Mining genomes to illuminate the specialized chemistry of life

A systematic comparison of natural product potential, with an emphasis on RiPPs, by mining of bacteria of three large ecosystems

The implementation of several global microbiome studies has yielded extensive insights into the biosynthetic potential of natural microbial communities. However, studies on the distribution of several classes of ribosomally synthesized and post-translationally modified peptides (RiPPs), non-ribosomal peptides (NRPs) and polyketides (PKs) in different large microbial ecosystems have been very limited. Here, we collected a large set of metagenome-assembled bacterial genomes from marine, freshwater and terrestrial ecosystems to investigate the biosynthetic potential of these bacteria. We demonstrate the utility of public dataset collections for revealing the different secondary metabolite biosynthetic potentials among these different living environments. We show that there is a higher occurrence of RiPPs in terrestrial systems, while in marine systems, we found relatively more terpene-, NRP-, and PK encoding gene clusters. Among the many new biosynthetic gene clusters (BGCs) identified, we analyzed various Nif-11-like and nitrile hydratase leader peptide (NHLP) containing gene clusters that would merit further study, including promising products, such as mersacidin-, LAP- and proteusin analogs. This research highlights the significance of public datasets in elucidating the biosynthetic potential of microbes in different living environments and underscores the wide bioengineering opportunities within the RiPP family.

The occurrence and bioactivity of tetrahydronaphthoquinoline-diones (THNQ-dione)

Natural products have been important in the discovery of new drugs, but their use is limited due to issues with accessibility and synthesis. Tetrahydronaphthoquinoline-dione (THNQ-dione) is a key structural feature found in several natural and synthetic compounds that exhibit notable biological properties. The unique properties of THNQ-diones can be attributed to the fusion of tetrahydroquinoline and anthraquinone moieties. These alkaloids are synthesised through various biosynthetic pathways, leading to diverse structures and bioactivities. Despite their significance, THNQ-diones have not been extensively covered in the review literature, highlighting the importance of this article in discussing their natural occurrence and biological activities. This article explores the distribution of THNQ-dione alkaloids in different organisms and their potential as a source of novel bioactive natural products.

Rule-based omics mining reveals antimicrobial macrocyclic peptides against drug-resistant clinical isolates

Antimicrobial resistance remains a significant global threat, driving up mortality rates worldwide. Ribosomally synthesized and post-translationally modified peptides have emerged as a promising source of novel peptide antibiotics due to their diverse chemical structures. Here, we report the discovery of new aminovinyl-(methyl)cysteine (Avi(Me)Cys)-containing peptide antibiotics through a synergistic approach combining biosynthetic rule-based omics mining and heterologous expression. We first bioinformatically identify 1172 RiPP biosynthetic gene clusters (BGCs) responsible for Avi(Me)Cys-containing peptides formation from a vast pool of over 50,000 bacterial genomes. Subsequently, we successfully establish the connection between three identified BGCs and the biosynthesis of five peptide antibiotics via biosynthetic rule-guided metabolic analysis. Notably, we discover a class V lanthipeptide, massatide A, which displays excellent activity against gram-positive pathogens, including drug-resistant clinical isolates like linezolid-resistant S. aureus and methicillin-resistant S. aureus, with a minimum inhibitory concentration of 0.25 μg/mL. The remarkable performance of massatide A in an animal infection model, coupled with a relatively low risk of resistance and favorable safety profile, positions it as a promising candidate for antibiotic development. Our study highlights the potential of Avi(Me)Cys-containing peptides in expanding the arsenal of antibiotics against multi-drug-resistant bacteria, offering promising drug leads in the ongoing battle against infectious diseases.

Unlocking plant bioactive pathways: omics data harnessing and machine learning assisting

Plant bioactives hold immense potential in the medicine and food industry. The recent advancements in omics applied in deciphering specialized metabolic pathways underscore the importance of high-quality genome releases and the wealth of data in metabolomics and transcriptomics. While harnessing data, whether integrated or standalone, has proven successful in unveiling plant natural product (PNP) biosynthetic pathways, the democratization of machine learning in biology opens exciting new opportunities for enhancing the exploration of these pathways. This review highlights the recent breakthroughs in disrupting plant-specialized biosynthetic pathways through the utilization of omics data harnessing and machine learning techniques.

Natural product discovery in soil actinomycetes: unlocking their potential within an ecological context

Natural products (NPs) produced by bacteria, particularly soil actinomycetes, often possess diverse bioactivities and play a crucial role in human health, agriculture, and biotechnology. Soil actinomycete genomes contain a vast number of predicted biosynthetic gene clusters (BGCs) yet to be exploited. Understanding the factors governing NP production in an ecological context and activating cryptic and silent BGCs in soil actinomycetes will provide researchers with a wealth of molecules with potential novel applications. Here, we highlight recent advances in NP discovery strategies employing ecology-inspired approaches and discuss the importance of understanding the environmental signals responsible for activation of NP production, particularly in a soil microbial community context, as well as the challenges that remain.

