Genome Mining of α-Pyrone Natural Products from Ascidian-Derived Fungus Amphichordafelina SYSU-MS7908

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Abstract

Culturing ascidian-derived fungus Amphichorda felina SYSU-MS7908 under standard laboratory conditions mainly yielded meroterpenoid, and nonribosomal peptide-type natural products. We sequenced the genome of Amphichorda felina SYSU-MS7908 and found 56 biosynthetic gene clusters (BGCs) after bioinformatics analysis, suggesting that the majority of those BGCSs are silent. Here we report our genome mining effort on one cryptic BGC by heterologous expression in Aspergillus oryzae NSAR1, and the identification of two new α-pyrone derivatives, amphichopyrone A (1) and B (2), along with a known compound, udagawanone A (3). Anti-inflammatory activities were performed, and amphichopyrone A (1) and B (2) displayed potent anti-inflammatory activity by inhibiting nitric oxide (NO) production in RAW264.7 cells with IC50 values 18.09 ± 4.83 and 7.18 ± 0.93 μM, respectively.

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Discovery of microbial natural products by activation of silent biosynthetic gene clusters.

Peter Rutledge, Gregory Challis (2015)

Microorganisms produce a wealth of structurally diverse specialized metabolites with a remarkable range of biological activities and a wide variety of applications in medicine and agriculture, such as the treatment of infectious diseases and cancer, and the prevention of crop damage. Genomics has revealed that many microorganisms have far greater potential to produce specialized metabolites than was thought from classic bioactivity screens; however, realizing this potential has been hampered by the fact that many specialized metabolite biosynthetic gene clusters (BGCs) are not expressed in laboratory cultures. In this Review, we discuss the strategies that have been developed in bacteria and fungi to identify and induce the expression of such silent BGCs, and we briefly summarize methods for the isolation and structural characterization of their metabolic products.

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clinker & clustermap.js: automatic generation of gene cluster comparison figures

Cameron Gilchrist, Yit-Heng Chooi, Peter Robinson (2021)

Genes involved in biological pathways are often collocalised in gene clusters, the comparison of which can give valuable insights into their function and evolutionary history. However, comparison and visualization of gene cluster similarity is a tedious process, particularly when many clusters are being compared. Here, we present clinker, a Python based tool and clustermap.js, a companion JavaScript visualization library, which used together can automatically generate accurate, interactive, publication-quality gene cluster comparison figures directly from sequence files. Source code and documentation for clinker and clustermap.js is available on GitHub (github.com/gamcil/clinker and github.com/gamcil/clustermap.js, respectively) under the MIT license. clinker can be installed directly from the Python Package Index via pip. Supplementary data are available at Bioinformatics online.

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antiSMASH 3.0—a comprehensive resource for the genome mining of biosynthetic gene clusters

Tilmann Weber, Kai Blin, Srikanth Duddela … (2015)

Microbial secondary metabolism constitutes a rich source of antibiotics, chemotherapeutics, insecticides and other high-value chemicals. Genome mining of gene clusters that encode the biosynthetic pathways for these metabolites has become a key methodology for novel compound discovery. In 2011, we introduced antiSMASH, a web server and stand-alone tool for the automatic genomic identification and analysis of biosynthetic gene clusters, available at http://antismash.secondarymetabolites.org. Here, we present version 3.0 of antiSMASH, which has undergone major improvements. A full integration of the recently published ClusterFinder algorithm now allows using this probabilistic algorithm to detect putative gene clusters of unknown types. Also, a new dereplication variant of the ClusterBlast module now identifies similarities of identified clusters to any of 1172 clusters with known end products. At the enzyme level, active sites of key biosynthetic enzymes are now pinpointed through a curated pattern-matching procedure and Enzyme Commission numbers are assigned to functionally classify all enzyme-coding genes. Additionally, chemical structure prediction has been improved by incorporating polyketide reduction states. Finally, in order for users to be able to organize and analyze multiple antiSMASH outputs in a private setting, a new XML output module allows offline editing of antiSMASH annotations within the Geneious software.