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.

Retinoblastoma (RB) is a childhood eye cancer. Currently, chemotherapy, local therapy, and enucleation are the main ways in which these tumors are managed. The present work is the first study that uses constraint‐based reconstruction and analysis approaches to identify and explain RB‐specific survival strategies, which are RB tumor specific. Importantly, our model‐specific secretion profile is also found in RB1‐depleted human retinal cells in vitro and suggests that novel biomarkers involved in lipid metabolism may be important. Finally, RB‐specific synthetic lethals have been predicted as lipid and nucleoside transport proteins that can aid in novel drug target development.

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.

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.

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.

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 in the ever-growing genomic datasets of biotechnologically relevant bacterial species. BGCFlow is available for downloading at https://github.com/NBChub/bgcflow.

13The growing number of sequenced genomes enables the study of secondary metabolite 14 biosynthetic gene clusters (BGC) in phyla beyond well-studied soil bacteria. We mined 2627 15 enterobacterial genomes to detect 8604 BGCs, including nonribosomal peptide synthetases, 16 siderophores, polyketide-nonribosomal peptide hybrids, and 60 other BGC types, with an 17 average of around 3.3 BGCs per genome. These BGCs represented 212 distinct BGC families, of 18 which only 20 have associated products in the MIBiG standard database with functions such as 19 siderophores, antibiotics, and genotoxins. Pangenome analysis identified genes associated with a 20 specific BGC encoding for colon cancer-related colibactin. In one example, we associated genes 21 involved in the type VI secretion system with the presence of a colibactin BGC in Escherichia. 22This richness of BGCs in enterobacteria opens up the possibility to discover novel secondary 23 metabolites, their physiological roles and provides a guide to identify and understand PKS 24 associated gene sets. 25 Main 26Secondary metabolites produced by a range of microorganisms display medicinally and 27 industrially important properties, as well as mediate microbe-host and microbe-microbe 28 interactions. Secondary metabolite biosynthesis often involves mega-enzymes such as polyketide 29 synthases (PKS) and non-ribosomal peptide synthetases (NRPS) that are encoded by large 30 biosynthetic gene clusters (BGCs). Recent advances in genome sequencing technology and 31 genome mining tools revealed an unexplored richness and diversity of BGCs encoding secondary 32 2 metabolites 1-4 . In addition, the availability of a large number of genomes from the same species 33 allowed for pangenome analysis revealing intra-species diversity, such as metabolic capabilities 34 5,6 . The focus of many genome mining based studies have been well-established secondary 35 metabolite producers, such as bacilli, actinobacteria, or myxobacteria 7,8 . In comparison with many 36 of the popular secondary metabolite producers, Escherichia coli or other enterobacteria have larger 37 availability of sequenced genomes, higher quality of genome annotations, comprehensive curated 38 databases, and extensive tools for data analysis. With the exception of Photorhabdus, Xenorhabdus 39 and related genera, which are known to produce a diverse range of secondary metabolites 9,10 , 40 enterobacteria are known to produce few secondary metabolites. These include metal ion chelators 41 like enterobactin, yersiniabactin 11,12 , colon cancer-related genotoxin colibactin 13,14 , antibiotic 42 althiomycin 15 , red pigment prodigiosin and biosurfactant serrawettin W1 16 . Here, we aim to 43

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