Decoding the molecular interplay in the central dogma: An overview of mass spectrometry-based methods to investigate protein-metabolite interactions

With the emergence of next-generation nucleotide sequencing and mass spectrometry-based proteomics and metabolomics tools, we have comprehensive and scalable methods to analyze the genes, transcripts, proteins, and metabolites of a multitude of biological systems. Despite the fascinating new molecular insights at the genome, transcriptome, proteome and metabolome scale, we are still far from fully understanding cellular organization, cell cycles and biology at the molecular level. Significant advances in sensitivity and depth for both sequencing as well as mass spectrometry-based methods allow the analysis at the single cell and single molecule level. At the same time, new tools are emerging that enable the investigation of molecular interactions throughout the central dogma of molecular biology. In this review, we provide an overview of established and recently developed mass spectrometry-based tools to probe metabolite-protein interactions-from individual interaction pairs to interactions at the proteome-metabolome scale.

Exploring the roles of ribosomal peptides in prokaryote-phage interactions through deep learning-enabled metagenome mining

BACKGROUND

Microbial secondary metabolites play a crucial role in the intricate interactions within the natural environment. Among these metabolites, ribosomally synthesized and post-translationally modified peptides (RiPPs) are becoming a promising source of therapeutic agents due to their structural diversity and functional versatility. However, their biosynthetic capacity and ecological functions remain largely underexplored.

RESULTS

Here, we aim to explore the biosynthetic profile of RiPPs and their potential roles in the interactions between microbes and viruses in the ocean, which encompasses a vast diversity of unique biomes that are rich in interactions and remains chemically underexplored. We first developed TrRiPP to identify RiPPs from ocean metagenomes, a deep learning method that detects RiPP precursors in a hallmark gene-independent manner to overcome the limitations of classic methods in processing highly fragmented metagenomic data. Applying this method to metagenomes from the global ocean microbiome, we uncover a diverse array of previously uncharacterized putative RiPP families with great novelty and diversity. Through correlation analysis based on metatranscriptomic data, we observed a high prevalence of antiphage defense-related and phage-related protein families that were co-expressed with RiPP families. Based on this putative association between RiPPs and phage infection, we constructed an Ocean Virus Database (OVD) and established a RiPP-involving host-phage interaction network through host prediction and co-expression analysis, revealing complex connectivities linking RiPP-encoding prokaryotes, RiPP families, viral protein families, and phages. These findings highlight the potential of RiPP families involved in prokaryote-phage interactions and coevolution, providing insights into their ecological functions in the ocean microbiome.

CONCLUSIONS

This study provides a systematic investigation of the biosynthetic potential of RiPPs from the ocean microbiome at a global scale, shedding light on the essential insights into the ecological functions of RiPPs in prokaryote-phage interactions through the integration of deep learning approaches, metatranscriptomic data, and host-phage connectivity. This study serves as a valuable example of exploring the ecological functions of bacterial secondary metabolites, particularly their associations with unexplored microbial interactions. Video Abstract.

The Impact of Metagenomics on Biocatalysis

In the ever-growing demand for sustainable ways to produce high-value small molecules, biocatalysis has come to the forefront of greener routes to these chemicals. As such, the need to constantly find and optimise suitable biocatalysts for specific transformations has never been greater. Metagenome mining has been shown to rapidly expand the toolkit of promiscuous enzymes needed for new transformations, without requiring protein engineering steps. If protein engineering is needed, the metagenomic candidate can often provide a better starting point for engineering than a previously discovered enzyme on the open database or from literature, for instance. In this review, we highlight where metagenomics has made substantial impact on the area of biocatalysis in recent years. We review the discovery of enzymes in previously unexplored or 'hidden' sequence space, leading to the characterisation of enzymes with enhanced properties that originate from natural selection pressures in native environments.

Direct α-Hydroxy Acid Loading onto a Bacterial Thiotemplate Assembly Line via a Multienzyme Gateway

Various nonribosomal peptide synthetases (NRPSs) create structural and functional diversity by incorporating α-hydroxy acids into peptide backbones. Trigonic acid, an unusual cyclopropanol-substituted hydroxy acid, is the source of the molecular warhead of malleicyprol, a critical virulence factor of human and animal pathogens of the Burkholderia pseudomallei (BP) group. The process of selecting and loading this building block remained enigmatic as the NRPS module designated for this task is incomplete. Using a combination of bioinformatics, mutational analyses, targeted metabolomics, and in vitro biochemical assays, we show that two trans-acting enzymes are required to load this central building block onto the modular assembly line. An adenylation-thiolation didomain enzyme (BurJ) activates trigonic acid, followed by the translocation of the enzyme-bound α-hydroxy acid thioester by an FkbH-like protein with a mutated phosphatase domain (BurH). This specialized gateway is the first reported direct loading of an α-hydroxy acid onto a bona fide NRPS module in bacteria and expands the synthetic biology toolbox for the site-specific incorporation of non-canonical building blocks. Moreover, insight into the biochemical basis of virulence factor biosynthesis can provide a foundation for developing enzyme inhibitors as anti-virulence therapeutics against BP pathogen infections.

