Article

Global analysis of biosynthetic gene clusters reveals vast potential of secondary metabolite production in Penicillium species

  • Nature Microbiology 2, Article number: 17044 (2017)
  • doi:10.1038/nmicrobiol.2017.44
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Abstract

Filamentous fungi produce a wide range of bioactive compounds with important pharmaceutical applications, such as antibiotic penicillins and cholesterol-lowering statins. However, less attention has been paid to fungal secondary metabolites compared to those from bacteria. In this study, we sequenced the genomes of 9 Penicillium species and, together with 15 published genomes, we investigated the secondary metabolism of Penicillium and identified an immense, unexploited potential for producing secondary metabolites by this genus. A total of 1,317 putative biosynthetic gene clusters (BGCs) were identified, and polyketide synthase and non-ribosomal peptide synthetase based BGCs were grouped into gene cluster families and mapped to known pathways. The grouping of BGCs allowed us to study the evolutionary trajectory of pathways based on 6-methylsalicylic acid (6-MSA) synthases. Finally, we cross-referenced the predicted pathways with published data on the production of secondary metabolites and experimentally validated the production of antibiotic yanuthones in Penicillia and identified a previously undescribed compound from the yanuthone pathway. This study is the first genus-wide analysis of the genomic diversity of Penicillia and highlights the potential of these species as a source of new antibiotics and other pharmaceuticals.

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Acknowledgements

This work was supported by the European Commission Marie Curie Initial Training Network Quantfung (FP7-People-2013-ITN, grant no. 607332), the Novo Nordisk Foundation and the Knut and Alice Wallenberg Foundation. The computations were performed using resources at the Chalmers Centre for Computational Science and Engineering (C3SE) provided by the Swedish National Infrastructure for Computing (SNIC). Sequencing support was provided by the Science for Life Laboratory (SciLifeLab), National Genomics Infrastructure (NGI) and UPPMAX (UPPNEX project ID no. b2014081). Support on genome annotation by the National Bioinformatics Infrastructure Sweden (NBIS) is acknowledged. Agilent Technologies is acknowledged for the Thought Leader Donation of the 6545 UHPLC-QTOF. The authors thank H. Wang for comments on the manuscript.

Author information

Affiliations

  1. Department of Biology and Biological Engineering, Chalmers University of Technology, SE412 96 Gothenburg, Sweden

    • Jens Christian Nielsen
    • , Sylvain Prigent
    • , Boyang Ji
    •  & Jens Nielsen
  2. Department of Biotechnology and Biomedicine, Technical University of Denmark, DK2800 Kgs. Lyngby, Denmark

    • Sietske Grijseels
    • , Kristian Fog Nielsen
    • , Jens Christian Frisvad
    •  & Mhairi Workman
  3. Department of Medical Biochemistry and Microbiology, Uppsala University, 751 23 Uppsala, Sweden

    • Jacques Dainat
  4. National Bioinformatics Infrastructure Sweden (NBIS), SciLifeLab, Uppsala University, 752 37 Uppsala, Sweden

    • Jacques Dainat
  5. Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK2800 Kgs. Lyngby, Denmark

    • Jens Nielsen

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Contributions

J.C.N., J.C.F., M.W. and J.N. conceived the study. J.C.N. designed and performed the bioinformatics computations, and analysed and interpreted the data. S.P. and B.J. assisted with bioinformatics design and interpretation. J.D. carried out the annotation of the genomes. S.G. and K.F.N. generated culture extracts and performed LC–MS analysis. J.C.N., S.G. and J.N. wrote the manuscript. All authors read and approved the final version of the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Jens Nielsen.

Supplementary information

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    Supplementary Information

    Supplementary Figures 1–12, Supplementary Table 1, Supplementary References.

Excel files

  1. 1.

    Supplementary Data 1 and 2

    Supplementary Data 1: Detected PKS containing BGCs mapped to BGCs in the MIBiG database. Supplementary Data 2: Detected NRPS containing BGCs mapped to BGCs in the MIBiG database.