Phylogenomic Analysis of Natural Products Biosynthetic Gene Clusters Allows Discovery of Arseno-Organic Metabolites in Model Streptomycetes

Abstract

Natural products from microbes have provided humans with beneficial antibiotics for millennia. However, a decline in the pace of antibiotic discovery exerts pressure on human health as antibiotic resistance spreads, a challenge that may better faced by unveiling chemical diversity produced by microbes. Current microbial genome mining approaches have revitalized research into antibiotics, but the empirical nature of these methods limits the chemical space that is explored.Here, we address the problem of finding novel pathways by incorporating evolutionary principles into genome mining. We recapitulated the evolutionary history of twenty-three enzyme families previously uninvestigated in the context of natural product biosynthesis in Actinobacteria, the most proficient producers of natural products. Our genome evolutionary analyses where based on the assumption that expanded—repurposed enzyme families—from central metabolism, occur frequently and thus have the potential to catalyze new conversions in the context of natural products biosynthesis. Our analyses led to the discovery of biosynthetic gene clusters coding for hidden chemical diversity, as validated by comparing our predictions with those from state-of-the-art genome mining tools; as well as experimentally demonstrating the existence of a biosynthetic pathway for arseno-organic metabolites in Streptomyces coelicolor and Streptomyces lividans, Using a gene knockout and metabolite profile combined strategy.As our approach does not rely solely on sequence similarity searches of previously identified biosynthetic enzymes, these results establish the basis for the development of an evolutionary-driven genome mining tool termed EvoMining that complements current platforms. We anticipate that by doing so real ‘chemical dark matter’ will be unveiled.

Antibiotic drug discovery

“…As proof of principles, the authors screened more than 1000 bacterial genomes and detected about 30 000 putative biosynthetic gene clusters. Another method that addresses the problem of finding novel pathways is based on evolutionary distances and was called EvoMining (Cruz‐Morales et al ., 2016). EvoMining is based on the assumption that most enzymes used in secondary metabolism have been duplicated and evolved from primary metabolism (Cruz‐Morales et al ., 2016).…”

“…Another method that addresses the problem of finding novel pathways is based on evolutionary distances and was called EvoMining (Cruz‐Morales et al ., 2016). EvoMining is based on the assumption that most enzymes used in secondary metabolism have been duplicated and evolved from primary metabolism (Cruz‐Morales et al ., 2016). By recapitulating the evolutionary history of 23 enzyme families previously uninvestigated in the context of natural product biosynthesis, biosynthetic gene clusters coding for hidden chemical diversity were discovered.…”

“…By recapitulating the evolutionary history of 23 enzyme families previously uninvestigated in the context of natural product biosynthesis, biosynthetic gene clusters coding for hidden chemical diversity were discovered. This phylogenomic analysis resulted in the discovery of arseno‐organic metabolites in Streptomycetes coelicolor and Streptomyces lividans (Cruz‐Morales et al ., 2016). …”

Toward Systems Metabolic Engineering of Streptomycetes for Secondary Metabolites Production

“…[20] Another recently released approach called EvoMining employs phylogenomic analysis to identify repurposed enzymes, originating from primary metabolism, which has been recruited to secondary metabolism for the secondary metabolite biosynthesis. [26] Using this EvoMining approach, Cruz-Morales et al identified a new clade of biosynthetic enzymes involved in the biosynthesis of arseno-organic metabolites. [26] In addition, an alternative way of mining BGCs is found in the Antibiotics Resistant Target Seeker (ARTS) (https://arts2.ziemertlab.com), which evaluates BGCs based on self-resistance mechanisms of antibiotic producers.…”

“…[26] Using this EvoMining approach, Cruz-Morales et al identified a new clade of biosynthetic enzymes involved in the biosynthesis of arseno-organic metabolites. [26] In addition, an alternative way of mining BGCs is found in the Antibiotics Resistant Target Seeker (ARTS) (https://arts2.ziemertlab.com), which evaluates BGCs based on self-resistance mechanisms of antibiotic producers. [27] ARTS scans genomes for promising BGCs based on known resistances and the presence of duplicated, co-localized housekeeping genes that display an evidence of horizontal gene transfer.…”