Enzymatic synthesis of mono- and trifluorinated alanine enantiomers expands the scope of fluorine biocatalysis

Fluorinated amino acids serve as an entry point for establishing new-to-Nature chemistries in biological systems, and novel methods are needed for the selective synthesis of these building blocks. In this study, we focused on the enzymatic synthesis of fluorinated alanine enantiomers to expand fluorine biocatalysis. The alanine dehydrogenase from Vibrio proteolyticus and the diaminopimelate dehydrogenase from Symbiobacterium thermophilum were selected for in vitro production of (R)-3-fluoroalanine and (S)-3-fluoroalanine, respectively, using 3-fluoropyruvate as the substrate. Additionally, we discovered that an alanine racemase from Streptomyces lavendulae, originally selected for setting an alternative enzymatic cascade leading to the production of these non-canonical amino acids, had an unprecedented catalytic efficiency in β-elimination of fluorine from the monosubstituted fluoroalanine. The in vitro enzymatic cascade based on the dehydrogenases of V. proteolyticus and S. thermophilum included a cofactor recycling system, whereby a formate dehydrogenase from Pseudomonas sp. 101 (either native or engineered) coupled formate oxidation to NAD(P)H formation. Under these conditions, the reaction yields for (R)-3-fluoroalanine and (S)-3-fluoroalanine reached >85% on the fluorinated substrate and proceeded with complete enantiomeric excess. The selected dehydrogenases also catalyzed the conversion of trifluoropyruvate into trifluorinated alanine as a first-case example of fluorine biocatalysis with amino acids carrying a trifluoromethyl group.

A vast repertoire of secondary metabolites potentially influences community dynamics and biogeochemical processes in cold seeps

In deep-sea cold seeps, microbial communities thrive on the geological seepage of hydrocarbons and inorganic compounds, differing from photosynthetically driven ecosystems. However, their biosynthetic capabilities remain largely unexplored. Here, we analyzed 81 metagenomes, 33 metatranscriptomes, and 7 metabolomes derived from nine different cold seep areas to investigate their secondary metabolites. Cold seep microbiomes encode diverse and abundant biosynthetic gene clusters (BGCs). Most BGCs are affiliated with understudied bacteria and archaea, including key mediators of methane and sulfur cycling. The BGCs encode diverse antimicrobial compounds that potentially shape community dynamics and various metabolites predicted to influence biogeochemical cycling. BGCs from key players are widely distributed and highly expressed, with their abundance and expression levels varying with sediment depth. Sediment metabolomics reveals unique natural products, highlighting uncharted chemical potential and confirming BGC activity in these sediments. Overall, these results demonstrate that cold seep sediments serve as a reservoir of hidden natural products and sheds light on microbial adaptation in chemosynthetically driven ecosystems.

The shikimate pathway: gateway to metabolic diversity

Covering: 1997 to 2023The shikimate pathway is the metabolic process responsible for the biosynthesis of the aromatic amino acids phenylalanine, tyrosine, and tryptophan. Seven metabolic steps convert phosphoenolpyruvate (PEP) and erythrose 4-phosphate (E4P) into shikimate and ultimately chorismate, which serves as the branch point for dedicated aromatic amino acid biosynthesis. Bacteria, fungi, algae, and plants (yet not animals) biosynthesize chorismate and exploit its intermediates in their specialized metabolism. This review highlights the metabolic diversity derived from intermediates of the shikimate pathway along the seven steps from PEP and E4P to chorismate, as well as additional sections on compounds derived from prephenate, anthranilate and the synonymous aminoshikimate pathway. We discuss the genomic basis and biochemical support leading to shikimate-derived antibiotics, lipids, pigments, cofactors, and other metabolites across the tree of life.

Diversity and distribution of biosynthetic gene clusters in agricultural soil microbiomes

Bacterial secondary metabolites serve as an important source of molecules for drug discovery. They also play an important function in mediating the interactions of microbial producers with their living environment and surrounding organisms. However, little is known about the genetic novelty, distribution, and community-level impacts of soil bacterial biosynthetic potential on a large geographic scale. Here, we constructed the first catalog of 11,149 biosynthetic gene clusters (BGCs) from agricultural soils across China and unearthed hidden biosynthetic potential for new natural product discovery from the not-yet-cultivated soil bacteria. Notably, we revealed soil pH as the strongest environmental driver of BGC biogeography and predicted that soil acidification and global climate change could damage the biosynthetic potential of the soil microbiome. The co-occurrence network of bacterial genomes revealed two BGC-rich species, i.e., Nocardia niigatensis from Actinobacteriota and PSRF01 from Acidobacteriota, as the module hub and connector, respectively, indicating their keystone positions in the soil microbial communities. We also uncovered a dominant role of BGC-inferred biotic interactions over environmental drivers in structuring the soil microbiome. Overall, this study achieved novel insights into the BGC landscape in agricultural soils of China, substantially expanding our understanding of the diversity and novelty of bacterial secondary metabolism and the potential role of secondary metabolites in microbiota assembly.IMPORTANCEBacterial secondary metabolites not only serve as the foundation for numerous therapeutics (e.g., antibiotics and anticancer drugs), but they also play critical ecological roles in mediating microbial interactions (e.g., competition and communication). However, our knowledge of bacterial secondary metabolism is limited to only a small fraction of cultured strains, thus restricting our comprehensive understanding of their diversity, novelty, and potential ecological roles in soil ecosystems. Here, we used culture-independent metagenomics to explore biosynthetic potentials in agricultural soils of China. Our analyses revealed a high degree of genetic diversity and novelty within biosynthetic gene clusters in agricultural soil environments, offering valuable insights for biochemists seeking to synthesize novel bioactive products. Furthermore, we uncovered the pivotal role of BGC-rich species in microbial communities and the significant relationship between BGC richness and microbial phylogenetic turnover. This information emphasizes the importance of biosynthetic potential in the assembly of microbial communities.

