Comparative Genomics and Metabolomics in the Genus Nocardia

Area of Science:

• Microbiology
• Genomics
• Natural Product Chemistry

Background:

• Automated genome analysis often struggles to correlate genetic variability in biosynthetic pathways with structural variations in compounds.
• This ambiguity hinders strain prioritization, compound identification, and can lead to overinterpretation of chemical diversity.
• The actinobacterial genus Nocardia, known for opportunistic human pathogens, remains underinvestigated for its metabolic potential.

Purpose of the Study:

• To assess the metabolic potential of Nocardia species.
• To investigate the correlation between genetic differences in biosynthetic gene clusters and structural variations in produced compounds, using nocobactin-like siderophores as a model.
• To establish a high-stringency genomics-driven approach for predicting natural product structural diversity.

Main Methods:

• Automated genome analysis to identify putative biosynthetic gene clusters (BGCs) in Nocardia.
• Biosynthetic Gene Similarity Clustering and Prospecting Engine (BiG-SCAPE) to generate sequence similarity networks of BGCs.
• Metabolic profiling using liquid chromatography-mass spectrometry (LC-MS), nuclear magnetic resonance (NMR) spectroscopy, and Global Natural Product Social molecular networking (GNPS).

Main Results:

• A wide array of putative BGCs, including polyketide, nonribosomal peptide, and terpenoid pathways, were identified in Nocardia.
• BiG-SCAPE analysis revealed distinct BGC families for nocobactin-like siderophores.
• Metabolic profiling confirmed that Nocardia BGC families with >70% similarity in BiG-SCAPE networks correspond to distinct structural types of nocobactin-like siderophores.

Conclusions:

• Nocardia represents a rich source of natural products, comparable to well-studied genera like Streptomyces.
• High-stringency analysis of BGCs using similarity networks effectively distinguishes and predicts natural product structural variations.
• This approach facilitates genomics-driven drug discovery by enabling better strain prioritization and compound identification.