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.
