Identification Of Biosynthetic Gene Cluster Research Articles

In Silico Profiling, Docking Analysis, and Protein Interactions of Secondary Metabolites in Musa spp. Against the SGE1 Protein of Fusariumoxysporum f. sp. cubense

Banana Fusarium Wilt (BFW), caused by Fusariumoxysporum f. sp. cubense (Foc), threatens banana crops globally, with the pathogen's virulence partially regulated by the Sge1 transcription factor, which enhances disease severity. Certain Musa species display resistance to Foc, suggesting inherent genetic traits that confer immunity against Sge1Foc. This study utilized bioinformatics tools to investigate the mechanisms underlying this resistance in Musa accuminata subsp. Malaccensis.Through in silico analyses, we explored interactions between Musa spp. and Foc, focusing on the Sge1 protein. Tools such as Anti-SMASH, AutoDockVina 4.0, STRING, and Phoenix facilitated the profiling of secondary metabolites in Musa spp. and the identification of biosynthetic gene clusters involved in defense. Our results indicate that secondary metabolites, including saccharides, terpenes, and polyketides, are crucial to the plant's immune response. Molecular docking studies of selected Musa metabolites, such as 3-Phenylphenol, catechin, and epicatechin, revealed 3-Phenylphenol as having the highest binding affinity to the Sge1Foc protein (-6.7kcal/mol).Further analysis of gene clusters associated with secondary metabolite biosynthesis in Musa spp. identified key domains like Chol synthase, Phenylalanine ammonia-lyase, Aminotran 1-2, and CoA-ligase, which are integral to phenylpropanoid production—a critical pathway for secondary metabolites. The study highlights that the phenylpropanoid pathway and secondary metabolite biosynthesis are vital for Musa spp. resistance to Foc. Flavonoids and lignin may inhibit Sge1 protein formation, potentially disrupting Foc's cellular processes. These findings emphasize the role of phenylpropanoid pathways and secondary metabolites in combating BFW and suggest that targeting these pathways could offer innovative strategies for enhancing resistance and controlling BFW in banana crops. This research lays the groundwork for developing sustainable methods to protect banana cultivation and ensure food security.

Mining metagenomic data to gain a new insight into the gut microbial biosynthetic potential in placental mammals.

Mammals host a remarkable diversity and abundance of gut microbes. Biosynthetic gene clusters (BGCs) in microbial genomes encode biologically active chemical products and play an important role in microbe-host interactions. Traditionally, the exploration of gut microbial metabolic functions has relied on the pure culture method. However, given the limited amounts of microbes being cultivated, insights into the metabolism of gut microbes in mammals continued to be very limited. In this study, we adopted a computational pipeline for mining the metagenomic data (named taxonomy-guided identification of biosynthetic gene clusters, TaxiBGC) to identify experimentally verified BGCs in 373 metagenomes across 53 mammalian species in an unbiased manner. We demonstrated that polyketides (PKs) and nonribosomal peptides (NRPs) are representative of mammals, and the products derived from them were associated with cell-cell communication and resistance to inflammation. Large carnivores had the highest number of BGCs, followed by large herbivores and small mammals. We also observed that the large mammals had more common BGCs that aid in the biosynthesis of a variety of natural products. However, small mammals not only had fewer BGCs but were also unique to each species. Our results provide novel insights into the mining of metagenomic data sets to identify active BGCs and their products across mammals.IMPORTANCEThe gut microbes host numerous biosynthetic gene clusters (BGCs) that biosynthesize natural products and impact the host's physiology. Historically, our understanding of BGCs in mammalian gut microbes was largely based on studies on cultured isolates; however, only a small fraction of mammal-associated microbes have been investigated. The biochemical diversity of the mammalian gut microbiota is poorly understood. Metagenomic sequencing contains data from a vast number of organisms and provides information on the total gene content of communities. Unfortunately, the existing BGC prediction tools are designed for individual microbial genomes. Recently, a BGC prediction tool called the taxonomy-guided identification of biosynthetic gene clusters (TaxiBGC) that directly mine the metagenome was developed. To gain new insights into the microbial metabolism, we used TaxiBGC to predict BGCs from 373 metagenomes across 53 mammalian species representing seven orders. Our findings elucidate the functional activities of complex microbial communities in the gut.

