Advances in mining and expressing microbial biosynthetic gene clusters.

Area of Science:

• Microbiology
• Bioinformatics
• Synthetic Biology

Background:

• Microbial natural products (NPs) are vital for biomedical, industrial, and agricultural applications.
• A significant challenge in NP discovery is the inability to culture many environmental microbes, hindering biosynthetic gene cluster (BGC) mining.
• Extremophilic microbes, in particular, harbor a vast, unexplored reservoir of novel BGCs.

Approach:

• This review summarizes bioinformatics tools and databases for mining BGCs from both pure cultures and environmental metagenomes.
• It highlights the increasing use of machine learning and deep learning for predicting specific NP classes, such as ribosomally synthesized and post-translationally modified peptides (RiPPs).
• The review also covers synthetic biology techniques for expressing identified BGCs in culturable hosts.

Key Points:

• High-throughput sequencing and advanced bioinformatics tools enable direct BGC exploration from unculturable microbial genomes.
• Machine learning and deep learning approaches are significantly improving the prediction and discovery of novel RiPPs.
• Synthetic biology offers pathways to express and produce NPs from newly discovered BGCs.

Conclusions:

• The integration of advanced bioinformatics and synthetic biology provides a powerful pipeline for discovering and producing novel natural products.
• This approach unlocks the potential of unculturable microbes, expanding the repertoire of available NPs for diverse applications.
• Continued development in these areas will accelerate the exploitation of Earth's microbial BGCs.