The structure of the monobactam-producing thioesterase domain of SulM forms a unique complex with the upstream carrier protein domain

Nonribosomal peptide synthetases (NRPSs) are responsible for the production of important biologically active peptides. The large, multidomain NRPSs operate through an assembly line strategy in which the growing peptide is tethered to carrier domains that deliver the intermediates to neighboring catalytic domains. While most NRPS domains catalyze standard chemistry of amino acid activation, peptide bond formation and product release, some canonical NRPS catalytic domains promote unexpected chemistry. The paradigm monobactam antibiotic sulfazecin is produced through the activity of a terminal thioesterase domain that catalyzes an unusual β-lactam forming reaction in which the nitrogen of the C-terminal N-sulfo-2,3-diaminopropionate residue attacks its thioester tether to release the β-lactam product. We have determined the structure of the thioesterase domain as both a free-standing domain and a didomain complex with the upstream holo peptidyl-carrier domain. The structure illustrates a constrained active site that orients the substrate properly for β-lactam formation. In this regard, the structure is similar to the β-lactone forming thioesterase domain responsible for the production of obafluorin. Analysis of the structure identifies features that are responsible for this four-membered ring closure and enable bioinformatic analysis to identify additional, uncharacterized β-lactam-forming biosynthetic gene clusters by genome mining.

Tryptophan-Centric Bioinformatics Identifies New Lasso Peptide Modifications

Lasso peptides are a class of ribosomally synthesized and post-translationally modified peptides (RiPPs) defined by a macrolactam linkage between the N-terminus and the side chain of an internal aspartic acid or glutamic acid residue. Instead of adopting a branched-cyclic conformation, lasso peptides are "threaded", with the C-terminal tail passing through the macrocycle to present a kinetically trapped rotaxane conformation. The availability of enhanced bioinformatics methods has led to a significant increase in the number of secondary modifications found on lasso peptides. To uncover new ancillary modifications in a targeted manner, a bioinformatic strategy was developed to discover lasso peptides with modifications to tryptophan. This effort identified numerous putative lasso peptide biosynthetic gene clusters with core regions of the precursor peptides enriched in tryptophan. Parsing of these tryptophan (Trp)-rich biosynthetic gene clusters uncovered several putative ancillary modifying enzymes, including halogenases and dimethylallyltransferases expected to act upon Trp. Characterization of two gene products yielded a lasso peptide with two 5-Cl-Trp modifications (chlorolassin) and another bearing 5-dimethylallyl-Trp and 2,3-didehydro-Tyr modifications (wygwalassin). Bioinformatic analysis of the requisite halogenase and dimethylallyltransferase revealed numerous other putative Trp-modified lasso peptides that remain uncharacterized. We anticipate that the Trp-centric strategy reported herein may be useful in discovering ancillary modifications for other RiPP classes and, more generally, guide the functional prediction of enzymes that act on specific amino acids.

Recent advances in biocatalytic C-N bond-forming reactions

Molecules containing C-N bonds are of paramount importance in a diverse array of organic-based materials, natural products, pharmaceutical compounds, and agricultural chemicals. Biocatalytic C-N bond-forming reactions represent powerful strategies for producing these valuable targets, and their significance in the field of synthetic chemistry has steadily increased over the past decade. In this review, we provide a concise overview of recent advancements in the development of C-N bond-forming enzymes, with a particular emphasis on the inherent chemistry involved in these enzymatic processes. Overall, these enzymatic systems have proven their potential in addressing long-standing challenges in traditional small-molecule catalysis.

Discovery and engineering of ribosomally synthesized and post-translationally modified peptide (RiPP) natural products

Ribosomally synthesized and post-translationally modified peptides (RiPPs) represent a diverse superfamily of natural products with immense potential for drug development. This review provides a concise overview of the recent advances in the discovery of RiPP natural products, focusing on rational strategies such as bioactivity guided screening, enzyme or precursor-based genome mining, and biosynthetic engineering. The challenges associated with activating silent biosynthetic gene clusters and the development of elaborate catalytic systems are also discussed. The logical frameworks emerging from these research studies offer valuable insights into RiPP biosynthesis and engineering, paving the way for broader pharmaceutic applications of these peptide natural products.

Giant polyketide synthase enzymes biosynthesize a giant marine polyether biotoxin

Prymnesium parvum are harmful haptophyte algae that cause massive environmental fish-kills. Their polyketide polyether toxins, the prymnesins, are amongst the largest nonpolymeric compounds in nature, alongside structurally-related health-impacting "red-tide" polyether toxins whose biosynthetic origins have been an enigma for over 40 years. Here we report the 'PKZILLAs', massive P. parvum polyketide synthase (PKS) genes, whose existence and challenging genomic structure evaded prior detection. PKZILLA-1 and -2 encode giant protein products of 4.7 and 3.2 MDa with 140 and 99 enzyme domains, exceeding the largest known protein titin and all other known PKS systems. Their predicted polyene product matches the proposed pre-prymnesin precursor of the 90-carbon-backbone A-type prymnesins. This discovery establishes a model system for microalgal polyether biosynthesis and expands expectations of genetic and enzymatic size limits in biology.

Antimicrobial resistance crisis: could artificial intelligence be the solution?

