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CRISPR base editing is a powerful method to engineer bacterial genomes. However, it restricts editing to single-nucleotide substitutions. Here, to address this challenge, we adapt a CRISPR-Prime Editing-based, DSB-free, versatile, and single-nucleotide resolution genetic manipulation toolkit for prokaryotes. It can introduce substitutions, deletions, insertions, and the combination thereof, both in plasmids and the chromosome of E. coli with high fidelity. Notably, under optimal conditions, the efficiency of 1-bp deletions reach up to 40%. Moreover, deletions of up to 97 bp and insertions up to 33 bp were successful with the toolkit in E. coli , however, efficiencies dropped sharply with increased fragment sizes. With a second guide RNA, our toolkit can achieve multiplexed editing albeit with low efficiency. Here we report not only a useful addition to the genome engineering arsenal for E. coli , but also a potential basis for the development of similar toolkits for other bacteria. CRISPR prime editing enables double-strand break free engineering of the genome. Here the authors present a toolkit for prime editing in E. coli.

It has been hypothesized that some antibiotic resistance genes (ARGs) found in pathogenic bacteria derive from antibiotic-producing actinobacteria. Here we provide bioinformatic and experimental evidence supporting this hypothesis. We identify genes in proteobacteria, including some pathogens, that appear to be closely related to actinobacterial ARGs known to confer resistance against clinically important antibiotics. Furthermore, we identify two potential examples of recent horizontal transfer of actinobacterial ARGs to proteobacterial pathogens. Based on this bioinformatic evidence, we propose and experimentally test a ‘carry-back’ mechanism for the transfer, involving conjugative transfer of a carrier sequence from proteobacteria to actinobacteria, recombination of the carrier sequence with the actinobacterial ARG, followed by natural transformation of proteobacteria with the carrier-sandwiched ARG. Our results support the existence of ancient and, possibly, recent transfers of ARGs from antibiotic-producing actinobacteria to proteobacteria, and provide evidence for a defined mechanism. Some antibiotic resistance genes found in pathogenic bacteria might derive from antibiotic-producing actinobacteria. Here, Jiang et al . provide bioinformatic and experimental evidence supporting this hypothesis, and propose a specific mechanism for the transfer of these genes between bacterial phyla.

Soil microbiota can confer fitness advantages to plants and increase crop resilience to drought and other abiotic stressors. However, there is little evidence on the mechanisms correlating a microbial trait with plant abiotic stress tolerance. Here, we report that Streptomyces effectively alleviate drought and salinity stress by producing spiroketal polyketide pteridic acid H ( 1 ) and its isomer F ( 2 ), both of which promote root growth in Arabidopsis at a concentration of 1.3 nM under abiotic stress. Transcriptomics profiles show increased expression of multiple stress responsive genes in Arabidopsis seedlings after pteridic acids treatment. We confirm in vivo a bifunctional biosynthetic gene cluster for pteridic acids and antimicrobial elaiophylin production. We propose it is mainly disseminated by vertical transmission and is geographically distributed in various environments. This discovery reveals a perspective for understanding plant- Streptomyces interactions and provides a promising approach for utilising beneficial Streptomyces and their secondary metabolites in agriculture to mitigate the detrimental effects of climate change. Soil microbiota can increase crop resilience to abiotic stressors. Here the authors show that Streptomyces produce bioactive spiroketal polyketides to enhance plant growth under drought and salt stress.

An ever-increasing demand for novel antimicrobials to treat life-threatening infections caused by the global spread of multidrug-resistant bacterial pathogens stands in stark contrast to the current level of investment in their development, particularly in the fields of natural-product-derived and synthetic small molecules. New agents displaying innovative chemistry and modes of action are desperately needed worldwide to tackle the public health menace posed by antimicrobial resistance. Here, our consortium presents a strategic blueprint to substantially improve our ability to discover and develop new antibiotics. We propose both short-term and long-term solutions to overcome the most urgent limitations in the various sectors of research and funding, aiming to bridge the gap between academic, industrial and political stakeholders, and to unite interdisciplinary expertise in order to efficiently fuel the translational pipeline for the benefit of future generations. Antimicrobial resistance is an increasing threat to public health and encouraging the development of new antimicrobials is one of the most important ways to address the problem. This Roadmap article aims to bring together industrial, academic and political partners, and proposes both short-term and long-term solutions to this challenge.

Streptomycetes are prominent sources of bioactive natural products, but metabolic engineering of the natural products of these organisms is greatly hindered by relatively inefficient genetic manipulation approaches. New advances in genome editing techniques, particularly CRISPR-based tools, have revolutionized genetic manipulation of many organisms, including actinomycetes. We have developed a comprehensive CRISPR toolkit that includes several variations of ‘classic’ CRISPR–Cas9 systems, along with CRISPRi and CRISPR-base editing systems (CRISPR-BEST) for streptomycetes. Here, we provide step-by-step protocols for designing and constructing the CRISPR plasmids, transferring these plasmids to the target streptomycetes, and identifying correctly edited clones. Our CRISPR toolkit can be used to generate random-sized deletion libraries, introduce small indels, generate in-frame deletions of specific target genes, reversibly suppress gene transcription, and substitute single base pairs in streptomycete genomes. Furthermore, the toolkit includes a Csy4-based multiplexing option to introduce multiple edits in a single experiment. The toolkit can be easily extended to other actinomycetes. With our protocol, it takes <10 d to inactivate a target gene, which is much faster than alternative protocols. This protocol offers a CRISPR-based toolkit, including several variants of ‘classic’ CRISPR–Cas9, along with CRISPRi and CRISPR-base editing systems (CRISPR-BEST) for genome editing in streptomycetes.