Identification of Terpene-Related Biosynthetic Gene Clusters in Tobacco through Computational-Based Genomic, Transcriptomic, and Metabolic Analyses

Terpenes and terpenoids contribute aroma and flavor that influence consumer preferences in selecting plant-based products. Computational identification of biosynthetic gene clusters (BGCs) in plants can pave the way for future biosynthetic genetic engineering. Using integrative genomic, transcriptomic, and metabolic pathway annotation analyses, 35 BGCs were identified in tobacco with high confidence. Among the 35 BGCs identified, 7 were classified as terpene biosynthesis-related BGCs. Two BGCs found on C13 and C14 chromosomes belonged to terpene and saccharide-terpene biosynthetic classes that were only 93 Mb and 189 Kb apart, respectively. Other clusters have lengths ranging from 120 Kb (Cluster 9) to 1.6 Mb (Cluster 18). Each cluster contained five (Cluster 21) to twenty genes (Cluster 32), and the number of terpene synthase genes present in the clusters also varied from one (Clusters 18 and 21) to eight (Cluster 32). Gene expression profiling using diurnal and topping transcriptome datasets identified co-expressing genes within modules and varying levels of expression among modules as represented by the normalized enrichment score measured in each module. The positions pinpointed from these computational analyses will allow for the more efficient modifications of specific genes and BGCs for the development of tobacco-based products with improved aroma and flavor.

Cost-effective hybrid long-short read assembly delineates alternative GC-rich Streptomyces hosts for natural product discovery

With the advent of rapid automated in silico identification of biosynthetic gene clusters (BGCs), genomics presents vast opportunities to accelerate natural product (NP) discovery. However, prolific NP producers, Streptomyces, are exceptionally GC-rich (>80%) and highly repetitive within BGCs. These pose challenges in sequencing and high-quality genome assembly which are currently circumvented via intensive sequencing. Here, we outline a more cost-effective workflow using multiplex Illumina and Oxford Nanopore sequencing with hybrid long-short read assembly algorithms to generate high quality genomes. Our protocol involves subjecting long read-derived assemblies to up to 4 rounds of polishing with short reads to yield accurate BGC predictions. We successfully sequenced and assembled 8 GC-rich Streptomyces genomes whose lengths range from 7.1 to 12.1 Mb with a median N50 of 8.2 Mb. Taxonomic analysis revealed previous misrepresentation among these strains and allowed us to propose a potentially new species, Streptomyces sydneybrenneri. Further comprehensive characterization of their biosynthetic, pan-genomic and antibiotic resistance features especially for molecules derived from type I polyketide synthase (PKS) BGCs reflected their potential as alternative NP hosts. Thus, the genome assemblies and insights presented here are envisioned to serve as gateway for the scientific community to expand their avenues in NP discovery.

Lessons from assembling a microbial natural product and pre-fractionated extract library in an academic laboratory.

Microbial natural products are specialized metabolites that are sources of many bioactive compounds including antibiotics, antifungals, antiparasitics, anticancer agents, and probes of biology. The assembly of libraries of producers of natural products has traditionally been the province of the pharmaceutical industry. This sector has gathered significant historical collections of bacteria and fungi to identify new drug leads with outstanding outcomes-upwards of 60% of drug scaffolds originate from such libraries. Despite this success, the repeated rediscovery of known compounds and the resultant diminishing chemical novelty contributed to a pivot from this source of bioactive compounds toward more tractable synthetic compounds in the drug industry. The advent of advanced mass spectrometry tools, along with rapid whole genome sequencing and in silico identification of biosynthetic gene clusters that encode the machinery necessary for the synthesis of specialized metabolites, offers the opportunity to revisit microbial natural product libraries with renewed vigor. Assembling a suitable library of microbes and extracts for screening requires the investment of resources and the development of methods that have customarily been the proprietary purview of large pharmaceutical companies. Here, we report a perspective on our efforts to assemble a library of natural product-producing microbes and the establishment of methods to extract and fractionate bioactive compounds using resources available to most academic labs. We validate the library and approach through a series of screens for antimicrobial and cytotoxic agents. This work serves as a blueprint for establishing libraries of microbial natural product producers and bioactive extract fractions suitable for screens of bioactive compounds.

