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Compendium of specialized metabolite biosynthetic diversity encoded in bacterial genomes

An Author Correction to this article was published on 06 June 2022

This article has been updated


Bacterial specialized metabolites are a proven source of antibiotics and cancer therapies, but whether we have sampled all the secondary metabolite chemical diversity of cultivated bacteria is not known. We analysed ~170,000 bacterial genomes and ~47,000 metagenome assembled genomes (MAGs) using a modified BiG-SLiCE and the new clust-o-matic algorithm. We estimate that only 3% of the natural products potentially encoded in bacterial genomes have been experimentally characterized. We show that the variation in secondary metabolite biosynthetic diversity drops significantly at the genus level, identifying it as an appropriate taxonomic rank for comparison. Equal comparison of genera based on relative evolutionary distance revealed that Streptomyces bacteria encode the largest biosynthetic diversity by far, with Amycolatopsis, Kutzneria and Micromonospora also encoding substantial diversity. Finally, we find that several less-well-studied taxa, such as Weeksellaceae (Bacteroidota), Myxococcaceae (Myxococcota), Pleurocapsa and Nostocaceae (Cyanobacteria), have potential to produce highly diverse sets of secondary metabolites that warrant further investigation.

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Fig. 1: Biosynthetic diversity of the sequenced bacterial kingdom.
Fig. 2: Comparison of biosynthetic diversity among phyla.
Fig. 3: Relations of taxonomic levels to variability in biosynthetic diversity.
Fig. 4: Overview of actual and potential biosynthetic diversity of the bacterial kingdom, compared at the REDgroup level.
Fig. 5: Unique diversity in the known producer Streptomyces and promising potential of less popular taxa.

Data availability

The datasets generated and analysed during the study are available in the Zenodo repository: Source data are provided with this paper.

Code availability

The clust-o-matic code is available at

The modified BiG-SLiCE script (that accepts as input a regular BiG-SLiCE output folder, then outputs the GCF membership in a tsv file) is available both in our Zenodo repository (file name: and at the following link:

Change history


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A.G. is grateful for the support of the Deutsche Forschungsgemeinschaft (DFG; Project ID No. 398967434-TRR 261). N. Ziemert was supported by the German Center for Infection Research (DZIF) (TTU 09.716). M.H.M. was supported by a European Research Council Starting Grant 948770-DECIPHER. S.K. was supported by the Graduate School for Experimental Plant Sciences (EPS) of Wageningen University. Work in the lab of R.M. was supported by BMBF (16GW0243), DFG and DZIF (807-5-8-0982600). A.G. and N. Ziemert thank the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany´s Excellence Strategy – EXC 2124 – 390838134 for the infrastructural support. A.G. thanks M. Direnc Mungan for valued discussions on optimizing the analysis, as well as C. Bagci for his imaginative suggestion on dealing with large data. We also thank L. do Presti for invaluable comments on the manuscript.

Author information

Authors and Affiliations



A.G., S.A.K., N. Zaburannyi and D.K. performed the analysis. S.A.K. and N. Zaburannyi contributed analysis tools. A.G., D.K., R.M., M.H.M. and N. Ziemert wrote the paper. All authors contributed to the conception and design of the analysis, read and agreed to the published version of the manuscript.

Corresponding authors

Correspondence to Marnix H. Medema or Nadine Ziemert.

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Competing interests

M.H.M. is a co-founder of Design Pharmaceuticals and a member of the scientific advisory board of Hexagon Bio. The other authors declare no competing interests.

Peer review

Peer review information

Nature Microbiology thanks Nigel Mouncey and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Illustrating the correlation between BGC clustering thresholds and the grouping of their pathway products.

a) a snippet of a complete-linkage hierarchical dendrogram constructed by doing a pairwise distance comparison of L2-normalized BGC features within the MIBiG dataset, highlighting the grouping of BGCs for the enediynes Uncialamycin (UCM) and Tiancimycin (TNM) under the threshold T = 0.5, and further grouping with another related enediyne BGC, Dynemicin (DNM) under the looser threshold of T = 0.7. b) Comparative genes analysis generated using the clinker tool92 v0.0.23 shows how UCM and TNM BGCs are much more similar to each other than to DNM (same-colored genes indicate <70% amino acid similarity, while colored edges indicate <50% amino acid similarity), which is consistent with the structural diversity of their compounds (pictured).

Source data

Extended Data Fig. 2 Intersections and distribution of biosynthetic diversity values among different ecosystem types.

