Nonribosomal Peptide Synthesis-Principles and Prospects

The vast majority of systemic bacterial infections are caused by facultative, often antibiotic-resistant, pathogens colonizing human body surfaces. Nasal carriage of Staphylococcus aureus predisposes to invasive infection, but the mechanisms that permit or interfere with pathogen colonization are largely unknown. Whereas soil microbes are known to compete by production of antibiotics, such processes have rarely been reported for human microbiota. We show that nasal Staphylococcus lugdunensis strains produce lugdunin, a novel thiazolidine-containing cyclic peptide antibiotic that prohibits colonization by S. aureus, and a rare example of a non-ribosomally synthesized bioactive compound from human-associated bacteria. Lugdunin is bactericidal against major pathogens, effective in animal models, and not prone to causing development of resistance in S. aureus. Notably, human nasal colonization by S. lugdunensis was associated with a significantly reduced S. aureus carriage rate, suggesting that lugdunin or lugdunin-producing commensal bacteria could be valuable for preventing staphylococcal infections. Moreover, human microbiota should be considered as a source for new antibiotics.

Fungi produce an impressive array of secondary metabolites (SMs) including mycotoxins, antibiotics and pharmaceuticals. The genes responsible for their biosynthesis, export, and transcriptional regulation are often found in contiguous gene clusters. To facilitate annotation of these clusters in sequenced fungal genomes, we developed the web-based software SMURF (www.jcvi.org/smurf/) to systematically predict clustered SM genes based on their genomic context and domain content. We applied SMURF to catalog putative clusters in 27 publicly available fungal genomes. Comparison with genetically characterized clusters from six fungal species showed that SMURF accurately recovered all clusters and detected additional potential clusters. Subsequent comparative analysis revealed the striking biosynthetic capacity and variability of the fungal SM pathways and the correlation between unicellularity and the absence of SMs. Further genetics studies are needed to experimentally confirm these clusters. 2010 Elsevier Inc. All rights reserved.

Covering: 2006 to 2016The computational mining of genomes has become an important part in the discovery of novel natural products as drug leads. Thousands of bacterial genome sequences are publically available these days containing an even larger number and diversity of secondary metabolite gene clusters that await linkage to their encoded natural products. With the development of high-throughput sequencing methods and the wealth of DNA data available, a variety of genome mining methods and tools have been developed to guide discovery and characterisation of these compounds. This article reviews the development of these computational approaches during the last decade and shows how the revolution of next generation sequencing methods has led to an evolution of various genome mining approaches, techniques and tools. After a short introduction and brief overview of important milestones, this article will focus on the different approaches of mining genomes for secondary metabolites, from detecting biosynthetic genes to resistance based methods and "evo-mining" strategies including a short evaluation of the impact of the development of genome mining methods and tools on the field of natural products and microbial ecology.