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Currently most fermentation products are produced from first-generation biomass by well characterized and genetically modifiable model strains, often referred to as cell-factories. To efficiently valorise second-generation biomass using fermentation, strains are needed that can grow on the diversity of sugars and tolerate the inhibitory compounds present in this substrate, traits that are often challenging to engineer in model organisms. In this project we have isolated bacteria from soil samples that are tolerant to hemicellulosic biomass hydrolysate from beech wood. This has yielded several novel environmental strains that are able to grow on and tolerate this generally toxic substrate. The most interesting isolates are a novel species of Pseudomonas and a novel strain of the Pantoea genus. Both strains show relatively high tolerance to the inhibitors present and can grow on a wide range of different substrates. The genome sequences of these strains have been determined using nanopore sequencing and basic physiological features have been characterized. The Pseudomonas strain has been shown to grow in a wide pH range; from pH 3 up to a pH of 9, which could be an advantage depending on the substrate and product. Both strains showed great robustness and tolerance to high density fermentation in a bioreactor, indicating a large potential for industrial applications. In order to use these novel strains to produce valuable compounds from lignocellulosic biomass they need to be modifiable and domesticated. To do this, a wide range of molecular tools must be identified and developed. We first established efficient transformation protocols for both strains. We have characterized a number of different constitutive and inducible promoters, a prerequisite for further development of efficient engineering tools. We are currently in the process of developing molecular tools for gene deletions and integrations to i

The lack of financial incentive from the pharmaceutical industry has led to a stagnation in the discovery of new antimicrobial compounds, creating a severe threat to human health from multiresistant bacterial pathogens. Advances in cost-efficient sequencing equipment and bioinformatics tools enable the identification of biosynthetic gene clusters with desirable properties, leading to the discovery of new secondary metabolite compounds and producer strains from Actinomyces bacteria. In order to investigate the biosynthetic potential in Actinomycetes, we have developed a comprehensive and efficient pipeline for generating a dataset of more than 1000 Actinomycete genomes sequenced using Nanopore and Illumina sequencing platforms. The pipeline spans the entire process from sampling to benchmarking of the assembled genome. By predicting the domains required for synthesizing natural products using antiSMASH (antibiotics & Secondary Metabolite Analysis Shell) and comparing them to known biosynthetic pathways we will be able to expand our knowledge of the properties, underlying mechanisms and in turn discover currently unknown features of sustainable natural products.

Here, we report the complete, circular genome sequence of a potential novel species from the underexplored Alphaproteobacterial genus Bosea. Bosea sp. NBC_00550 was isolated from a soil sample collected in Lyngby, Denmark. We explore the biosynthetic potential of Bosea sp. NBC_00550 and compare it with that of other Bosea species.

Microorganisms produce small bioactive compounds as part of their secondary or specialised metabolism. Often, such metabolites have antimicrobial, anticancer, antifungal, antiviral or other bio-activities and thus play an important role for applications in medicine and agriculture. In the past decade, genome mining has become a widely-used method to explore, access, and analyse the available biodiversity of these compounds. Since 2011, the 'antibiotics and secondary metabolite analysis shell-antiSMASH' (https://antismash.secondarymetabolites.org/) has supported researchers in their microbial genome mining tasks, both as a free to use web server and as a standalone tool under an OSI-approved open source licence. It is currently the most widely used tool for detecting and characterising biosynthetic gene clusters (BGCs) in archaea, bacteria, and fungi. Here, we present the updated version 7 of antiSMASH. antiSMASH 7 increases the number of supported cluster types from 71 to 81, as well as containing improvements in the areas of chemical structure prediction, enzymatic assembly-line visualisation and gene cluster regulation.

Microorganisms produce small bioactive compounds as part of their secondary or specialised metabolism. Often, such metabolites have antimicrobial, anticancer, antifungal, antiviral or other bio-Activities and thus play an important role for applications in medicine and agriculture. In the past decade, genome mining has become a widely-used method to explore, access, and analyse the available biodiversity of these compounds. Since 2011, the 'antibiotics and secondary metabolite analysis shell-antiSMASH' (https://antismash.secondarymetabolites.org/) has supported researchers in their microbial genome mining tasks, both as a free to use web server and as a standalone tool under an OSI-Approved open source licence. It is currently the most widely used tool for detecting and characterising biosynthetic gene clusters (BGCs) in archaea, bacteria, and fungi. Here, we present the updated version 7 of antiSMASH. antiSMASH 7 increases the number of supported cluster types from 71 to 81, as well as containing improvements in the areas of chemical structure prediction, enzymatic assembly-line visualisation and gene cluster regulation.

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.

Here, we report the complete genome sequences of Methylorubrum extorquens NBC_00036 and Methylorubrum extorquens NBC_00404. The genomes were sequenced using the Oxford Nanopore Technologies MinION and Illumina NovaSeq systems. Both genomes are circular, with sizes of 5,661,342 bp and 5,869,086 bp, respectively.

