Clusters ◽

Streptomyces Sp ◽

A Genome

The contribution of streptomycetes to human health is undeniably important and significant, given that these filamentous microbes can produce interesting compounds that can be used to cure deadly infections and even cancer. Isolated from the east coast of Peninsular Malaysia, Streptomyces sp. MUSC 14 has shown significant antioxidant capacity. The current study explores the genomic potential of MUSC 14 via a genome mining approach. The genome size of MUSC 14 is 10,274,825 bp with G + C content of 71.3 %. AntiSMASH analysis revealed a total of nine biosynthetic gene clusters (with more than 80 % similarities to known gene clusters). This information serves as an important foundation for subsequent studies, particularly the purification and isolation of bioactive compounds by genetic manipulation techniques.

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Further biochemical profiling of Hypholoma fasciculare metabolome reveals its chemogenetic diversity

10.1101/2020.05.28.122176 ◽

2020 ◽

Author(s):

Suhad A.A. Al-Salihi ◽

Ian Bull ◽

Raghad A. Al-Salhi ◽

Paul J. Gates ◽

Kifah Salih ◽

...

Keyword(s):

Natural Products ◽

Chemical Properties ◽

Structural Diversity ◽

Gene Clusters ◽

Biosynthetic Gene Cluster ◽

Biosynthetic Gene ◽

Microbial Drug Resistance ◽

Bioactive Natural Products ◽

Highly Active ◽

Comprehensive Picture

AbstractThere is a desperate need in continuing the search for natural products with novel mechanism to battle the constant increase of microbial drug resistance. Previously mushroom forming fungi were neglected as a source of novel antibiotics, due to the difficulties associated with their culture preparation and genetic tractability. However, modern fungal molecular and synthetic biology tools, renewed the interest in exploring mushroom fungi for novel therapeutics. The aim of this study was to have a comprehensive picture of nine basidiomycetes secondary metabolites (SM), screen their biological and chemical properties to describe the genetic pathways associated with their production. H. fasciculare revealed to be highly active antagonistic species, with antimicrobial activity against three different microorganisms - Bacillus subtilis, Escherichia coli and Saccharomyces cerevisiae-. Extensive genomic comparison and chemical analysis using analytical chromatography, led to the characterisation of more than 15 variant biosynthetic gene clusters and the first identification of a potent antibacterial metabolite-3, 5-dichloromethoxy benzoic acid (3, 5-D)-in this species, for which a biosynthetic gene cluster was predicted. This work demonstrates the great potential of mushroom forming fungi as a reservoir of bioactive natural products which are currently unexplored, and that access to their genomic data and structural diversity natural products via utilizing modern computational analysis and efficient chemical methods, could accelerate the development and applications of such distinct molecules in both pharmaceutical and agrochemical industry.

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Recent development of antiSMASH and other computational approaches to mine secondary metabolite biosynthetic gene clusters

Briefings in Bioinformatics ◽

10.1093/bib/bbx146 ◽

2017 ◽

Vol 20 (4) ◽

pp. 1103-1113 ◽

Cited By ~ 37

Author(s):

Kai Blin ◽

Hyun Uk Kim ◽

Marnix H Medema ◽

Tilmann Weber

Keyword(s):

Natural Products ◽

Small Molecules ◽

Sequence Similarity ◽

Genome Mining ◽

Gene Clusters ◽

Biosynthetic Gene ◽

Biosynthetic Gene Clusters ◽

Rule Based ◽

Chemical Structures ◽

Annotation Quality

Abstract Many drugs are derived from small molecules produced by microorganisms and plants, so-called natural products. Natural products have diverse chemical structures, but the biosynthetic pathways producing those compounds are often organized as biosynthetic gene clusters (BGCs) and follow a highly conserved biosynthetic logic. This allows for the identification of core biosynthetic enzymes using genome mining strategies that are based on the sequence similarity of the involved enzymes/genes. However, mining for a variety of BGCs quickly approaches a complexity level where manual analyses are no longer possible and require the use of automated genome mining pipelines, such as the antiSMASH software. In this review, we discuss the principles underlying the predictions of antiSMASH and other tools and provide practical advice for their application. Furthermore, we discuss important caveats such as rule-based BGC detection, sequence and annotation quality and cluster boundary prediction, which all have to be considered while planning for, performing and analyzing the results of genome mining studies.

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Further Biochemical Profiling of Hypholoma fasciculare Metabolome Reveals Its Chemogenetic Diversity

Frontiers in Bioengineering and Biotechnology ◽

10.3389/fbioe.2021.567384 ◽

2021 ◽

Vol 9 ◽

Author(s):

Suhad A. A. Al-Salihi ◽

Ian D. Bull ◽

Raghad Al-Salhi ◽

Paul J. Gates ◽

Kifah S. M. Salih ◽

...

Keyword(s):

Natural Products ◽

Chemical Properties ◽

Gene Clusters ◽

Biosynthetic Gene Cluster ◽

Biosynthetic Gene ◽

Biosynthetic Gene Clusters ◽

Microbial Drug Resistance ◽

Bioactive Natural Products ◽

Highly Active ◽

Hypholoma Fasciculare

Natural products with novel chemistry are urgently needed to battle the continued increase in microbial drug resistance. Mushroom-forming fungi are underutilized as a source of novel antibiotics in the literature due to their challenging culture preparation and genetic intractability. However, modern fungal molecular and synthetic biology tools have renewed interest in exploring mushroom fungi for novel therapeutic agents. The aims of this study were to investigate the secondary metabolites of nine basidiomycetes, screen their biological and chemical properties, and then investigate the genetic pathways associated with their production. Of the nine fungi selected, Hypholoma fasciculare was revealed to be a highly active antagonistic species, with antimicrobial activity against three different microorganisms: Bacillus subtilis, Escherichia coli, and Saccharomyces cerevisiae. Genomic comparisons and chromatographic studies were employed to characterize more than 15 biosynthetic gene clusters and resulted in the identification of 3,5-dichloromethoxy benzoic acid as a potential antibacterial compound. The biosynthetic gene cluster for this product is also predicted. This study reinforces the potential of mushroom-forming fungi as an underexplored reservoir of bioactive natural products. Access to genomic data, and chemical-based frameworks, will assist the development and application of novel molecules with applications in both the pharmaceutical and agrochemical industries.

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

10.1101/2021.11.15.468547 ◽

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.

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