A panoramic view of the genomic landscape of the genus Streptomyces

Nikolaidis, Marios; Hesketh, Andrew; Frangou, Nikoletta; Mossialos, Dimitris; Van de Peer, Yves; Oliver, Stephen G.; Amoutzias, Grigorios D.

doi:10.1099/mgen.0.001028

Cited by 11 publications

(11 citation statements)

References 94 publications

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“…The number of BGCs per genome in our dataset ranged from 19 to 65 (median=42.5) (Fig. 3a), which is consistent with those reported in other studies of Streptomyces [45][46][47]. We detected a significant positive correlation albeit small between the genome size and the number of BGCs per genome (R 2 =0.13, p-value=3.1e-05; Fig.…”

Section: Bgcs Are Diverse and Abundant But Not Structured By The Sour...supporting

confidence: 91%

Ecology shapes the genomic and biosynthetic diversification of Streptomyces bacteria from insectivorous bats

Montoya-Giraldo,

Piper,

Ikhimiukor

et al. 2024

Microbial Genomics

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Streptomyces are prolific producers of secondary metabolites from which many clinically useful compounds have been derived. They inhabit diverse habitats but have rarely been reported in vertebrates. Here, we aim to determine to what extent the ecological source (bat host species and cave sites) influence the genomic and biosynthetic diversity of Streptomyces bacteria. We analysed draft genomes of 132 Streptomyces isolates sampled from 11 species of insectivorous bats from six cave sites in Arizona and New Mexico, USA. We delineated 55 species based on the genome-wide average nucleotide identity and core genome phylogenetic tree. Streptomyces isolates that colonize the same bat species or inhabit the same site exhibit greater overall genomic similarity than they do with Streptomyces from other bat species or sites. However, when considering biosynthetic gene clusters (BGCs) alone, BGC distribution is not structured by the ecological or geographical source of the Streptomyces that carry them. Each genome carried between 19–65 BGCs (median=42.5) and varied even among members of the same Streptomyces species. Nine major classes of BGCs were detected in ten of the 11 bat species and in all sites: terpene, non-ribosomal peptide synthetase, polyketide synthase, siderophore, RiPP-like, butyrolactone, lanthipeptide, ectoine, melanin. Finally, Streptomyces genomes carry multiple hybrid BGCs consisting of signature domains from two to seven distinct BGC classes. Taken together, our results bring critical insights to understanding Streptomyces-bat ecology and BGC diversity that may contribute to bat health and in augmenting current efforts in natural product discovery, especially from underexplored or overlooked environments.

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Section: Bgcs Are Diverse and Abundant But Not Structured By The Sour...supporting

confidence: 91%

Ecology shapes the genomic and biosynthetic diversification of Streptomyces bacteria from insectivorous bats

Montoya-Giraldo,

Piper,

Ikhimiukor

et al. 2024

Microbial Genomics

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“…Each genome presents 16 to 53 BGCs, consistent with the most recent metagenomic studies [38]. The strains situated at the extremities of the range are Streptomyces sp.…”

Section: Distribution and Diversity Of Biosynthetic Gene Clusters (Bg...supporting

confidence: 84%

“…HSG2 to 12.47 Mb for Streptomyces rapamycinicus NRRL 5491, with an average of 8.3 Mb. Despite the increased genomic information available for this genus, the genome size values are very close to those reported in previous metagenomic analyses[21,38,39].…”

supporting

confidence: 80%

Genome mining ofStreptomycesfor the discovery of low-resistance antibiotics

Antony,

Alejandro,

Norberto

et al. 2023

Preprint

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2.AbstractAntimicrobial 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.Graphical abstract3.Impact statementExploring 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 summaryBig 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 inStreptomycesgenomes; (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 (https://drive.google.com/file/d/1VGWJo9rsF429IUFf2g-tqs_NfBicNX91/view?usp=drive_link).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:https://github.com/Bert-IQ/ripps-classification).

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“…While the importance of optimized growth medium for metabolite production and life cycle progression is widely recognized, few have examined nutrient poor conditions. The abundance of gene regulatory proteins, two-component systems, transporters, and substrate-binding proteins identified in the S. coelicolor genome suggests a significant capacity for adaptation and the utilization of varied nutrient sources (Bentley et al, 2002; Nikolaidis et al, 2023). A recent study on rich media showed that S. coelicolor metabolite production was enhanced because of genomic heterogeneity (Z.…”

Section: Introductionmentioning

confidence: 99%

Nutrient-deprived growth of streptomycetes reveals a novel growth phenotype with enhanced antimicrobial activity

Söderholm,

Tanner,

Sandblad

2024

Preprint

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The capacity of gram-positive streptomycetes to adapt to various environments is reflected in the sheer size of their genomes and the number of encoded regulatory proteins. However, typical studies of Streptomycetes are conducted using media rich in available nutrients. Here, we demonstrate thatS. coelicolorcolonies respond to nutrient depletion with a phenotype transition. We refer to this phenotype as “foraging growth” for its submerged and continuous growth on depleted media. The foraging phenotype differ distinctly from colonies on nutrient-rich media, in terms of colony morphology, genomic stability and their secretome. The results demonstrate that adaption to nutrient deprivation through foraging is found throughout the phylogeny, indicating it is highly conserved. Furthermore, foragingS. coelicolorgains the ability to inhibit mold on deprived media and exhibits an altered metabolomic profile. An enhanced competitive activity against gram-negative and gram-positive bacteria was also observed among the tested species. These findings highlight the morphological adaptions of streptomycetes during nutrient deprived growth and demonstrate altered secondary metabolite production and that novel antimicrobial activities can be detected.

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A panoramic view of the genomic landscape of the genus Streptomyces

Cited by 11 publications

References 94 publications

Ecology shapes the genomic and biosynthetic diversification of Streptomyces bacteria from insectivorous bats

Ecology shapes the genomic and biosynthetic diversification of Streptomyces bacteria from insectivorous bats

Genome mining ofStreptomycesfor the discovery of low-resistance antibiotics

Nutrient-deprived growth of streptomycetes reveals a novel growth phenotype with enhanced antimicrobial activity

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