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Biological systems are robust and redundant. The redundancy can manifest as alternative metabolic pathways. Synthetic double lethals are pairs of reactions that, when deleted simultaneously, abrogate cell growth. However, removing one reaction allows the rerouting of metabolites through alternative pathways. Little is known about these hidden linkages between pathways. Understanding them in the context of pathogens is useful for therapeutic innovations. We propose a constraint-based optimisation approach to identify inter-dependencies between metabolic pathways. It minimises rerouting between two reaction deletions, corresponding to a synthetic lethal pair, and outputs the set of reactions vital for metabolic rewiring, known as the synthetic lethal cluster. We depict the results for different pathogens and show that the reactions span across metabolic modules, illustrating the complexity of metabolism. Finally, we demonstrate how the two classes of synthetic lethals play a role in metabolic networks and influence the different properties of a synthetic lethal cluster., (© 2024. The Author(s).)

Strains across the Lactobacillaceae family form the basis for a trillion-dollar industry. Our understanding of the genomic basis for their key traits is fragmented, however, including the metabolism that is foundational to their industrial uses. Pangenome analysis of publicly available Lactobacillaceae genomes allowed us to generate genome-scale metabolic network reconstructions for 26 species of industrial importance. Their manual curation led to more than 75,000 gene-protein-reaction associations that were deployed to generate 2,446 genome-scale metabolic models. Cross-referencing genomes and known metabolic traits allowed for manual metabolic network curation and validation of the metabolic models. As a result, we provide the first pangenomic basis for metabolism in the Lactobacillaceae family and a collection of predictive computational metabolic models that enable a variety of practical uses.IMPORTANCE Lactobacillaceae , a bacterial family foundational to a trillion-dollar industry, is increasingly relevant to biosustainability initiatives. Our study, leveraging approximately 2,400 genome sequences, provides a pangenomic analysis of Lactobacillaceae metabolism, creating over 2,400 curated and validated genome-scale models (GEMs). These GEMs successfully predict (i) unique, species-specific metabolic reactions; (ii) niche-enriched reactions that increase organism fitness; (iii) essential media components, offering insights into the global amino acid essentiality of Lactobacillaceae ; and (iv) fermentation capabilities across the family, shedding light on the metabolic basis of Lactobacillaceae -based commercial products. This quantitative understanding of Lactobacillaceae metabolic properties and their genomic basis will have profound implications for the food industry and biosustainability, offering new insights and tools for strain selection and manipulation., Competing Interests: The authors declare no conflict of interest.

Filamentous Actinobacteria, recently renamed Actinomycetia, are the most prolific source of microbial bioactive natural products. Studies on biosynthetic gene clusters benefit from or require chromosome-level assemblies. Here, we provide DNA sequences from >1000 isolates: 881 complete genomes and 153 near-complete genomes, representing 28 genera and 389 species, including 244 likely novel species. All genomes are from filamentous isolates of the class Actinomycetia from the NBC culture collection. The largest genus is Streptomyces with 886 genomes including 742 complete assemblies. We use this data to show that analysis of complete genomes can bring biological understanding not previously derived from more fragmented sequences or less systematic datasets. We document the central and structured location of core genes and distal location of specialized metabolite biosynthetic gene clusters and duplicate core genes on the linear Streptomyces chromosome, and analyze the content and length of the terminal inverted repeats which are characteristic for Streptomyces. We then analyze the diversity of trans-AT polyketide synthase biosynthetic gene clusters, which encodes the machinery of a biotechnologically highly interesting compound class. These insights have both ecological and biotechnological implications in understanding the importance of high quality genomic resources and the complex role synteny plays in Actinomycetia biology., (© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Genome mining is revolutionizing natural products discovery efforts. The rapid increase in available genomes demands comprehensive computational platforms to effectively extract biosynthetic knowledge encoded across bacterial pangenomes. Here, we present BGCFlow, a novel systematic workflow integrating analytics for large-scale genome mining of bacterial pangenomes. BGCFlow incorporates several genome analytics and mining tools grouped into five common stages of analysis such as: (i) data selection, (ii) functional annotation, (iii) phylogenetic analysis, (iv) genome mining, and (v) comparative analysis. Furthermore, BGCFlow provides easy configuration of different projects, parallel distribution, scheduled job monitoring, an interactive database to visualize tables, exploratory Jupyter Notebooks, and customized reports. Here, we demonstrate the application of BGCFlow by investigating the phylogenetic distribution of various biosynthetic gene clusters detected across 42 genomes of the Saccharopolyspora genus, known to produce industrially important secondary/specialized metabolites. The BGCFlow-guided analysis predicted more accurate dereplication of BGCs and guided the targeted comparative analysis of selected RiPPs. The scalable, interoperable, adaptable, re-entrant, and reproducible nature of the BGCFlow will provide an effective novel way to extract the biosynthetic knowledge from the ever-growing genomic datasets of biotechnologically relevant bacterial species., (© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Lactobacillaceae represent a large family of important microbes that are foundational to the food industry. Many genome sequences of Lactobacillaceae strains are now available, enabling us to conduct a comprehensive pangenome analysis of this family. We collected 3591 high-quality genomes from public sources and found that: 1) they contained enough genomes for 26 species to perform a pangenomic analysis, 2) the normalized Heap's coefficient λ (a measure of pangenome openness) was found to have an average value of 0.27 (ranging from 0.07 to 0.37), 3) the pangenome openness was correlated with the abundance and genomic location of transposons and mobilomes, 4) the pangenome for each species was divided into core, accessory, and rare genomes, that highlight the species-specific properties (such as motility and restriction-modification systems), 5) the pangenome of Lactiplantibacillus plantarum (which contained the highest number of genomes found amongst the 26 species studied) contained nine distinct phylogroups, and 6) genome mining revealed a richness of detected biosynthetic gene clusters, with functions ranging from antimicrobial and probiotic to food preservation, but ∼93% were of unknown function. This study provides the first in-depth comparative pangenomics analysis of the Lactobacillaceae family., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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., (© 2023. The Author(s).)

