Evolutionary age of genes can assist in genome mining

Abstract

The rate of sequencing microbial genomes is accelerating, with the hope of discovering new antibiotics, cures for various diseases or new industrial en-zymes. However, about 25-30% of the genes in the sequenced microbial genomes do not have an assigned function. Predicting the functions of these “unknown” genes could unlock a considerable biological potential for biomedical and biotechnology applications, as well as further our understanding of the molecular tenets of life. Current methods for gene mining rely basically on comparison of primary sequences or 3D-structures to those of already characterized genes. The problem with such approaches is that unknown genes with no homology to the already characterized genes remain completely out of reach. Herein, I argue that evolutionary approaches, such as the genomic phylostratigraphy, can make a substantial contribution to genome mining – especially regarding genes with no homology to the characterized ones. My group has recently used genomic phylostratigraphy to discover new genes involved in sporulation of the bacterial model organism Bacillus subtilis . These new sporulation genes exhibited no sequence homology with the known sporulation genes and were missed by all other genome mining approaches. They have been discovered solely based on their evolutionary age. Along these lines, I argue that phylostratigraphy should be integrated into genome mining pipelines and develop a brief example of how this could be done .