Antimicrobial resistance is a global public health threat, and the World Health Organization (WHO) has announced a priority list of the most threatening pathogens against which novel antibiotics need to be developed. The discovery and introduction of novel antibiotics are time-consuming and expensive. According to WHO's report of antibacterial agents in clinical development, only 18 novel antibiotics have been approved since 2014. Therefore, novel antibiotics are critically needed. Artificial intelligence (AI) has been rapidly applied to drug development since its recent technical breakthrough and has dramatically improved the efficiency of the discovery of novel antibiotics. Here, we first summarized recently marketed novel antibiotics, and antibiotic candidates in clinical development. In addition, we systematically reviewed the involvement of AI in antibacterial drug development and utilization, including small molecules, antimicrobial peptides, phage therapy, essential oils, as well as resistance mechanism prediction, and antibiotic stewardship.

ABC-HuMi: the Atlas of Biosynthetic Gene Clusters in the Human Microbiome

The human microbiome has emerged as a rich source of diverse and bioactive natural products, harboring immense potential for therapeutic applications. To facilitate systematic exploration and analysis of its biosynthetic landscape, we present ABC-HuMi: the Atlas of Biosynthetic Gene Clusters (BGCs) in the Human Microbiome. ABC-HuMi integrates data from major human microbiome sequence databases and provides an expansive repository of BGCs compared to the limited coverage offered by existing resources. Employing state-of-the-art BGC prediction and analysis tools, our database ensures accurate annotation and enhanced prediction capabilities. ABC-HuMi empowers researchers with advanced browsing, filtering, and search functionality, enabling efficient exploration of the resource. At present, ABC-HuMi boasts a catalog of 19 218 representative BGCs derived from the human gut, oral, skin, respiratory and urogenital systems. By capturing the intricate biosynthetic potential across diverse human body sites, our database fosters profound insights into the molecular repertoire encoded within the human microbiome and offers a comprehensive resource for the discovery and characterization of novel bioactive compounds. The database is freely accessible at https://www.ccb.uni-saarland.de/abc_humi/.

The genomes of Dahlia pinnata, Cosmos bipinnatus, and Bidens alba in tribe Coreopsideae provide insights into polyploid evolution and inulin biosynthesis

BACKGROUND

The Coreopsideae tribe, a subset of the Asteraceae family, encompasses economically vital genera like Dahlia, Cosmos, and Bidens, which are widely employed in medicine, horticulture, ecology, and food applications. Nevertheless, the lack of reference genomes hinders evolutionary and biological investigations in this tribe.

RESULTS

Here, we present 3 haplotype-resolved chromosome-level reference genomes of the tribe Coreopsideae, including 2 popular flowering plants (Dahlia pinnata and Cosmos bipinnatus) and 1 invasive weed plant (Bidens alba), with assembled genome sizes 3.93 G, 1.02 G, and 1.87 G, respectively. We found that Gypsy transposable elements contribute mostly to the larger genome size of D. pinnata, and multiple chromosome rearrangements have occurred in tribe Coreopsideae. Besides the shared whole-genome duplication (WGD-2) in the Heliantheae alliance, our analyses showed that D. pinnata and B. alba each underwent an independent recent WGD-3 event: in D. pinnata, it is more likely to be a self-WGD, while in B. alba, it is from the hybridization of 2 ancestor species. Further, we identified key genes in the inulin metabolic pathway and found that the pseudogenization of 1-FEH1 and 1-FEH2 genes in D. pinnata and the deletion of 3 key residues of 1-FFT proteins in C. bipinnatus and B. alba may probably explain why D. pinnata produces much more inulin than the other 2 plants.

CONCLUSIONS

Collectively, the genomic resources for the Coreopsideae tribe will promote phylogenomics in Asteraceae plants, facilitate ornamental molecular breeding improvements and inulin production, and help prevent invasive weeds.

The undiscovered biosynthetic potential of the Greenland Ice Sheet microbiome

The Greenland Ice Sheet is a biome which is mainly microbially driven. Several different niches can be found within the glacial biome for those microbes able to withstand the harsh conditions, e.g., low temperatures, low nutrient conditions, high UV radiation in summer, and contrasting long and dark winters. Eukaryotic algae can form blooms during the summer on the ice surface, interacting with communities of bacteria, fungi, and viruses. Cryoconite holes and snow are also habitats with their own microbial community. Nevertheless, the microbiome of supraglacial habitats remains poorly studied, leading to a lack of representative genomes from these environments. Under-investigated extremophiles, like those living on the Greenland Ice Sheet, may provide an untapped reservoir of chemical diversity that is yet to be discovered. In this study, an inventory of the biosynthetic potential of these organisms is made, through cataloging the presence of biosynthetic gene clusters in their genomes. There were 133 high-quality metagenome-assembled genomes (MAGs) and 28 whole genomes of bacteria obtained from samples of the ice sheet surface, cryoconite, biofilm, and snow using culturing-dependent and -independent approaches. AntiSMASH and BiG-SCAPE were used to mine these genomes and subsequently analyze the resulting predicted gene clusters. Extensive sets of predicted Biosynthetic Gene Clusters (BGCs) were collected from the genome collection, with limited overlap between isolates and MAGs. Additionally, little overlap was found in the biosynthetic potential among different environments, suggesting specialization of organisms in specific habitats. The median number of BGCs per genome was significantly higher for the isolates compared to the MAGs. The most talented producers were found among Proteobacteria. We found evidence for the capacity of these microbes to produce antimicrobials, carotenoid pigments, siderophores, and osmoprotectants, indicating potential survival mechanisms to cope with extreme conditions. The majority of identified BGCs, including those in the most prevalent gene cluster families, have unknown functions, presenting a substantial potential for bioprospecting. This study underscores the diverse biosynthetic potential in Greenland Ice Sheet genomes, revealing insights into survival strategies and highlighting the need for further exploration and characterization of these untapped resources.

