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These materials were used as a part of the NPLinker eScience Workshop on Natural Product Genome & Metabolome Mining: https://www.wur.nl/en/research-results/chair-groups/plant-sciences/bioinformatics/teaching/nplinker_workshop.htm Most materials are available in a format facilitating its reuse. When doing so, we kindly ask you to - where possible and appropriate - acknowledge and credit those that put effort in creating the slides and developing the methods. You will find information in the slide decks to do so. Please note that the materials also have a DOI through this Zenodo link that can be used to credit the workshop material. Thanks in advance on behalf of the instructors and organizers of the workshop. Whilst natural products discovery was a prime application area for the workshop, we do note that both genome mining and especially metabolome mining do have other application areas for which - sometimes with some modifications - the materials could be reused. We hope that this initiative will help to further foster teaching the future generation of omics miners, to aid in setting up teaching materials for genome mining, metabolomics, and metabolome mining, and to promote discussions in between and bring together the fields of genome mining and metabolome mining., This workshop was funded by a Netherlands eScienceCenter Open eScience Call: NLESC.OEC.2021.002

Additional file 1. UmetaFlow: An untargeted metabolomics workflow for high-throughput data processing and analysis. Figure S1. A detailed overview of UmetaFlow. Table S1. Important instrument, method, and sample-specific parameters for UmetaFlow parameter optimization. Table S2. The optimal parameters for OpenMS (UmetaFlow) for feature detection, formula, and structural predictions of the in-house datasets. Table S3. Feature detection, structural and formula predictions for pyracrimycin A in Streptomyces sp. NBC 00162, Streptomyces sp. CA-210063 and Streptomyces eridani. Table S4. The optimal parameters for OpenMS (UmetaFlow) for feature detection, quantification, and marker selection of the MTBLS733 QE HF dataset. Table S5. Feature identification, quantification, and marker selection performance of different untargeted metabolomic data processing software using the benchmark dataset MTBLS733. Table S6. The optimal parameters for OpenMS (UmetaFlow) for feature detection, quantification, and marker selection of the MTBLS736 tripleTOF dataset. Table S7. Feature identification, quantification, and marker selection performance of different untargeted metabolomic data processing software using the benchmark dataset MTBLS736. Table S8. The optimal parameters for OpenMS (UmetaFlow) for feature detection and quantification of the MTBLS1129 and MTBLS1130 dataset. Figure S1. Plotted average metabolite intensities in normal and colon cancer tissue samples, detected and quantified with (a) XCMS and (b) UmetaFlow (dataset MTBLS1129).

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.

By culturing microorganisms under standard laboratory conditions, most biosynthetic gene clusters (BGCs) are not expressed, and thus, the products are not produced. To explore this biosynthetic potential, we developed a novel “semi-targeted” approach focusing on activating “silent” BGCs by concurrently introducing a group of regulator genes into streptomycetes of the Tübingen strain collection. We constructed integrative plasmids containing two classes of regulatory genes under the control of the constitutive promoter ermE*p (cluster situated regulators (CSR) and Streptomyces antibiotic regulatory proteins (SARPs)). These plasmids were introduced into Streptomyces sp. TÜ17, Streptomyces sp. TÜ10 and Streptomyces sp. TÜ102. Introduction of the CSRs-plasmid into strain S. sp. TÜ17 activated the production of mayamycin A. By using the individual regulator genes, we proved that Aur1P, was responsible for the activation. In strain S. sp. TÜ102, the introduction of the SARP-plasmid triggered the production of a chartreusin-like compound. Insertion of the CSRs-plasmid into strain S. sp. TÜ10 resulted in activating the warkmycin-BGC. In both recombinants, activation of the BGCs was only possible through the simultaneous expression of aur1PR3 and griR in S. sp. TÜ102 and aur1P and pntR in of S. sp. TÜ10.

International audience

Additional file 2. CAiSMD Feedback form.

Additional file 1. Workshop statistics.

Tilmann Weber’s group at the Novo Nordisk Foundation Center for Biosustainability developed a user-friendly, web server implementation of the sgRNA prediction software, CRISPy, for non-computer scientists.

The present invention relates to CRISPR/Cas-based methods for generating random-sized deletions around at least one target nucleic acid sequence, or for generating precise indels around at least one target nucleic acid sequence, or for modulating transcription of at least one target nucleic acid sequence. Also disclosed is a clonal library comprising clones with random-sized deletions, as well as polynucleotides, polypeptides, cells and kits useful for performing the present methods. The present methods can be performed in organisms where gene editing is typically considered as difficult, such as actinomycetes, in particular streptomycetes.

We report the draft genome sequence of Microbispora sp. strain ATCC-PTA-5024, a soil isolate that produces NAI-107, a new lantibiotic with the potential to treat life-threatening infections caused by multidrug-resistant Gram-positive pathogens. The draft genome of strain Microbispora sp. ATCC-PTA-5024 consists of 8,543,819 bp, with a 71.2% G+C content and 7,860 proteincoding genes.

Talaromyces islandicus (“Penicillium islandicum”) is a widespread foodborne mold that produces numerous secondary metabolites, among them potent mycotoxins belonging to different chemical classes. A notable metabolite is the hepatotoxic and carcinogenic pentapeptide cyclochlorotine that contains the unusual amino acids β-phenylalanine, 2-aminobutyric acid and 3,4-dichloroproline. Although the chemical structure has been known for over five decades, nothing is known about the biosynthetic pathway of cyclochlorotine. Bioinformatic analysis of the recently sequenced genome of T. islandicus identified a wealth of gene clusters potentially coding for the synthesis of secondary metabolites. Here we show by RNA interference-mediated gene silencing that a nonribosomal peptide synthetase, CctN, is responsible for the synthesis of cyclochlorotine. Moreover, we identified novel cyclochlorotine chemical variants, whose production also depended on cctN expression. Surprisingly, the halogenase required for cyclochlorotine biosynthesis is not encoded in the cct cluster. Nonetheless, our findings enabled us to propose a detailed model for cyclochlorotine biosynthesis. In addition, comparative genomics revealed cct-like clusters are present in all of the sequenced Talaromyces strains indicating a high prevalence of cyclochlorotine production ability.

A wide variety of enzymatic pathways that produce specialized metabolites in bacteria, fungi and plants are known to be encoded in biosynthetic gene clusters. Information about these clusters, pathways and metabolites is currently dispersed throughout the literature, making it difficult to exploit. To facilitate consistent and systematic deposition and retrieval of data on biosynthetic gene clusters, we propose the Minimum Information about a Biosynthetic Gene cluster (MIBiG) data standard.

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.

Supplementary Results section. (PDF 123 kb)

Figure S1-S6 with corresponding figure legends. (PDF 511 kb)

Supplementary Results section. (PDF 123 kb)

Figure S1-S6 with corresponding figure legends. (PDF 511 kb)