Natural Products Genome Mining

Genome Mining - Extended Bioinformatics Solutions

Developing the next generation of bioinformatics solutions for genome mining for secondary/specialized metabolites.

Genome Mining of microbial genome data indicates that many microorganisms code for a plethora of biosynthetic gene clusters (BGCs) that code for the synthesis of so called natural products, or secondary/specialised metabolites – small molecules that often have high bioactivities. For decades, they have served as lead compounds of most antibiotics, as well as many other drugs.

The majority of these pathways are still unexplored, and their potential of being drug candidates is untested. In our interdisciplinary team of microbiologists, bioinformaticians and software engineers, we are developing methods and computer programs to efficiently mine genomic data for BGCs. As our next step, we will integrate this with other omics data sources that will help us to assessing potential functions, and provide information for future metabolic engineering strategies to produce our target compounds.

The overall aim of our group is to transform omics data of specialised metabolite producers into knowledge about the biosynthesis, production and application of their natural products, thus providing powerful tools for finding the next generation of antibiotics and other drugs.

Selected projects:

• The genome mining platform antiSMASH (collaboration with M. Medema’s group at Wageningen University), is currently the most comprehensive tool to analyse microbial genomes and identify potential BGCs. More than 700,000 antiSMASH runs have been carried by scientists all over the world on the public antiSMASH webserver, which is hosted by our group at DTU.
• The antiSMASH database provides easy access to several thousand pre-computed antiSMASH analyses, and allows identification of interesting BGCs by providing an extensive query interface on the annotation.
• The Minimum Information on Biosynthetic Gene Cluster standard MIBiG is a manually curated resource on experimentally studied BGCs, and serves as a reference dataset for researchers worldwide.

References

1. Blin, K., Shaw, S., Steinke, K., Villebro, R., Ziemert, N., Lee, S.Y., Medema, M.H., and Weber, T. (2019). antiSMASH 5.0: updates to the secondary metabolite genome mining pipeline. Nucleic Acids Res 47, W81-W87.
2. Blin, K., Pascal Andreu, V., de los Santos, E.L., Del Carratore, F., Lee, S.Y., Medema, M.H., and Weber, T. (2019). The antiSMASH database version 2: a comprehensive resource on secondary metabolite biosynthetic gene clusters. Nucleic Acids Res 47, D625-D630.
3. Kautsar, S.A., Blin, K., Shaw, S., Navarro-Munoz, J.C., Terlouw, B.R., van der Hooft, J.J.J., van Santen, J.A., Tracanna, V., Suarez Duran, H.G., Pascal Andreu, V., et al. (2020). MIBiG 2.0: a repository for biosynthetic gene clusters of known function. Nucleic Acids Res 48, D454-D458.

Cell factories for novel bioactive compounds

How can we most efficiently unleash the full potential of specialised actinobacterial metabolite producers?

Most antibiotics that we currently use to treat bacterial infections are natural products. Many of these life-saving compounds are produced by a group of soil bacteria called actinomycetes, with the genus Streptomyces being the most famous representative.

Although studied for many decades, genome mining studies indicate that these organisms still harbour a huge number of unknown biosynthetic gene clusters (BGCs) that code for the biosynthesis of yet to be discovered molecules.

In our group of microbiologists, bioengineers, chemists and bioinformaticians, we are developing and using molecular tools, which often are based on CRISPR technology, in order to access and utilise this huge potential.

By integrating these genome editing technologies with large-scale transcriptomics and metabolomics data, we aim to develop a platform that can be used for rapid experimental identification of computationally predicted BGCs. We can then use this knowledge to rationally engineer the strains and design expression strategies to produce the target compounds under given fermentation conditions.

Toolkits:

• Actinomycete CRISPR system^1,2 (Plasmids available at Addgene)
• CRISPR-BEST base editor system for actinomycetes³ (Plasmids available at Addgene)
• CRISPy-web sgRNA design software^4,5

References

1. Tong, Y. et al. CRISPR-Cas9-based, CRISPRi, and CRISPR-BEST-mediated genetic manipulation in streptomycetes. Nat. Protocols, in press. (2020).
2. Tong, Y., Charusanti, P., Zhang, L., Weber, T. & Lee, S. Y. CRISPR-Cas9 based engineering of actinomycetal genomes. ACS Synth. Biol. 4, 1020-1029, doi:10.1021/acssynbio.5b00038 (2015).
3. Tong, Y. et al. Highly efficient DSB-free base editing for streptomycetes with CRISPR-BEST. Proc. Natl. Acad. Sci. U. S. A. 116, 20366-20375, doi:10.1073/pnas.1913493116 (2019).
4. Blin, K., Lee, S. Y. & Weber, T. Designing sgRNAs with CRISPy-web. Lab Times, 53 (2017).
5. Blin, K., Pedersen, L. E., Weber, T. & Lee, S. Y. CRISPy-web: An online resource to design sgRNAs for CRISPR applications. Synth. Syst. Biotechnol. 1, 118-121, doi:10.1016/j.synbio.2016.01.003 (2016).