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., (© 2022. The Author(s).)

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 recently with new long-read technologies. Here, we determined the biosynthetic potential of activated sludge (AS), the microbial community used in resource recovery and wastewater treatment, by mining high-quality metagenome-assembled genomes generated from long-read data. We found over 4,000 BGCs, including BGCs in abundant process-critical bacteria, with no similarity to the BGCs of characterized products. We show how long-read MAGs are required to confidently assemble complete BGCs, and we determined that the AS BGCs from different studies have very little overlap, suggesting that AS is a rich source of biosynthetic potential and new bioactive compounds.

Actinomycetes make a wealth of complex, structurally diverse natural products, and a key challenge is to link them to their biosynthetic gene clusters and delineate the reactions catalyzed by each of the enzymes. Here, we report the biosynthetic gene cluster for pyracrimycin A, a set of nine genes that includes a core nonribosomal peptide synthase ( pymB ) that utilizes serine and proline as precursors and a monooxygenase ( pymC ) that catalyzes Baeyer-Villiger oxidation. The cluster is similar to the one for brabantamide A; however, pyracrimycin A biosynthesis differs in that feeding experiments with isotope-labeled serine and proline suggest that a ring opening reaction takes place and a carbon is lost from serine downstream of the oxidation reaction. Based on these data, we propose a full biosynthesis pathway for pyracrimycin A.

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., Competing Interests: The authors declare no competing interests., (© 2022 The Authors.)

Streptomyces are well-known producers of a range of different secondary metabolites, including antibiotics and other bioactive compounds. Recently, it has been demonstrated that "silent" biosynthetic gene clusters (BGCs) can be activated by heterologously expressing transcriptional regulators from other BGCs. Here, we have activated a silent BGC in Streptomyces sp. CA-256286 by overexpression of a set of SARP family transcriptional regulators. The structure of the produced compound was elucidated by NMR and found to be an N -acetyl cysteine adduct of the pyranonaphtoquinone polyketide 3'- O -α-d-forosaminyl-(+)-griseusin A. Employing a combination of multi-omics and metabolic engineering techniques, we identified the responsible BGC. These methods include genome mining, proteomics and transcriptomics analyses, in combination with CRISPR induced gene inactivations and expression of the BGC in a heterologous host strain. This work demonstrates an easy-to-implement workflow of how silent BGCs can be activated, followed by the identification and characterization of the produced compound, the responsible BGC, and hints of its biosynthetic pathway.

