Genetic platforms for heterologous expression of microbial natural products

Abstract

Covering: 2005 up to 2019Natural products are of paramount importance in human medicine. Not only are most antibacterial and anticancer drugs derived directly from or inspired by natural products, many other branches of medicine, such as immunology, neurology, and cardiology, have similarly benefited from natural product-based drugs. Typically, the genetic material required to synthesize a microbial specialized product is arranged in a multigene biosynthetic gene cluster (BGC), which codes for proteins associated with molecule construction, regulation, and transport. The ability to connect natural product compounds to BGCs and vice versa, along with ever-increasing knowledge of biosynthetic machineries, has spawned the field of genomics-guided natural product genome mining for the rational discovery of new chemical entities. One significant challenge in the field of natural product genome mining is how to rapidly link orphan biosynthetic genes to their associated chemical products. This review highlights state-of-the-art genetic platforms to identify, interrogate, and engineer BGCs from diverse microbial sources, which can be broken into three stages: (1) cloning and isolation of genomic loci, (2) heterologous expression in a host organism, and (3) genetic manipulation of cloned pathways. In the future, we envision natural product genome mining will be rapidly accelerated by de novo DNA synthesis and refactoring of whole biosynthetic pathways in combination with systematic heterologous expression methodologies.

Fig. 1

General workflow for (A) cloning, (B) heterologous expression, and (C) genetic manipulation of microbial BGCs.

Fig. 2

Chemical structures of natural products mentioned in this article.

Fig. 3

Streamlined design of universal BGC expression platforms. (A) Modular expression vector system, leveraging knock-in of modular expression elements for BGC integration across many hosts. (B) Single vector system, compatible across several engineered hosts. RG: resistance gene; att: attachment site.

Fig. 4

“Design, build, test” workflow for future natural product genome mining.