Metabolomics experiments generate highly complex datasets, which are time and work-intensive, sometimes even error-prone if inspected manually. Therefore, new methods for automated, fast, reproducible, and accurate data processing and dereplication are required. Here, we present UmetaFlow, a computational workflow for untargeted metabolomics that combines algorithms for data pre-processing, spectral matching, molecular formula and structural predictions, and an integration to the GNPS workflows Feature-Based Molecular Networking and Ion Identity Molecular Networking for downstream analysis. UmetaFlow is implemented as a Snakemake workflow, making it easy to use, scalable, and reproducible. For more interactive computing, visualization, as well as development, the workflow is also implemented in Jupyter notebooks using the Python programming language and a set of Python bindings to the OpenMS algorithms (pyOpenMS). Finally, UmetaFlow is also offered as a web-based Graphical User Interface for parameter optimization and processing of smaller-sized datasets. UmetaFlow was validated with in-house LC–MS/MS datasets of actinomycetes producing known secondary metabolites, as well as commercial standards, and it detected all expected features and accurately annotated 76% of the molecular formulas and 65% of the structures. As a more generic validation, the publicly available MTBLS733 and MTBLS736 datasets were used for benchmarking, and UmetaFlow detected more than 90% of all ground truth features and performed exceptionally well in quantification and discriminating marker selection. We anticipate that UmetaFlow will provide a useful platform for the interpretation of large metabolomics datasets. Graphical Abstract: [Figure not available: see fulltext.]

With an ever-increasing amount of (meta)genomic data being deposited in sequence databases, (meta)genome mining for natural product biosynthetic pathways occupies a critical role in the discovery of novel pharmaceutical drugs, crop protection agents and biomaterials. The genes that encode these pathways are often organised into biosynthetic gene clusters (BGCs). In 2015, we defined the Minimum Information about a Biosynthetic Gene cluster (MIBiG): a standardised data format that describes the minimally required information to uniquely characterise a BGC. We simultaneously constructed an accompanying online database of BGCs, which has since been widely used by the community as a reference dataset for BGCs and was expanded to 2021 entries in 2019 (MIBiG 2.0). Here, we describe MIBiG 3.0, a database update comprising large-scale validation and re-annotation of existing entries and 661 new entries. Particular attention was paid to the annotation of compound structures and biological activities, as well as protein domain selectivities. Together, these new features keep the database up-to-date, and will provide new opportunities for the scientific community to use its freely available data, e.g. for the training of new machine learning models to predict sequence-structure-function relationships for diverse natural products.

Actinomycetota (Actinobacteria) is an ecologically and industrially important phylum which is challenging to extract pure high-molecular-weight (HMW) DNA from. This protocol provides a parallelized, cost-effective, and straightforward approach for consistently extracting pure HMW DNA using modified non-toxic commercial kits suitable for higher throughput applications. We further provide a workflow for sequencing and assembly of complete genomes using an optimized Oxford Nanopore rapid barcoding protocol and Illumina data error correction.

In silico genome mining provides easy access to secondary metabolite biosynthetic gene clusters (BGCs) encoding the biosynthesis of many bioactive compounds, which are the basis for many important drugs used in human medicine. However, the association between BGCs and other functions encoded in the genomes of producers have remained elusive. Here, we present a systems biology workflow that integrates genome mining with a detailed pangenome analysis for detecting genes associated with a particular BGC. We analyzed 3,889 enterobacterial genomes and found 13,266 BGCs, represented by 252 distinct BGC families and 347 additional singletons. A pangenome analysis revealed 88 genes putatively associated with a specific BGC coding for the colon cancer-related colibactin that code for diverse metabolic and regulatory functions. The presented workflow opens up the possibility to discover novel secondary metabolites, better understand their physiological roles, and provides a guide to identify and analyze BGC associated gene sets.

Microorganisms produce a wide variety of secondary/specialized metabolites (SMs), the majority of which are yet to be discovered. These natural products play multiple roles in microbiomes and are important for microbial competition, communication, and success in the environment. SMs have been our major source of antibiotics and are used in a range of biotechnological applications. In silico mining for biosynthetic gene clusters (BGCs) encoding the production of SMs is commonly used to assess the genetic potential of organisms. However, as BGCs span tens to over 200 kb, identifying complete BGCs requires genome data that has minimal assembly gaps within the BGCs, a prerequisite that was previously only met by individually sequenced genomes. Here, we assess the performance of the currently available genome mining platform antiSMASH on 1,080 high-quality metagenome-assembled bacterial genomes (HQ MAGs) previously produced from wastewater treatment plants (WWTPs) using a combination of long-read (Oxford Nanopore) and short-read (Illumina) sequencing technologies. More than 4,200 different BGCs were identified, with 88% of these being complete. Sequence similarity clustering of the BGCs implies that the majority of this biosynthetic potential likely encodes novel compounds, and few BGCs are shared between genera. We identify BGCs in abundant and functionally relevant genera in WWTPs, suggesting a role of secondary metabolism in this ecosystem. We find that the assembly of HQ MAGs using long-read sequencing is vital to explore the genetic potential for SM production among the uncultured members of microbial communities. IMPORTANCE Cataloguing secondary metabolite (SM) potential using genome mining of metagenomic data has become the method of choice in bioprospecting for novel compounds. However, accurate biosynthetic gene cluster (BGC) detection requires unfragmented genomic assemblies, which have been technically difficult to obtain from metagenomes until very rec

The actinomycete Streptomyces sp. strain Gö40/10 has the potential to produce a range of secondary metabolites, one of which is collinolactone, a compound with neuroprotective properties and potential for pharmaceutical applications. The genome was sequenced with Oxford Nanopore Technologies MinION and Illumina MiSeq systems and consists of a single 9,635,564-nucleotide linear chromosome.