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.

The entomopathogenic nematode Steinernema carpocapsae has been widely used for the biological control of insect pests. It shares a symbiotic relationship with the bacterium Xenorhabdus nematophila, and is emerging as a genetic model to study symbiosis and pathogenesis. We obtained a high-quality draft of the nematode’s genome comprising 84,613,633 bp in 347 scaffolds, with an N50 of 1.24 Mb. To improve annotation, we sequenced both short and long RNA and conducted shotgun proteomic analyses. S. carpocapsae shares orthologous genes with other parasitic nematodes that are absent in the free-living nematode C. elegans, it has ncRNA families that are enriched in parasites, and expresses proteins putatively associated with parasitism and pathogenesis, suggesting an active role for the nematode during the pathogenic process. Host and parasites might engage in a co-evolutionary arms-race dynamic with genes participating in their interaction showing signatures of positive selection. Our analyses indicate that the consequence of this arms race is better characterized by positive selection altering specific functions instead of just increasing the number of positively selected genes, adding a new perspective to these co-evolutionary theories. We identified a protein, ATAD-3, that suggests a relevant role for mitochondrial function in the evolution and mechanisms of nematode parasitism.

Natural products (NPs), or specialized metabolites, are important for medicine and agriculture alike, and for the fitness of the organisms that produce them. NP genome-mining aims at extracting biosynthetic information from the genomes of microbes presumed to produce these compounds. Typically, canonical enzyme sequences from known biosynthetic systems are identified after sequence similarity searches. Despite this being an efficient process, the likelihood of identifying truly novel systems by this approach is low. To overcome this limitation, we previously introduced EvoMining, a genome-mining approach that incorporates evolutionary principles. Here, we release and use our latest EvoMining version, which includes novel visualization features and customizable databases, to analyse 42 central metabolic enzyme families (EFs) conserved throughout Actinobacteria , Cyanobacteria , Pseudomonas and Archaea. We found that expansion-and-recruitment profiles of these 42 families are lineage specific, opening the metabolic space related to ‘shell’ enzymes. These enzymes, which have been overlooked, are EFs with orthologues present in most of the genomes of a taxonomic group, but not in all. As a case study of canonical shell enzymes, we characterized the expansion and recruitment of glutamate dehydrogenase and acetolactate synthase into scytonemin biosynthesis, and into other central metabolic pathways driving Archaea and Bacteria adaptive evolution. By defining the origin and fate of enzymes, EvoMining complements traditional genome-mining approaches as an unbiased strategy and opens the door to gaining insights into the evolution of NP biosynthesis. We anticipate that EvoMining will be broadly used for evolutionary studies, and for generating predictions of unprecedented chemical scaffolds and new antibiotics. This article contains data hosted by Microreact.

Natural products, or specialized metabolites, are important for medicine and agriculture alike, as well as for the fitness of the organisms that produce them. Microbial genome mining aims at extracting metabolic information from genomes of microbes presumed to produce these compounds. Typically, canonical enzyme sequences from known biosynthetic systems are identified after sequence similarity searches. Despite this being an efficient process the likelihood of identifying truly novel biosynthetic systems is low. To overcome this limitation we previously introduced EvoMining, a genome mining approach that incorporates evolutionary principles.Here, we release and use our latest version of EvoMining, which includes novel visualization features and customizable databases, to analyze 42 central metabolic enzyme families conserved throughout Actinobacteria, Cyanobacteria, Pseudomonas and Archaea. We found that expansion-and-recruitment profiles of these enzyme families are lineage specific, opening a new metabolic space related to 'shell' enzymes, which have been overlooked to date. As a case study of canonical shell enzymes, we characterized the expansion and recruitment of glutamate dehydrogenase and acetolactate synthase into scytonemin biosynthesis, and into other central metabolic pathways driving microbial adaptive evolution. By defining the origins and fates of metabolic enzymes, EvoMining not only complements traditional genome mining approaches as an unbiased and rule-independent strategy, but it opens the door to gain insights into the evolution of natural products biosynthesis. We anticipate that EvoMining will be broadly used for metabolic evolutionary studies, and to generate genome-mining predictions leading to unprecedented chemical scaffolds and new antibiotics. DATA SUMMARYDatabases have been deposited at Zenodo; DOI: 10.5281/zenodo.1162336 http://zenodo.org/deposit/1219709 Trees and metadata have been deposited in MicroReact GDH Actinobacteria https://microreact.org/project/r1IhjVm6X GDH Cyanobacteria https://microreact.org/project/HyjYUN7pQ) GDH Pseudomonas https://microreact.org/project/rJPC4EQa7 GDH Archaea https://microreact.org/project/ByUcvNmaX ALS Cyanobacteria https://microreact.org/project/B11HkUtdm EvoMining code has been deposited in gitHub https://github/nselem/evomining Docker container in Dockerhub https:// hub.docker.com/r/nselem/evomining/ We confirm all supporting data, code and protocols have been provided within the article or through supplementary data files.