Complete Genome Sequence Analysis of Kribbella sp. CA-293567 and Identification of the Kribbellichelins A & B and Sandramycin Biosynthetic Gene Clusters.

Minor genera actinomycetes are considered a promising source of new secondary metabolites. The strain Kribbella sp. CA-293567 produces sandramycin and kribbellichelins A & B In this work, we describe the complete genome sequencing of this strain and the in silico identification of biosynthetic gene clusters (BGCs), focusing on the pathways encoding sandramycin and kribbellichelins A-B. We also present a comparative analysis of the biosynthetic potential of 38 publicly available genomes from Kribbella strains.

TaxiBGC: a Taxonomy-Guided Approach for Profiling Experimentally Characterized Microbial Biosynthetic Gene Clusters and Secondary Metabolite Production Potential in Metagenomes.

Biosynthetic gene clusters (BGCs) in microbial genomes encode bioactive secondary metabolites (SMs), which can play important roles in microbe-microbe and host-microbe interactions. Given the biological significance of SMs and the current profound interest in the metabolic functions of microbiomes, the unbiased identification of BGCs from high-throughput metagenomic data could offer novel insights into the complex chemical ecology of microbial communities. Currently available tools for predicting BGCs from shotgun metagenomes have several limitations, including the need for computationally demanding read assembly, predicting a narrow breadth of BGC classes, and not providing the SM product. To overcome these limitations, we developed taxonomy-guided identification of biosynthetic gene clusters (TaxiBGC), a command-line tool for predicting experimentally characterized BGCs (and inferring their known SMs) in metagenomes by first pinpointing the microbial species likely to harbor them. We benchmarked TaxiBGC on various simulated metagenomes, showing that our taxonomy-guided approach could predict BGCs with much-improved performance (mean F1 score, 0.56; mean PPV score, 0.80) compared with directly identifying BGCs by mapping sequencing reads onto the BGC genes (mean F1 score, 0.49; mean PPV score, 0.41). Next, by applying TaxiBGC on 2,650 metagenomes from the Human Microbiome Project and various case-control gut microbiome studies, we were able to associate BGCs (and their SMs) with different human body sites and with multiple diseases, including Crohn's disease and liver cirrhosis. In all, TaxiBGC provides an in silico platform to predict experimentally characterized BGCs and their SM production potential in metagenomic data while demonstrating important advantages over existing techniques. IMPORTANCE Currently available bioinformatics tools to identify BGCs from metagenomic sequencing data are limited in their predictive capability or ease of use to even computationally oriented researchers. We present an automated computational pipeline called TaxiBGC, which predicts experimentally characterized BGCs (and infers their known SMs) in shotgun metagenomes by first considering the microbial species source. Through rigorous benchmarking techniques on simulated metagenomes, we show that TaxiBGC provides a significant advantage over existing methods. When demonstrating TaxiBGC on thousands of human microbiome samples, we associate BGCs encoding bacteriocins with different human body sites and diseases, thereby elucidating a possible novel role of this antibiotic class in maintaining the stability of microbial ecosystems throughout the human body. Furthermore, we report for the first time gut microbial BGC associations shared among multiple pathologies. Ultimately, we expect our tool to facilitate future investigations into the chemical ecology of microbial communities across diverse niches and pathologies.

Overview on Strategies and Assays for Antibiotic Discovery.

