With an ever-increasing amount of (meta)genomic data being deposited in sequence databases, (meta)genome mining for natural product biosynthetic pathways occupies a critical role in the discovery of novel pharmaceutical drugs, crop protection agents and biomaterials. The genes that encode these pathways are often organised into biosynthetic gene clusters (BGCs). In 2015, we defined the Minimum Information about a Biosynthetic Gene cluster (MIBiG): a standardised data format that describes the minimally required information to uniquely characterise a BGC. We simultaneously constructed an accompanying online database of BGCs, which has since been widely used by the community as a reference dataset for BGCs and was expanded to 2021 entries in 2019 (MIBiG 2.0). Here, we describe MIBiG 3.0, a database update comprising large-scale validation and re-annotation of existing entries and 661 new entries. Particular attention was paid to the annotation of compound structures and biological activities, as well as protein domain selectivities. Together, these new features keep the database up-to-date, and will provide new opportunities for the scientific community to use its freely available data, e.g. for the training of new machine learning models to predict sequence-structure-function relationships for diverse natural products. MIBiG 3.0 is accessible online at https://mibig.secondarymetabolites.org/.

We review known evolutionary mechanisms underlying the overwhelming chemical diversity of bacterial natural products biosynthesis, focusing on enzyme promiscuity and the evolution of enzymatic domains that enable metabolic traits.

BackgroundEscherichia coli strains lacking the phosphoenolpyruvate: carbohydrate phosphotransferase system (PTS), which is the major bacterial component involved in glucose transport and its phosphorylation, accumulate high amounts of phosphoenolpyruvate that can be diverted to the synthesis of commercially relevant products. However, these strains grow slowly in glucose as sole carbon source due to its inefficient transport and metabolism. Strain PB12, with 400% increased growth rate, was isolated after a 120 hours adaptive laboratory evolution process for the selection of faster growing derivatives in glucose. Analysis of the genetic changes that occurred in the PB12 strain that lacks PTS will allow a better understanding of the basis of its growth adaptation and, therefore, in the design of improved metabolic engineering strategies for enhancing carbon diversion into the aromatic pathways.ResultsWhole genome analyses using two different sequencing methodologies: the Roche NimbleGen Inc. comparative genome sequencing technique, and high throughput sequencing with Illumina Inc. GAIIx, allowed the identification of the genetic changes that occurred in the PB12 strain. Both methods detected 23 non-synonymous and 22 synonymous point mutations. Several non-synonymous mutations mapped in regulatory genes (arcB, barA, rpoD, rna) and in other putative regulatory loci (yjjU, rssA and ypdA). In addition, a chromosomal deletion of 10,328 bp was detected that removed 12 genes, among them, the rppH, mutH and galR genes. Characterization of some of these mutated and deleted genes with their functions and possible functions, are presented.ConclusionsThe deletion of the contiguous rppH, mutH and galR genes that occurred simultaneously, is apparently the main reason for the faster growth of the evolved PB12 strain. In support of this interpretation is the fact that inactivation of the rppH gene in the parental PB11 strain substantially increased its growth rate, very likely by increasing glycolytic mRNA genes stability. Furthermore, galR inactivation allowed glucose transport by GalP into the cell. The deletion of mutH in an already stressed strain that lacks PTS is apparently responsible for the very high mutation rate observed.

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.

The ptsHIcrr operon was deleted from Escherichia coli wild-type JM101 to generate strain PB11 (PTS^–). In a mutant derived from PB11 that partially recovered its growth capacity on glucose by an adaptive evolution process (PB12, PTS^–Glc⁺), part of the phosphoenolpyruvate not used in glucose transport has been utilized for the synthesis of aromatic compounds. In this report, it is shown that on acetate as a carbon source, PB11 displayed a specific growth rate (μ) higher than PB12 (0.21 and 0.13 h^–1, respectively) while JM101 had a μ of 0.28 h^–1. To understand these growth differences on acetate, we compared the expression profiles of central metabolic genes by RT-PCR analysis. Obtained data revealed that some gluconeogenic genes were downregulated in both PTS^– strains as compared to JM101, while most glycolytic genes were upregulated in PB12 in contrast to PB11 and JM101. Furthermore, inactivation of gluconeogenic genes, like ppsA, sfcA, and maeB,and poxB gene that codes for pyruvate oxidase, has differential impacts in the acetate metabolism of these strains. Results indicate that growth differences on acetate in the PTS^– derivatives are due to potential carbon recycling strategies, mainly in PB11, and futile carbon cycles, especially in PB12.