Generating high-quality plant and fish reference genomes from field-collected specimens by optimizing preservation

Sample preservation often impedes efforts to generate high-quality reference genomes or pangenomes for Earth's more than 2 million plant and animal species due to nucleotide degradation. Here we compare the impacts of storage methods including solution type, temperature, and time on DNA quality and Oxford Nanopore long-read sequencing quality in 9 fish and 4 plant species. We show 95% ethanol largely protects against degradation for fish blood (22 °C, ≤6 weeks) and plant tissue (4 °C, ≤3 weeks). From this furthest storage timepoint, we assemble high-quality reference genomes of 3 fish and 2 plant species with contiguity (contig N50) and completeness (BUSCO) that achieve the Vertebrate Genome Project benchmarking standards. For epigenetic applications, we also report methylation frequency compared to liquid nitrogen control. The results presented here remove the necessity for cryogenic storage in many long read applications and provide a framework for future studies focused on sampling in remote locations, which may represent a large portion of the future sequencing of novel organisms.

Continuous evolution of user-defined genes at 1-million-times the genomic mutation rate

When nature maintains or evolves a gene's function over millions of years at scale, it produces a diversity of homologous sequences whose patterns of conservation and change contain rich structural, functional, and historical information about the gene. However, natural gene diversity likely excludes vast regions of functional sequence space and includes phylogenetic and evolutionary eccentricities, limiting what information we can extract. We introduce an accessible experimental approach for compressing long-term gene evolution to laboratory timescales, allowing for the direct observation of extensive adaptation and divergence followed by inference of structural, functional, and environmental constraints for any selectable gene. To enable this approach, we developed a new orthogonal DNA replication (OrthoRep) system that durably hypermutates chosen genes at a rate of >10 -4 substitutions per base in vivo . When OrthoRep was used to evolve a conditionally essential maladapted enzyme, we obtained thousands of unique multi-mutation sequences with many pairs >60 amino acids apart (>15% divergence), revealing known and new factors influencing enzyme adaptation. The fitness of evolved sequences was not predictable by advanced machine learning models trained on natural variation. We suggest that OrthoRep supports the prospective and systematic discovery of constraints shaping gene evolution, uncovering of new regions in fitness landscapes, and general applications in biomolecular engineering.

Elucidation of the melitidin biosynthesis pathway in pummelo

Specialized plant metabolism is a rich resource of compounds for drug discovery. The acylated flavonoid glycoside melitidin is being developed as an anti-cholesterol statin drug candidate, but its biosynthetic route in plants has not yet been fully characterized. Here, we describe the gene discovery and functional characterization of a new flavonoid gene cluster (UDP-glucuronosyltransferases (CgUGTs), 1,2 rhamnosyltransferase (Cg1,2RhaT), acyltransferases (CgATs)) that is responsible for melitidin biosynthesis in pummelo (Citrus grandis (L.) Osbeck). Population variation analysis indicated that the tailoring of acyltransferases, specific for bitter substrates, mainly determine the natural abundance of melitidin. Moreover, 3-hydroxy-3-methylglutaryl-CoA reductase enzyme inhibition assays showed that the product from this metabolic gene cluster, melitidin, may be an effective anti-cholesterol statin drug candidate. Co-expression of these clustered genes in Nicotiana benthamiana resulted in the formation of melitidin, demonstrating the potential for metabolic engineering of melitidin in a heterologous plant system. This study establishes a biosynthetic pathway for melitidin, which provides genetic resources for the breeding and genetic improvement of pummelo aimed at fortifying the content of biologically active metabolites.

Artificial intelligence for natural product drug discovery

Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to exciting developments in the computational drug design field, facilitating biological activity prediction and de novo drug design for molecular targets of interest. Here, we describe current and future synergies between these developments to effectively identify drug candidates from the plethora of molecules produced by nature. We also discuss how to address key challenges in realizing the potential of these synergies, such as the need for high-quality datasets to train deep learning algorithms and appropriate strategies for algorithm validation.

Recent advances in triterpenoid pathway elucidation and engineering

Triterpenoids are among the most assorted class of specialized metabolites found in all the taxa of living organisms. Triterpenoids are the leading active ingredients sourced from plant species and are utilized in pharmaceutical and cosmetic industries. The triterpenoid precursor 2,3-oxidosqualene, which is biosynthesized via the mevalonate (MVA) pathway is structurally diversified by the oxidosqualene cyclases (OSCs) and other scaffold-decorating enzymes such as cytochrome P450 monooxygenases (P450s), UDP-glycosyltransferases (UGTs) and acyltransferases (ATs). A majority of the bioactive triterpenoids are harvested from the native hosts using the traditional methods of extraction and occasionally semi-synthesized. These methods of supply are time-consuming and do not often align with sustainability goals. Recent advancements in metabolic engineering and synthetic biology have shown prospects for the green routes of triterpenoid pathway reconstruction in heterologous hosts such as Escherichia coli, Saccharomyces cerevisiae and Nicotiana benthamiana, which appear to be quite promising and might lead to the development of alternative source of triterpenoids. The present review describes the biotechnological strategies used to elucidate complex biosynthetic pathways and to understand their regulation and also discusses how the advances in triterpenoid pathway engineering might aid in the scale-up of triterpenoid production in engineered hosts.

