Genome mining of Streptomyces for the discovery of low-resistance antibiotics

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Antimicrobial resistance is considered one of the top ten global health crises, which requires discovering and developing antibiotics with low resistance. Historically, Streptomyces bacteria are well-known for adapting to their complex environment by accumulating numerous clusters of specialized metabolites, some of which remain inactive in laboratory settings. The genomic revolution significantly increased their potential, complementing laboratory practices, and allowing for the discovery of antimicrobials associated with this biosynthetic machinery that are not constitutively produced. In the current study, we used the latest bioinformatics analyses to improve upon these predictions for Streptomyces genomes, and to identify low-resistance novel antimicrobial candidates. Our integrated pipeline used antiSMASH, BiG- SCAPE/EFI-EST, BiG-FAM, and additional tools and identified 326 novel BGCs and 67 peptides with antimicrobial potential. Further exploration of ribosomally synthesized and post-translationally modified peptides (RiPPs) revealed diverse chemical structures and suggested new mechanisms of action. Artificial intelligence platforms, such as MACREL, predicted the antimicrobial activity of the identified peptides, offering a comprehensive strategy for discovering bioactive compounds. Their low-resistance potential was estimated on a case-by-case basis. This study showcases the extensive genomic potential of Streptomyces, providing valuable insights for future antibiotic discovery efforts.


Impact statement

Exploring the potential applications of microorganisms is essential in fields like medicine, ecology, and biotechnology. Genomics is an invaluable tool that can uncover secondary metabolites with various applications in these fields. To this end, we conducted a detailed analysis of 388 complete genomes of Streptomyces, with a focus on identifying new biosynthetic gene clusters (BGCs) and antimicrobial peptides. Our analysis involved the use of various tools, including AntiSMASH, BiG-SCAPE, and reference databases like MiBIG and BiG-FAM, to unravel the diversity of these biosynthetic systems. We established a manually curated database and a robust pipeline for identifying valuable compounds, which allowed us to prioritize 326 BGCs with novel and diverse biosynthetic machinery. These unique insights into the genomic richness of Streptomyces will serve as a valuable guide for future antibiotic discovery efforts, facilitating the selection of strains that can produce new natural products. Moreover, our approach also helped us identify 67 potential antimicrobial peptides, including some belonging to the newly discovered Class V lanthipeptides, highlighting the diversity and promise of these compounds. Our work offers a glimpse into the potential of genomics in identifying important molecules and provides a framework for future studies in this field.

Data summary

Big tables and special files can be found in Zenodo, under the following link In there, the following objects are included:

  • Supplementary Table 1: a spreadsheet with nine tables: (Table S1) Accession numbers and genomic characteristics of the genomes used in this study; (Table S2) summary of predicted Biosynthetic Gene Clusters (BGCs) for each genome; (Table S3) summary of the distribution of BGC types present in Streptomyces genomes; (Table S4) IDs and strains of MiBIG BGCs; (Table S5) All information regarding the database of peptides with antimicrobial activity; (Table S6) Data of BGCs recovered after the analysis of MiBIG and BiG-FAM; (Table S7) Complete and potentially novel BGC data used for the creation of the SSN with EFI-EST; (Table S8) Data predicting potential antimicrobial activity using MACREL v1.2.0; (Table S9) Data of predicted peptides with positive antimicrobial activity. These data were used to create the graph showing the distribution of RiPPs classes within these peptides.

  • Supplementary Data 1: a folder containing all .gbk files for the BGCs predicted by antiSMASH and MiBIG. These files were used as input for BiG-SCAPE ( ).

  • Supplementary Data 2: amino acid sequences of the mined and reference peptides (from the database built into this work) in .fasta format.

  • Supplementary Table 2: a spreadsheet with five tables: Data from the evaluation of the prediction of antimicrobial activity with five different AIs through five comparisons of the created database (Table S1-Macrel); (Table S2-CAMPR3); (Table S3-AMP Scanner); (Table S4-AmPEP); (Table S5-AL4AMP).

  • Supplementary Figure 1: results of the predictive performance analysis (script: ).

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