The bar plot on the left depicts the number of Gene Cluster Families (GCFs as defined by BiG-SLiCE with T = 0.4) found in each biome type. The bar plot on top shows the size (number of GCFs) of each intersection. Which sets (biome types) are included in each intersection can be seen in the matrix below the bar plot, where the dark dots pinpoint included sets. If more than one set is part of an intersection, connecting lines are drawn for better visibility. The data presented in this graph come only from the MAGs in the GEMS dataset (see Supplementary Table 1), which was the only one with sufficient metadata. Only the top 63 most sizable intersections are depicted here, and only the 35 ecosystem types (with the most GCFs out of the 63) that were part of them are shown on the left. The data indicate that there is barely any overlap between the ecosystem types; most GCFs (74.43 %) are specific to a single biome (a complete overview of unique GCFs per ecosystem type can be found in Supplementary Table 7), while the largest intersection (the one including most habitats - not visible in this Figure) includes 50 of the 63 ecosystem types.

Source data

Extended Data Fig. 3 Overview of actual and potential biosynthetic diversity of bacterial kingdom, compared at REDgroup level.

Extended Data Fig. 3 is interactive and can be accessed online on iTOL: GTDB bacterial tree up to REDgroup level (for more details see Methods - REDgroup definition), colour-coded by phylum, decorated with barplots of actual (orange) and potential (purple) Gene Cluster Families (GCFs) as defined by BiG-SLiCE (T = 0.4). Potential GCFs were computed by rarefaction analyses (for more details see Results - Well known and less popular taxa as sources of biosynthetic diversity). REDgroups names are displayed around the tree as leaf node labels; hovering over them provides further taxonomic information (for full REDgroup metadata see Supplementary Table 1). Phyla known to be enriched in NP producers are immediately visible (Actinobacteriota, Protobacteriota), with the most promising groups coming from the Actinobacteriota phylum (the highest peak belongs to a REDgroup containing Streptomyces strains). Simultaneously, within the underexplored phyla, there seems to be notable biosynthetic diversity and potential. This Figure is meant to be explored by zooming in and out, searching for keywords and visualizing different kinds of information by switching between Tree Views. Any other attempt at modification (for example turning datasets on and off) may result in an unreadable graph.

Source data

Extended Data Fig. 4 Unique diversity in the known producer Streptomyces.

Unique GCFs, as defined by BiG-SLICE (T = 0.4), of bacterial phyla and Streptomyces (solid shapes) and pairwise overlaps of phyla - phyla and phyla - Streptomyces (ribbons). Each taxon has a distinct colour. The genus Streptomyces (1) appears to have a very high amount of unique GCFs comparable to entire phyla, such as Proteobacteria (43).

Source data

Supplementary information

Supplementary Information

Supplementary Methods and Figs. 1–7.

Reporting Summary

Peer Review File

Supplementary Tables

Supplementary Tables 1–5 are available in the project’s zenodo repository ( Table 6. BGC IDs, MiBIG IDs, producer GTDB-based taxonomic information and GCF assignment (for BiG-SLiCE T = 0.4) for all MiBIG BGCs included in the creation of Fig. 1c. Table 7. Biogeography analysis of the Nayfach MAGs dataset. Number of genomes, BGCs, GCFs and unique GCFs per ecosystem type (as defined in the corresponding paper). Table 8. REDgroup full metadata: node IDs (can be used in the exploration of the tree in Extended Data 3), labels, number of members, number of BGCs, number of GCFs and potential GCFs (pGCFs) as defined by BiG-SLiCE (T = 0.4) and clust-o-matic (T = 0.5), GTDB taxonomic information and number of products in the NPASS database whose producer is a member of the REDgroup (NPASS_hits). Table 9. Comparison of random sampling analysis results to original results, including node IDs, labels, number of members, number of BGCs, number of GCFs and potential GCFs (pGCFs) as defined by BiG-SLiCE (T = 0.4), the original ranking based on the pGCFs, the average pGCFs from all random sampling iterations, the ranking based on the random sampling and GTDB taxonomic information.

Source data

Source Data Fig. 1

Source Data for Fig. 1.

Source Data Fig. 2

Source Data for Fig. 2 except for the tree in a, which is provided in the Zenodo repository.

Source Data Fig. 3

Source Data for Fig. 3.

Source Data Fig. 4

Source Data for Fig. 4 except for the tree in a and the source data of b, which are provided in the Zenodo repository.

Source Data Fig. 5

Source Data for Fig. 5.

Source Data Extended Data Fig. 1

Source Data for Extended Data Fig. 1. Accompanying source data are provided in the Zenodo repository.

Source Data Extended Data Fig. 2

Source Data for Extended Data Fig. 2.

Source Data Extended Data Fig. 3

Source Data for Extended Data Fig. 3 except for the tree, which is provided in the Zenodo repository.

Source Data Extended Data Fig. 4

Source Data for Extended Data Fig. 4.

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Gavriilidou, A., Kautsar, S.A., Zaburannyi, N. et al. Compendium of specialized metabolite biosynthetic diversity encoded in bacterial genomes. Nat Microbiol 7, 726–735 (2022).

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