gutSMASH predicts specialized primary metabolic pathways from the human gut microbiota

The gut microbiota produce hundreds of small molecules, many of which modulate host physiology. Although efforts have been made to identify biosynthetic genes for secondary metabolites, the chemical output of the gut microbiome consists predominantly of primary metabolites. Here we introduce the gutSMASH algorithm for identification of primary metabolic gene clusters, and we used it to systematically profile gut microbiome metabolism, identifying 19,890 gene clusters in 4,240 high-quality microbial genomes. We found marked differences in pathway distribution among phyla, reflecting distinct strategies for energy capture. These data explain taxonomic differences in short-chain fatty acid production and suggest a characteristic metabolic niche for each taxon. Analysis of 1,135 individuals from a Dutch population-based cohort shows that the level of microbiome-derived metabolites in plasma and feces is almost completely uncorrelated with the metagenomic abundance of corresponding metabolic genes, indicating a crucial role for pathway-specific gene regulation and metabolite flux. This work is a starting point for understanding differences in how bacterial taxa contribute to the chemistry of the microbiome.

Structural advances toward understanding the catalytic activity and conformational dynamics of modular nonribosomal peptide synthetases

Covering: up to fall 2022.Nonribosomal peptide synthetases (NRPSs) are a family of modular, multidomain enzymes that catalyze the biosynthesis of important peptide natural products, including antibiotics, siderophores, and molecules with other biological activity. The NRPS architecture involves an assembly line strategy that tethers amino acid building blocks and the growing peptides to integrated carrier protein domains that migrate between different catalytic domains for peptide bond formation and other chemical modifications. Examination of the structures of individual domains and larger multidomain proteins has identified conserved conformational states within a single module that are adopted by NRPS modules to carry out a coordinated biosynthetic strategy that is shared by diverse systems. In contrast, interactions between modules are much more dynamic and do not yet suggest conserved conformational states between modules. Here we describe the structures of NRPS protein domains and modules and discuss the implications for future natural product discovery.

Contribution of endophytes towards improving plant bioactive metabolites: a rescue option against red-taping of medicinal plants

Medicinal plants remain a valuable source for natural drug bioprospecting owing to their multi-target spectrum. However, their use as raw materials for novel drug synthesis has been greatly limited by unsustainable harvesting leading to decimation of their wild populations coupled with inherent low concentrations of constituent secondary metabolites per unit mass. Thus, adding value to the medicinal plants research dynamics calls for adequate attention. In light of this, medicinal plants harbour endophytes which are believed to be contributing towards the host plant survival and bioactive metabolites through series of physiological interference. Stimulating secondary metabolite production in medicinal plants by using endophytes as plant growth regulators has been demonstrated to be one of the most effective methods for increasing metabolite syntheses. Use of endophytes as plant growth promotors could help to ensure continuous supply of medicinal plants, and mitigate issues with fear of extinction. Endophytes minimize heavy metal toxicity in medicinal plants. It has been hypothesized that when medicinal plants are exposed to harsh conditions, associated endophytes are the primary signalling channels that induce defensive reactions. Endophytes go through different biochemical processes which lead to activation of defence mechanisms in the host plants. Thus, through signal transduction pathways, endophytic microorganisms influence genes involved in the generation of secondary metabolites by plant cells. Additionally, elucidating the role of gene clusters in production of secondary metabolites could expose factors associated with low secondary metabolites by medicinal plants. Promising endophyte strains can be manipulated for enhanced production of metabolites, hence, better probability of novel bioactive metabolites through strain improvement, mutagenesis, co-cultivation, and media adjustment.

In silico resources help combat cancer drug resistance mediated by target mutations

Drug resistance causes catastrophic cancer treatment failures. Mutations in target proteins with altered drug binding indicate a main mechanism of cancer drug resistance (CDR). Global research has generated considerable CDR-related data and well-established knowledge bases and predictive tools. Unfortunately, these resources are fragmented and underutilized. Here, we examine computational resources for exploring CDR caused by target mutations, analyzing these tools based on their functional characteristics, data capacity, data sources, methodologies and performance. We also discuss their disadvantages and provide examples of how potential inhibitors of CDR have been discovered using these resources. This toolkit is designed to help specialists explore resistance occurrence effectively and to explain resistance prediction to non-specialists easily.

The biotechnological potential of Aeromonas: a bird's eye view

The genus Aeromonas comprises Gram-negative bacilli widely distributed in aquatic habitats that can also be found in the terrestrial environment and in close association with humans and animals. Aeromonas spp. are particularly versatile bacteria, with high genomic plasticity and notable capacity to adapt to different environments and extreme conditions. On account of being mostly associated with their pathogenic potential, research on the biotechnological potentialities of Aeromonas spp. is considerably scarce when compared to other bacterial groups. Nonetheless, studies over the years have been hinting at several interesting hidden potentialities in this bacterial group, especially with the recent advances in whole-genome sequencing, unveiling Aeromonas spp. as interesting candidates for the discovery of novel industrial biocatalysts, bioremediation strategies, and biopolyester production. In this context, the present study aims to provide an overview of the main biotechnological applications reported in the genus Aeromonas and provide new insights into the further exploration of these frequently overlooked, yet fascinating, bacteria.

