Glycopeptide antibiotics are produced by Actinobacteria through biosynthetic gene clusters that include genes supporting their regulation, synthesis, export and resistance. The chemical and biosynthetic diversities of glycopeptides are the product of an intricate evolutionary history. Extracting this history from genome sequences is difficult as conservation of the individual components of these gene clusters is variable and each component can have a different trajectory. We show that glycopeptide biosynthesis and resistance in Actinobacteria maps to approximately 150–400 million years ago. Phylogenetic reconciliation reveals that the precursors of glycopeptide biosynthesis are far older than other components, implying that these clusters arose from a pre-existing pool of genes. We find that resistance appeared contemporaneously with biosynthetic genes, raising the possibility that the mechanism of action of glycopeptides was a driver of diversification in these gene clusters. Our results put antibiotic biosynthesis and resistance into an evolutionary context and can guide the future discovery of compounds possessing new mechanisms of action, which are especially needed as the usefulness of the antibiotics available at present is imperilled by human activity.
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All genome sequences produced for this study (22 organisms) have been deposited to GenBank under the BioProject accession number PRJNA472056. The source of every BGC is listed in Supplementary Table 1. The dates for all nodes in all of the dated trees are provided in Supplementary Table 2. The input BGC sequences, 16S rRNA sequences, 16S rRNA alignment, 16S rRNA tree, concatenated TIGRFAM core genome sequence alignment, all dated BEAST species trees, extracted gene/domain family sequences, annotated gene/domain families, gene/domain family alignments, gene/domain family trees and all reconciliations (scheme A00, A10, B00, B10, C01 and C03) are available at http://github.com/waglecn/GPA_evolution.
Reconciliations were visualized by overlaying the reconciled nodes of each gene tree to the species tree using a custom Python script, which is available at http://github.com/waglecn/GPA_evolution.
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C. Groves provided valuable input on the figures. This research was funded by the Canadian Institutes of Health Research (grant no. MT-14981) and by a Canada Research Chair (to G.D.W.). N.W. was supported by a Canadian Institutes of Health Research graduate scholarship. A.G.M. holds a Cisco Research Chair in Bioinformatics, supported by Cisco Systems Canada, Inc. Some computer resources were provided by the McMaster Service Lab and Repository computing cluster, funded in part by grants from the Canadian Foundation for Innovation (grant no. 34531 to A.G.M.).
The authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Figs. 1–7, Supplementary Table 3, Supplementary Table 4 and Supplementary References.
Description of organisms, clusters, sequences used in this study.
Time tree node details.
Node reconciliation details.