EvoMining reveals the origin and fate of natural product biosynthetic enzymes

Abstract

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.

Integration of machine learning and pan-genomics expands the biosynthetic landscape of RiPP natural products

“…This raises the question of whether entirely novel classes of natural products could still be discovered. A few genome mining methods, such as ClusterFinder 16 and EvoMining 17,18 , have tried to tackle this problem. These methods either use criteria true of all BGCs or build around the evolutionary properties of gene families found in BGCs, rather than using specific BGC-class-specific genetic markers.…”

Cryptic or Silent? The Known Unknowns, Unknown Knowns, and Unknown Unknowns of Secondary Metabolism

“…The same evolutionary rationale applies to the duplication of some housekeeping genes, which confer resistance to the antibiotic, so-called target duplication ( 25 ). Broadly speaking, these evolutionarily guided approaches can expand the predictions of commonly used BGC prediction algorithms by up to 26% ( 12 ) and can potentially identify BGCs for natural products that have novel modes of action ( 25 ). With the exciting expansion of genomic, metagenomic, and metabolomic resources and their integration with both small- and large-scale functional studies ( 26 ), we owe it to ourselves to be precise in our terminology.…”

ActDES – a curated Actinobacterial Database for Evolutionary Studies

“…As a result, computational 'mining' of actinobacterial genomes has become an important part of the drug-discovery pipeline, with increasing numbers of online resources and software devoted to identification of natural-product biosynthetic gene clusters (BGCs) [7][8][9]. It is important to move beyond approaches that rely on similarity searches of known BGCs and to expand searches to identify hidden chemical diversity within the genomes [6,7,[10][11][12][13].…”