Bacterial natural product discovery by heterologous expression

Abstract

Abstract   Natural products have found important applications in the pharmaceutical and agricultural sectors. In bacteria, the genes that encode the biosynthesis of natural products are often colocalized in the genome, forming biosynthetic gene clusters. It has been predicted that only 3% of natural products encoded in bacterial genomes have been discovered thus far, in part because gene clusters may be poorly expressed under laboratory conditions. Heterologous expression can help convert bioinformatics predictions into products. However, challenges remain, such as gene cluster prioritization, cloning of the complete gene cluster, high level expression, product identification, and isolation of products in practical yields. Here we reviewed the literature from the past 5 years (January 2018 to June 2023) to identify studies that discovered natural products by heterologous expression. From the 50 studies identified, we present analyses of the rationale for gene cluster prioritization, cloning methods, biosynthetic class, source taxa, and host choice. Combined, the 50 studies led to the discovery of 63 new families of natural products, supporting heterologous expression as a promising way to access novel chemistry. However, the success rate of natural product detection varied from 11% to 32% based on four large-scale studies that were part of the reviewed literature. The low success rate makes it apparent that much remains to be improved. The potential reasons for failure and points to be considered to improve the chances of success are discussed. One-Sentence Summary At least 63 new families of bacterial natural products were discovered using heterologous expression in the last 5 years, supporting heterologous expression as a promising way to access novel chemistry; however, the success rate is low (11–32%) making it apparent that much remains to be improved—we discuss the potential reasons for failure and points to be considered to improve the chances of success. BioRender was used to generate the graphical abstract figure.