Heterologous Expression in Anabaena of the Columbamide Pathway from the Cyanobacterium Moorena bouillonii and Production of New Analogs

Columbamides are chlorinated acyl amide natural products, several of which exhibit cannabinomimetic activity. These compounds were originally discovered from a culture of the filamentous marine cyanobacterium Moorena bouillonii PNG5-198 collected from the coastal waters of Papua New Guinea. The columbamide biosynthetic gene cluster (BGC) had been identified using bioinformatics, but not confirmed by experimental evidence. Here, we report the heterologous expression in Anabaena (Nostoc) PCC 7120 of the 28.5 kb BGC that encodes for columbamide biosynthesis. The production of columbamides in Anabaena is investigated under several different culture conditions, and several new columbamide analogs are identified by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and nuclear magnetic resonance (NMR). In addition to previously characterized columbamides A, B, and C, new columbamides I-M are produced in these experiments, and the structure of the most abundant monochlorinated analog, columbamide K (11), is fully characterized. The other new columbamide analogs are produced in only small quantities, and structures are proposed based on high-resolution-MS, MS/MS, and 1H NMR data. Overexpression of the pathway's predicted halogenases resulted in increased productions of di- and trichlorinated compounds. The most significant change in production of columbamides in Anabaena is correlated with the concentration of NaCl in the medium

A Convolutional Neural Network-Based Approach for the Rapid Annotation of Molecularly Diverse Natural Products

This report describes the first application of the novel NMR-based machine learning tool "Small Molecule Accurate Recognition Technology" (SMART 2.0) for mixture analysis and subsequent accelerated discovery and characterization of new natural products. The concept was applied to the extract of a filamentous marine cyanobacterium known to be a prolific producer of cytotoxic natural products. This environmental Symploca extract was roughly fractionated, and then prioritized and guided by cancer cell cytotoxicity, NMR-based SMART 2.0, and MS2-based molecular networking. This led to the isolation and rapid identification of a new chimeric swinholide-like macrolide, symplocolide A, as well as the annotation of swinholide A, samholides A-I, and several new derivatives. The planar structure of symplocolide A was confirmed to be a structural hybrid between swinholide A and luminaolide B by 1D/2D NMR and LC-MS2 analysis. A second example applies SMART 2.0 to the characterization of structurally novel cyclic peptides, and compares this approach to the recently appearing "atomic sort" method. This study exemplifies the revolutionary potential of combined traditional and deep learning-assisted analytical approaches to overcome longstanding challenges in natural products drug discovery

Feature-based molecular networking in the GNPS analysis environment

Molecular networking has become a key method to visualize and annotate the chemical space in non-targeted mass spectrometry data. We present feature-based molecular networking (FBMN) as an analysis method in the Global Natural Products Social Molecular Networking (GNPS) infrastructure that builds on chromatographic feature detection and alignment tools. FBMN enables quantitative analysis and resolution of isomers, including from ion mobility spectrometry

Feature-based molecular networking in the GNPS analysis environment.

Molecular networking has become a key method to visualize and annotate the chemical space in non-targeted mass spectrometry data. We present feature-based molecular networking (FBMN) as an analysis method in the Global Natural Products Social Molecular Networking (GNPS) infrastructure that builds on chromatographic feature detection and alignment tools. FBMN enables quantitative analysis and resolution of isomers, including from ion mobility spectrometry

ReDU: a framework to find and reanalyze public mass spectrometry data

We present ReDU (https://redu.ucsd.edu/), a system for metadata capture of public mass spectrometry-based metabolomics data, with validated controlled vocabularies. Systematic capture of knowledge enables the reanalysis of public data and/or co-analysis of one’s own data. ReDU enables multiple types of analyses, including finding chemicals and associated metadata, comparing the shared and different chemicals between groups of samples, and metadata-filtered, repository-scale molecular networking. © 2020, The Author(s), under exclusive licence to Springer Nature America, Inc