The increase in antibiotic resistance poses a major threat to global health. Actinomycetes, the Gram-positive bacteria of the order Actinomycetales, are fertile producers of bioactive secondary metabolites, including antibiotics. Nearly two-thirds of antibiotics that are used for the treatment of bacterial infections were originally isolated from actinomycetes strains belonging to the genus Streptomyces. This emphasizes the importance of actinomycetes in antibiotic discovery. However, the identification of a new antimicrobial compound and the exploration of its mode of action are very challenging tasks. Therefore, different approaches that enable the “detection” of an antibiotic and the characterization of the mechanisms leading to the biological activity are indispensable. Beyond bioinformatics tools facilitating the identification of biosynthetic gene clusters (BGCs), whole cell-screenings—in which cells are exposed to actinomycete-derived compounds—are a common strategy applied at the very early stage in antibiotic drug development. More recently, target-based approaches have been established. In this case, the drug candidates were tested for interactions with usually validated targets. This review focuses on the bioactivity-based screening methods and provides the readers with an overview on the most relevant assays for the identification of antibiotic activity and investigation of mechanisms of action. Moreover, the article includes examples of the successful application of these methods and suggestions for improvement.

Genome Mining as an Alternative Way for Screening the Marine Organisms for Their Potential to Produce UV-Absorbing Mycosporine-like Amino Acid.

Mycosporine-like amino acids (MAAs) are small molecules with robust ultraviolet (UV)-absorbing capacities and a huge potential to be used as an environmentally friendly natural sunscreen. MAAs, temperature, and light-stable compounds demonstrate powerful photoprotective capacities and the ability to capture light in the UV-A and UV-B ranges without the production of damaging free radicals. The biotechnological uses of these secondary metabolites have been often limited by the small quantities restored from natural resources, variation in MAA expression profiles, and limited success in heterologous expression systems. Overcoming these obstacles requires a better understanding of MAA biosynthesis and its regulatory processes. MAAs are produced to a certain extent via a four-enzyme pathway, including genes encoding enzymes dehydroquinate synthase, enzyme O-methyltransferase, adenosine triphosphate grasp, and a nonribosomal peptide synthetase. However, there are substantial genetic discrepancies in the MAA genetic pathway in different species, suggesting further complexity of this pathway that is yet to be fully explored. In recent years, the application of genome-mining approaches allowed the identification of biosynthetic gene clusters (BGCs) that resulted in the discovery of many new compounds from unconventional sources. This review explores the use of novel genomics tools for linking BGCs and secondary metabolites based on the available omics data, including MAAs, and evaluates the potential of using novel genome-mining tools to reveal a cryptic potential for new bioproduct screening approaches and unrevealing new MAA producers.

Improving candidate Biosynthetic Gene Clusters in fungi through reinforcement learning.

MotivationPrecise identification of Biosynthetic Gene Clusters (BGCs) is a challenging task. Performance of BGC discovery tools is limited by their capacity to accurately predict components belonging to candidate BGCs, often overestimating cluster boundaries. To support optimizing the composition and boundaries of candidate BGCs, we propose reinforcement learning approach relying on protein domains and functional annotations from expert curated BGCs.ResultsThe proposed reinforcement learning method aims to improve candidate BGCs obtained with state-of-the-art tools. It was evaluated on candidate BGCs obtained for two fungal genomes, Aspergillus niger and Aspergillus nidulans. The results highlight an improvement of the gene precision by above 15% for TOUCAN, fungiSMASH and DeepBGC; and cluster precision by above 25% for fungiSMASH and DeepBCG, allowing these tools to obtain almost perfect precision in cluster prediction. This can pave the way of optimizing current prediction of candidate BGCs in fungi, while minimizing the curation effort required by domain experts.Availability and implementation https://github.com/bioinfoUQAM/RL-bgc-components.Supplementary information Supplementary data are available at Bioinformatics online.

Genome sequences of Rhizopogon roseolus, Mariannaea elegans, Myrothecium verrucaria, and Sphaerostilbella broomeana and the identification of biosynthetic gene clusters for fungal peptide natural products.