Background Streptomyces is a highly diverse genus known for the production of secondary or specialized metabolites with a wide range of applications in the medical and agricultural industries. Several thousand complete or nearly complete Streptomyces genome sequences are now available, affording the opportunity to deeply investigate the biosynthetic potential within these organisms and to advance natural product discovery initiatives. Results We perform pangenome analysis on 2371 Streptomyces genomes, including approximately 1200 complete assemblies. Employing a data-driven approach based on genome similarities, the Streptomyces genus was classified into 7 primary and 42 secondary Mash-clusters, forming the basis for comprehensive pangenome mining. A refined workflow for grouping biosynthetic gene clusters (BGCs) redefines their diversity across different Mash-clusters. This workflow also reassigns 2729 known BGC families to only 440 families, a reduction caused by inaccuracies in BGC boundary detections. When the genomic location of BGCs is included in the analysis, a conserved genomic structure, or synteny, among BGCs becomes apparent within species and Mash-clusters. This synteny suggests that vertical inheritance is a major factor in the diversification of BGCs. Conclusions Our analysis of a genomic dataset at a scale of thousands of genomes refines predictions of BGC diversity using Mash-clusters as a basis for pangenome analysis. The observed conservation in the order of BGCs’ genomic locations shows that the BGCs are vertically inherited. The presented workflow and the in-depth analysis pave the way for large-scale pangenome investigations and enhance our understanding of the biosynthetic potential of the Streptomyces genus.

Lanthipeptides are a class of ribosomally synthesised and post-translationally modified peptide (RiPP) natural products from the bacterial secondary metabolism. Their name is derived from the characteristic lanthionine or methyl-lanthionine residues contained in the processed peptide. Lanthipeptides that possess an antibacterial activity are called lantibiotics. Whereas multiple tools exist to identify lanthipeptide gene clusters from genomic data, no programs are available to predict the post-translational modifications of lanthipeptides, such as the proteolytic cleavage of the leader peptide part or tailoring modifications based on the analysis of the gene cluster sequence. antiSMASH is a software pipeline for the identification of secondary metabolite biosynthetic clusters from genomic input and the prediction of products produced by the identified clusters. Here we present a novel antiSMASH module using a rule-based approach to combine signature motifs for biosynthetic enzymes and lanthipeptide-specific cleavage site motifs to identify lanthipeptide clusters in genomic data, assign the specific lanthipeptide class, predict prepeptide cleavage, tailoring reactions, and the processed molecular weight of the mature peptide products.

Background Escherichia coli Nissle 1917 (EcN) is a probiotic bacterium used to treat various gastrointestinal diseases. EcN is increasingly being used as a chassis for the engineering of advanced microbiome therapeutics. To aid in future engineering efforts, our aim was to construct an updated metabolic model of EcN with extended secondary metabolite representation. Results An updated high-quality genome-scale metabolic model of EcN, iHM1533, was developed based on comparison with 55 E. coli/Shigella reference GEMs and manual curation, including expanded secondary metabolite pathways (enterobactin, salmochelins, aerobactin, yersiniabactin, and colibactin). The model was validated and improved using phenotype microarray data, resulting in an 82.3% accuracy in predicting growth phenotypes on various nutrition sources. Flux variability analysis with previously published 13 C fluxomics data validated prediction of the internal central carbon fluxes. A standardised test suite called Memote assessed the quality of iHM1533 to have an overall score of 89%. The model was applied by using constraint-based flux analysis to predict targets for optimisation of secondary metabolite production. Modelling predicted design targets from across amino acid metabolism, carbon metabolism, and other subsystems that are common or unique for influencing the production of various secondary metabolites. Conclusion iHM1533 represents a well-annotated metabolic model of EcN with extended secondary metabolite representation. Phenotype characterisation and the iHM1533 model provide a better understanding of the metabolic capabilities of EcN and will help future metabolic engineering efforts.

Polyketides such as the antibiotic erythromycin or the immunosuppressant rapamycin, and non-ribosomal peptides, such as the antibiotics penicillin or vancomycin, are important classes of natural products. The core of these molecules are biosynthesized by large polyketide synthases (PKS) and non-ribosomal peptide synthetases (NRPS), respectively. The modular architecture of these enzymatic assembly lines makes them interesting candidates for synthetic biology approaches. The re-engineering efforts aim to understand the molecular structure, produce new compounds, produce analogs of known compounds, tag the products or improve activity and/or yield. Here, we first consider the definition of PKS and NRPS modules, then give an overview of different strategies for re-engineering and finally review recent examples of PKS and NRPS reengineering.