EvoMining reveals the origin and fate of natural products biosynthetic enzymes
Authors: Sélem-Mojica Nelly , Aguilar César , Gutiérrez-García Karina , Martínez-Guerrero Christian E. , Barona-Gómez Francisco 
Published: November 2018

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 SUMMARY

Databases 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.

IMPACT STATEMENT

EvoMining allows studying expansion-and-recruitment events of enzyme families in prokaryotic lineages, with the goal of providing both evolutionary insights and a genome mining approach for the discovery of truly novel natural products biosynthetic gene clusters. Thus, by better understanding the origin and fate of gene copies within enzyme families, this work contributes towards the identification of lineage-dependent enzymes that we call ‘shell’ enzymes, which are ideal beacons to unveil ‘chemical dark matter’. We show that enzyme functionality is a continuum, including transition enzymes located between central and specialized metabolism. To exemplify these evolutionary dynamics, we focused in the genes directing the synthesis of the sunscreen peptide scytonemin, as the two key enzymes of this biosynthetic pathway behave as shell enzymes and were correctly identified by EvoMining. We also show how evolutionary approaches are better suited to study unexplored lineages, such as those belonging to the Archaea domain, which is systematically mined here for novel natural products for the first time. The release of EvoMining as a stand-alone tool will allow researchers to explore its own enzyme families of interest, within their own genomic lineages of expertise, by taking into account the lessons learned from this work