In recent years, a variety of fungal cyclic peptides with interesting bioactivities have been discovered. For many of these peptides, the biosynthetic pathways are unknown and their elucidation often holds surprises. The cyclic and backbone N-methylated omphalotins from Omphalotus olearius were recently shown to constitute a novel class (borosins) of ribosomally synthesized and posttranslationally modified peptides, members of which are produced by many fungi, including species of the genus Rhizopogon. Other recently discovered fungal peptide macrocycles include the mariannamides from Mariannaea elegans and the backbone N-methylated verrucamides and broomeanamides from Myrothecium verrucaria and Sphaerostilbella broomeana, respectively. Here, we present draft genome sequences of four fungal species Rhizopogon roseolus, Mariannaea elegans, Myrothecium verrucaria, and Sphaerostilbella broomeana. We screened these genomes for precursor proteins or gene clusters involved in the mariannamide, verrucamide, and broomeanamide biosynthesis including a general screen for borosin-producing precursor proteins. While our genomic screen for potential ribosomally synthesized and posttranslationally modified peptide precursor proteins of mariannamides, verrucamides, broomeanamides, and borosins remained unsuccessful, antiSMASH predicted nonribosomal peptide synthase gene clusters that may be responsible for the biosynthesis of mariannamides, verrucamides, and broomeanamides. In M. verrucaria, our antiSMASH search led to a putative NRPS gene cluster with a predicted peptide product of 20 amino acids, including multiple nonproteinogenic isovalines. This cluster likely encodes a member of the peptaibols, an antimicrobial class of peptides previously isolated primarily from the Genus Trichoderma. The nonribosomal peptide synthase gene clusters discovered in our screenings are promising candidates for future research.

Generating lineage-resolved, complete metagenome-assembled genomes from complex microbial communities.

Microbial communities might include distinct lineages of closely related organisms that complicate metagenomic assembly and prevent the generation of complete metagenome-assembled genomes (MAGs). Here we show that deep sequencing using long (HiFi) reads combined with Hi-C binning can address this challenge even for complex microbial communities. Using existing methods, we sequenced the sheep fecal metagenome and identified 428 MAGs with more than 90% completeness, including 44 MAGs in single circular contigs. To resolve closely related strains (lineages), we developed MAGPhase, which separates lineages of related organisms by discriminating variant haplotypes across hundreds of kilobases of genomic sequence. MAGPhase identified 220 lineage-resolved MAGs in our dataset. The ability to resolve closely related microbes in complex microbial communities improves the identification of biosynthetic gene clusters and the precision of assigning mobile genetic elements to host genomes. We identified 1,400 complete and 350 partial biosynthetic gene clusters, most of which are novel, as well as 424 (298) potential host-viral (host-plasmid) associations using Hi-C data.

Research progress on plant biosynthetic gene cluster

<p indent=0mm>Plants produce a wide variety of structurally and functionally diverse metabolites, many of which play an indispensable role in plant growth and development and in the response to abiotic and biotic stresses. In recent years, plant biosynthetic gene clustering has attracted extensive attention from scientists, and the rapid development of multi-omics technologies such as genome, transcriptome and metabolome makes it possible to efficiently mine plant metabolic gene clusters. Further, the studies of plant biosynthetic gene clusters will provide new insights to explore and utilize the diversity of plant secondary metabolites. Here, we not only systematically summarize the structural characteristics and regulatory models of plant biosynthetic gene clusters, but also review the application of new technologies in the discovery and identification of biosynthetic gene clusters.

New keys to unlock the treasure trove of microbial natural products.

This month’s Genome Watch highlights how recent advances in computational identification of biosynthetic gene clusters (BGCs), and in their experimental manipulation, are opening new avenues to access novel secondary metabolites.

Combining OSMAC Approach and Untargeted Metabolomics for the Identification of New Glycolipids with Potent Antiviral Activity Produced by a Marine Rhodococcus.

Natural products of microbial origin have inspired most of the commercial pharmaceuticals, especially those from Actinobacteria. However, the redundancy of molecules in the discovery process represents a serious issue. The untargeted approach, One Strain Many Compounds (OSMAC), is one of the most promising strategies to induce the expression of silent genes, especially when combined with genome mining and advanced metabolomics analysis. In this work, the whole genome of the marine isolate Rhodococcus sp. I2R was sequenced and analyzed by antiSMASH for the identification of biosynthetic gene clusters. The strain was cultivated in 22 different growth media and the generated extracts were subjected to metabolomic analysis and functional screening. Notably, only a single growth condition induced the production of unique compounds, which were partially purified and structurally characterized by liquid chromatography high-resolution tandem mass spectrometry (LC-HRMS/MS). This strategy led to identifying a bioactive fraction containing >30 new glycolipids holding unusual functional groups. The active fraction showed a potent antiviral effect against enveloped viruses, such as herpes simplex virus and human coronaviruses, and high antiproliferative activity in PC3 prostate cancer cell line. The identified compounds belong to the biosurfactants class, amphiphilic molecules, which play a crucial role in the biotech and biomedical industry.

