Marine Natural Products

Natural Products

About 9 months ago, I started my postdoc in a brand new field, natural products. Instead of focusing on the processes contributing to microbial diversity and diversification, the objective is to utilize microbes for the discovery of new natural products. As a total novice, I was blown away by the amazing work being done to quantify single microbial compounds. So, as I look back on what I learned over the past few months, I just wanted to highlight some of the amazing and novel techniques being used to give us insights into the structural diversity and complexity of natural products. This is my (very) humble understanding of natural products and definitely going to be a bit biased towards Scripps and marine sediment bacterium!

Natural products (NPs) are a broad definition including the production of small molecules (usually in the form of secondary metabolites) derived from natural environments. The discovery of these compounds from nature have been instrumental in modern medicine (thanks penicillin!) with some estimates ranging from 50-70% of clinical drugs are derived from natural products or its derivatives. This led to the explosion of new natural products (right Figure) over the first few decades, but the last 10-20 years has seen this number plateaued. Further, we are limited in our ability to describe new natural products with novel structures. One reason for this is the detection of these compounds are mostly derived from bioassay or chemical signature-guided isolation [1]. As such, researchers are turning to new methods to identify novel NPs and elucidate their structures. Below I will outline a few approaches I have seen in my brief time as a postdoc in this field. 

Number of compounds published per year and rate of novel compound isolation as a percentage of total natural product isolation

Genome Mining for NPs

Of course I would start here! Obviously, the major advances in sequencing technology and costs have allowed for extensive mining of genomes for genetic signatures related to the production of natural products. In bacteria, these genes are typically clustered in the genome to build proteins in a modular fashion (akin to a car assembly line). Modules within these biosynthetic gene clusters (BGCs) enable the loading, attachment, and extension of building blocks to produce NPs (below Figure).

The salinosporamide A  BGC from Salinispora tropica [2], a NP known for its protesome inhibition properties and currently in clinical trials

The most studied BGCs allowing for identification of these genetic signatures are the polyketide synthases (PKSs) and the nonribosomal peptide-synthetase (NRPS) enzymes. Tools such as antiSMASH and NaPDoS can identify BGC regions and give a rough estimate to known, well-characterized BGCs. However, linking these BGCs to compounds remains a critical step for NP research. More recent tools, such as BiG-SCAPE, seek to compare BGCs across strains to cluster and dereplicate BGCs. Still subtle differences in the BGC (gene gain/loss) can result in variants of molecules and affect their potency and, therefore, their application to human health.

Decades of work identifying novel genomic signatures for BGCs has allowed for extensive surveys across bacterial taxa. Most notably are the Actinobacteria, which include the well-studied NP producing genera such as Streptomyces and Salinispora. Genome mining has revealed large portions of the genome can be dedicated to the production of NPs. This computational approach assesses the entire biosynthetic potential of an organism rather than examining individual metabolites (of which may or may not be expressed in culture conditions due to regulation or environmental signaling).

Mass Spectrometry

Traditionally, strains are grown in culture and crude extracts are examined to determine which products a bacteria might be producing. This led to the one strain many compounds (OSMAC) approach to characterize diverse compounds in different culture conditions (see below figure). However, analyzing these crude extracts are difficult as secondary metabolites are highly diverse in their size, structure, and physicochemical properties. Instruments such as the LC-MS (liquid chromatography - mass spectrometry) can separate and identify masses of compounds, but still an organisms can produce hundreds of compounds in any given sample. Much work is still to be done, but analytical tools, such as GNPS and MZMine, can aid with the data processing and identification (and dereplication) to characterize compounds.

Identification of molecules A) 614.27 m/z and B) 754.44 m/z in high temperature culturing conditions. C) Molecular networking based on MS2 spectra (via GNPS) clustered both masses with a known natural product (red node) [3].

Heterologous Expression of BGCs 

​Linking the identification of BGCs to their products using mass spec is really difficult. As such, most of the "low-hanging fruit" have been characterized in most model organisms for natural products research. This has led to some creative approaches to identify novel secondary metabolites. With the advances in computational tools, exploring biosynthetic potential in the genome has revealed a number of "orphan" BGCs in genomes, or identified BGCs that have yet to be linked to its corresponding molecule.

Detection of the thiolactomycin (tlm) (structure f) and its analogues (1-4). The tlm BGC (A) and HPLC chromatograms from the natural producing strain (D), and the host without the vector (C) and expressing the vector (B) [4].

One way to express and characterize these molecules is through the heterologous expression of whole BGCs. This requires cloning the BGC into a vector and then finding a compatible host to express the BGC. This is another complicated factor as expression of BGCs in hosts can lead to inactivation as this process is highly dependent on host machinery, biochemistry, genetics, etc. However, utilizing this approach can identify the compound of the BGC easily in a host. For example, Salinispora can act as a heterologous host in expressing large BGCs by directly introducing cloned vectors (see left figure)[4].

Papers:

1. Pye CR, Bertin MJ, Lokey RS, Gerwick WH, Linington RG. (2017). Retrospective analysis of natural products provides insights for future discovery trends. Proceedings to the National Academy of Sciences 114(22): 5601-5606.

2. Eustáquio AS, McGlinchey RP, Liu Y, Hazzard C, Beer LL, Florova G, Alhamadsheh MM, Lechner A, Kale AJ, Kobayashi Y, Reynolds KA, Moore BS. (2009). Biosynthesis of the salinosporamide A polyketide synthase substrate chloroethylmalonyl-coenzyme A from S-adenosyl-l-methionine. Proceedings to the National Academy of Sciences 106(30): 12295-12300.

3. Sidebottom AM, Johnson AR, Karty JA, Trader DJ, Carlson EE. (2013). Integrated metabolomics approach facilitates discovery of an unpredicted natural product suite from Streptomyces coelicolor M145. ACS Chemical Biology 8: 2009-2016.

4. Zhang JJ, Moore BS, Tang X. (2018). Engineering Salinispora tropica for heterologous expression of natural product biosynthetic gene clusters. Applied Microbiology and Biotechnology 102(19): 8437-8446.