Modern Approaches to Genome Mining for the Development of New Anti-infectives: In Silico Gene Prediction and Experimental Metabolomics

Genome mining consists in assessing the potential encoded in the genome of microorganisms to produce novel secondary metabolites. Actinobacteria have been reported to hold unexplored potential for the biosynthesis of secondary metabolites, according to the number of gene clusters predicted from recently published genome sequences. This is of significant interest in the area of anti-infectives, since many of the secondary metabolites produced by Actinobacteria have been reported to have antibacterial, antiviral and antitumor properties. The first part of this review offers an overview on in silico bioinformatics software and databases for the prediction of gene clusters involved in the production of putative secondary metabolites. The second part of this review encompasses experimental metabolomics techniques, facilitated by mass spectrometry and quantitative proteomics, all of which have the end goal to identify and characterize secondary metabolites. Examples where metabolomics were associated with computational prediction tools to propose the link between genes and metabolites have been highlighted. As an addition, this review also explores the potential of the OSMAC and co-culturing experimental approaches to induce the expression of silent gene clusters under laboratory conditions. Examples are offered of novel secondary metabolites and gene clusters discovered following a genome mining approach.

Keywords: Actinobacteria, Antibiotics, Anti-infectives, Antimicrobials, Bioinformatics, Biosynthetic pathways, Co-culturing, Cryptic cluster, Gene cluster, Genome mining, Homologous expression, Mass spectrometry, Metabolomics, Natural products, Nonribosomal peptide, OSMAC, Polyketide, Secondary metabolites, Silent cluster.