Streptomyces griseofuscus DSM 40191 is a fast growing Streptomyces strain that remains largely underexplored as a heterologous host. Here, we report the genome mining of S. griseofuscus, followed by the detailed exploration of its phenotype, including the production of native secondary metabolites and ability to utilise carbon, nitrogen, sulphur and phosphorus sources. Furthermore, several routes for genetic engineering of S. griseofuscus were explored, including use of GusA-based vectors, CRISPR-Cas9 and CRISPR-cBEST-mediated knockouts. Two out of the three native plasmids were cured using CRISPR-Cas9 technology, leading to the generation of strain S. griseofuscus DEL1. DEL1 was further modified by the full deletion of a pentamycin BGC and an unknown NRPS BGC, leading to the generation of strain DEL2, lacking approx. 500 kbp of the genome, which corresponds to a 5.19% genome reduction. DEL2 can be characterized by faster growth and inability to produce three main native metabolites: lankacidin, lankamycin, pentamycin and their derivatives. To test the ability of DEL2 to heterologously produce secondary metabolites, the actinorhodin BGC was used. We were able to observe a formation of a blue halo, indicating a potential production of actinorhodin by both DEL2 and a wild type., (© 2021. The Author(s).)

Microbes produce a plethora of secondary (or specialized) metabolites that, although not essential for primary metabolism, benefit them to survive in the environment, communicate, and influence cell differentiation. Biosynthetic gene clusters (BGCs), responsible for the production of these secondary metabolites, are readily identifiable on bacterial genome sequences. Understanding the phylogeny and distribution of BGCs helps us to predict the natural product synthesis ability of new isolates. Here, we examined 310 genomes from the Bacillus subtilis group, determined the inter- and intraspecies patterns of absence/presence for all BGCs, and assigned them to defined gene cluster families (GCFs). This allowed us to establish patterns in the distribution of both known and unknown products. Further, we analyzed variations in the BGC structures of particular families encoding natural products, such as plipastatin, fengycin, iturin, mycosubtilin, and bacillomycin. Our detailed analysis revealed multiple GCFs that are species or clade specific and a few others that are scattered within or between species, which will guide exploration of the chemodiversity within the B. subtilis group. Surprisingly, we discovered that partial deletion of BGCs and frameshift mutations in selected biosynthetic genes are conserved within phylogenetically related isolates, although isolated from around the globe. Our results highlight the importance of detailed genomic analysis of BGCs and the remarkable phylogenetically conserved erosion of secondary metabolite biosynthetic potential in the B. subtilis group. IMPORTANCE Members of the B. subtilis species complex are commonly recognized producers of secondary metabolites, among those, the production of antifungals, which makes them promising biocontrol strains. While there are studies examining the distribution of well-known secondary metabolites in Bacilli , intraspecies clade-specific distribution has not been systematically reported for the B. subtilis group. Here, we report the complete biosynthetic potential within the B. subtilis group to explore the distribution of the biosynthetic gene clusters and to reveal an exhaustive phylogenetic conservation of secondary metabolite production within Bacillus that supports the chemodiversity within this species complex. We identify that certain gene clusters acquired deletions of genes and particular frameshift mutations, rendering them inactive for secondary metabolite biosynthesis, a conserved genetic trait within phylogenetically conserved clades of certain species. The overview guides the assignment of the secondary metabolite production potential of newly isolated Bacillus strains based on genome sequence and phylogenetic relatedness., (Copyright © 2021 Steinke et al.)

Systems biology approaches are increasingly applied to explore the potential of actinomycetes for the discovery and optimal production of antibiotics. In particular, genome-scale metabolic models (GEMs) of various actinomycetes are reconstructed at a faster rate in recent years, which has opened avenues to study interaction between primary and secondary metabolism at systems level, and to predict gene manipulation targets for overproduction of important antibiotics. Here, the status of actinomycetes' GEMs and their applications for designing antibiotics-overproducing strains are presented. Despite advances in the practice of GEM reconstruction, actinomycetes' GEMs still remain incomplete in describing a full set of biosynthetic pathways of secondary metabolites. As to the GEM-based strategies, various simulation methods are deployed to better describe secondary metabolism by introducing changes in constraints and/or objective function as well as by using omics data. Gene manipulation targeting algorithms developed for metabolic engineering of model organisms have also been actively applied to actinomycetes for the antibiotics production. Further consideration of computational resources dedicated to secondary metabolites in addition with automated GEM reconstruction tools will further upgrade GEMs of actinomycetes for antibiotics discovery and development., (© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)