A chromosome-level Amaranthus cruentus genome assembly highlights gene family evolution and biosynthetic gene clusters that may underpin the nutritional value of this traditional crop.

Traditional crops have historically provided accessible and affordable nutrition to millions of rural dwellers but have been neglected, with most modern agricultural systems over-reliant on a small number of internationally traded crops. Traditional crops are typically well-adapted to local agro-ecological conditions and many are nutrient-dense. They can play a vital role in local food systems through enhanced nutrition (particularly where diets are dominated by starch crops), food security and livelihoods for smallholder farmers, and a climate-resilient and biodiverse agriculture. Using short-read, long-read and phased sequencing technologies, we generated a high-quality chromosome-level genome assembly for Amaranthus cruentus, an under-researched crop with micronutrient- and protein-rich leaves and gluten-free seed, but lacking improved varieties, with respect to productivity and quality traits. The 370.9Mb genome demonstrates a shared whole genome duplication with a related species, Amaranthus hypochondriacus. Comparative genome analysis indicates chromosomal loss and fusion events following genome duplication that are common to both species, as well as fission of chromosome 2 in A. cruentus alone, giving rise to a haploid chromosome number of 17 (versus 16 in A. hypochondriacus). Genomic features potentially underlying the nutritional value of this crop include two A. cruentus-specific genes with a likely role in phytic acid synthesis (an anti-nutrient), expansion of ion transporter gene families, and identification of biosynthetic gene clusters conserved within the amaranth lineage. The A. cruentus genome assembly will underpin much-needed research and global breeding efforts to develop improved varieties for economically viable cultivation and realization of the benefits to global nutrition security and agrobiodiversity.

Leveraging Microbial Genomes and Genomic Context for Chemical Discovery.

ConspectusThe genomic era has dramatically changed how we discover and investigate microbial biochemistry. In particular, the exponential expansion in the number of sequenced microbial genomes provides investigators with a vast wealth of sequence data to exploit for the discovery of biochemical functions and mechanisms, as well as novel enzymes and metabolites. In contrast to early biochemical work, which was largely characterized by “forward” approaches that proceed from biomass to enzyme to gene, the availability of genome sequences enables the discovery of new microbial metabolic activities, enzymes, and metabolites by “reverse” approaches that originate with genetic information or by approaches that incorporate features of both forward and reverse methodologies. In the genomic era, the canonical organization of microbial genomes into gene clusters presents a singular opportunity for the utilization of genomic data. Specifically, genomic context (information gleaned from the genes surrounding a gene of interest in the chromosome) is a powerful tool for chemical discovery in microbial systems because of the functional and/or physiological relationship that usually exists between genes found within a gene cluster. This means that the investigator can use this inferred link to generate hypotheses about the functions of individual genes in the cluster or even the function of the entire cluster itself. Here, we discuss how analysis of genomic context in combination with a mechanistic understanding of enzymes can facilitate numerous facets of microbial biochemical research including the identification of biosynthetic gene clusters, the discovery of important and novel enzymes, the elucidation of natural product structures, and the identification of new metabolic pathways. We highlight work from our laboratory using genomic context to discover and study biosynthetic pathways that produce natural products, including the cylindrocyclophanes, nitrogen–nitrogen bond-containing metabolites, and the gut microbial genotoxin colibactin. Although use of genomic context is most commonly associated with studies of natural product biosynthesis, we also show that it can be applied to the study of primary metabolism. We illustrate this with examples from our work studying the members of the glycyl radical enzyme superfamily involved in choline and 4-hydroxyproline degradation in the human gut. Looking forward, we envision increased opportunities to use such information, with the combination of biochemical knowledge and computational tools poised to fuel a new revolution in our ability to connect genes and their biochemical functions. In particular, we note a need for methods that computationally formalize the functional association between genes when such associations are not obvious from manual gene annotations. Such tools will drastically augment the feasibility and scope of gene cluster analysis and accelerate the discovery of new microbial enzymes, metabolites, and metabolic processes.

The Nonribosomal Peptide Valinomycin: From Discovery to Bioactivity and Biosynthesis.

Valinomycin is a nonribosomal peptide that was discovered from Streptomyces in 1955. Over the past more than six decades, it has received continuous attention due to its special chemical structure and broad biological activities. Although many research papers have been published on valinomycin, there has not yet been a comprehensive review that summarizes the diverse studies ranging from structural characterization, biogenesis, and bioactivity to the identification of biosynthetic gene clusters and heterologous biosynthesis. In this review, we aim to provide an overview of valinomycin to address this gap, covering from 1955 to 2020. First, we introduce the chemical structure of valinomycin together with its chemical properties. Then, we summarize the broad spectrum of bioactivities of valinomycin. Finally, we describe the valinomycin biosynthetic gene cluster and reconstituted biosynthesis of valinomycin. With that, we discuss possible opportunities for the future research and development of valinomycin.

Taxogenomic and Comparative Genomic Analysis of the Genus Saccharomonospora Focused on the Identification of Biosynthetic Clusters PKS and NRPS.

Actinobacteria are prokaryotes with a large biotechnological interest due to their ability to produce secondary metabolites, produced by two main biosynthetic gene clusters (BGCs): polyketide synthase (PKS) and non-ribosomal peptide synthetase (NRPS). Most studies on bioactive products have been carried out on actinobacteria isolated from soil, freshwater or marine habitats, while very few have been focused on halophilic actinobacteria isolated from extreme environments. In this study we have carried out a comparative genomic analysis of the actinobacterial genus Saccharomonospora, which includes species isolated from soils, lake sediments, marine or hypersaline habitats. A total of 19 genome sequences of members of Saccharomonospora were retrieved and analyzed. We compared the 16S rRNA gene-based phylogeny of this genus with evolutionary relationships inferred using a phylogenomic approach obtaining almost identical topologies between both strategies. This method allowed us to unequivocally assign strains into species and to identify some taxonomic relationships that need to be revised. Our study supports a recent speciation event occurring between Saccharomonospora halophila and Saccharomonospora iraqiensis. Concerning the identification of BGCs, a total of 18 different types of BGCs were detected in the analyzed genomes of Saccharomonospora, including PKS, NRPS and hybrid clusters which might be able to synthetize 40 different putative products. In comparison to other genera of the Actinobacteria, members of the genus Saccharomonospora showed a high degree of novelty and diversity of BGCs.

Discovery, Bioactivity Evaluation, Biosynthetic Gene Cluster Identification, and Heterologous Expression of Novel Albofungin Derivatives

The crude extract of Streptomyces chrestomyceticus exhibited strong and broad activities against most “ESKAPE pathogens.” We conducted a comprehensive chemical investigation for secondary metabolites from the S. chrestomyceticus strain and identified two novel albofungin (alb) derivatives, i.e., albofungins A (1) and B (2), along with two known compounds, i.e., albofungin (3) and chloroalbofungin (4). The chemical structures of the novel compounds were elucidated using HRMS, 1D and 2D NMR, and electronic circular dichroism spectroscopy. The draft genome of S. chrestomyceticus was sequenced, and a 72 kb albofungin (alb) gene cluster with 72 open reading frames encoding type II polyketide synthases (PKSs), regulators, and transporters, and tailoring enzymes were identified using bioinformatics analysis. The alb gene cluster was confirmed using the heterologous expression in Streptomyces coelicolor, which successfully produced the compounds 3 and 4. Furthermore, compounds 1–4 displayed remarkable activities against Gram-positive bacteria and antitumor activities toward various cancer cells. Notably, compounds 1 and 3 showed potent activities against Gram-negative pathogenic bacteria. The terminal deoxynucleotidyl transferase (dUTP) nick-end labeling and flow cytometry analysis verified that compound 1 inhibited cancer cell proliferation by inducing cellular apoptosis. These results indicated that albofungins might be potential candidates for the development of antibiotics and antitumor drugs.