Metabolome-guided genome mining of RiPP natural products

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a chemically diverse class of metabolites. Many RiPPs show potent biological activities that make them attractive starting points for drug development. A promising approach for the discovery of new classes of RiPPs is genome mining. However, the accuracy of genome mining is hampered by the lack of signature genes shared across different RiPP classes. One way to reduce false-positive predictions is by complementing genomic information with metabolomics data. In recent years, several new approaches addressing such integrative genomics and metabolomics analyses have been developed. In this review, we provide a detailed discussion of RiPP-compatible software tools that integrate paired genomics and metabolomics data. We highlight current challenges in data integration and identify opportunities for further developments targeting new classes of bioactive RiPPs.

Sequence controlled secondary structure is important for the site-selectivity of lanthipeptide cyclization

Lanthipeptides are ribosomally synthesized and post-translationally modified peptides that are generated from precursor peptides through a dehydration and cyclization process. ProcM, a class II lanthipeptide synthetase, demonstrates high substrate tolerance. It is enigmatic that a single enzyme can catalyze the cyclization process of many substrates with high fidelity. Previous studies suggested that the site-selectivity of lanthionine formation is determined by substrate sequence rather than by the enzyme. However, exactly how substrate sequence contributes to site-selective lanthipeptide biosynthesis is not clear. In this study, we performed molecular dynamic simulations for ProcA3.3 variants to explore how the predicted solution structure of the substrate without enzyme correlates to the final product formation. Our simulation results support a model in which the secondary structure of the core peptide is important for the final product's ring pattern for the substrates investigated. We also demonstrate that the dehydration step in the biosynthesis pathway does not influence the site-selectivity of ring formation. In addition, we performed simulation for ProcA1.1 and 2.8, which are well-suited candidates to investigate the connection between order of ring formation and solution structure. Simulation results indicate that in both cases, C-terminal ring formation is more likely which was supported by experimental results. Our findings indicate that the substrate sequence and its solution structure can be used to predict the site-selectivity and order of ring formation, and that secondary structure is a crucial factor influencing the site-selectivity. Taken together, these findings will facilitate our understanding of the lanthipeptide biosynthetic mechanism and accelerate bioengineering efforts for lanthipeptide-derived products.

Small Molecule in situ Resin Capture - A Compound First Approach to Natural Product Discovery

Microbial natural products remain an important resource for drug discovery. Yet, commonly employed discovery techniques are plagued by the rediscovery of known compounds, the relatively few microbes that can be cultured, and laboratory growth conditions that do not elicit biosynthetic gene expression among myriad other challenges. Here we introduce a culture independent approach to natural product discovery that we call the Small Molecule In situ Resin Capture (SMIRC) technique. SMIRC exploits in situ environmental conditions to elicit compound production and represents a new approach to access poorly explored chemical space by capturing natural products directly from the environments in which they are produced. In contrast to traditional methods, this compound-first approach can capture structurally complex small molecules across all domains of life in a single deployment while relying on Nature to provide the complex and poorly understood environmental cues needed to elicit biosynthetic gene expression. We illustrate the effectiveness of SMIRC in marine habitats with the discovery of numerous new compounds and demonstrate that sufficient compound yields can be obtained for NMR-based structure assignment. Two new compound classes are reported including one novel carbon skeleton that possesses a functional group not previously observed among natural products and a second that possesses potent biological activity. We introduce expanded deployments, in situ cultivation, and metagenomics as methods to facilitate compound discovery, enhance yields, and link compounds to producing organisms. This compound first approach can provide unprecedented access to new natural product chemotypes with broad implications for drug discovery.

Accessing hidden microbial biosynthetic potential from underexplored sources for novel drug discovery

Microbial natural products and their structural analogues have widely used as pharmaceutical agents, especially for infectious diseases and cancer. Despite this success, new structural classes with innovative chemistry and modes of action are urgently needed to be developed to combat the growing antimicrobial resistance and other public health problems. The advances in next-generation sequencing technologies and powerful computational tools open up new opportunities to explore microbial biosynthetic potential from underexplored sources, with millions of secondary metabolites awaiting discovery. The review highlights challenges associated with discovery of new chemical entities, rich reservoirs provided by untapped taxa, ecological niches or host microbiomes, emerging synthetic biotechnologies to unearth the hidden microbial biosynthetic potential for novel drug discovery at scale and speed.

Data-Driven Elucidation of Flavor Chemistry

Flavor molecules are commonly used in the food industry to enhance product quality and consumer experiences but are associated with potential human health risks, highlighting the need for safer alternatives. To address these health-associated challenges and promote reasonable application, several databases for flavor molecules have been constructed. However, no existing studies have comprehensively summarized these data resources according to quality, focused fields, and potential gaps. Here, we systematically summarized 25 flavor molecule databases published within the last 20 years and revealed that data inaccessibility, untimely updates, and nonstandard flavor descriptions are the main limitations of current studies. We examined the development of computational approaches (e.g., machine learning and molecular simulation) for the identification of novel flavor molecules and discussed their major challenges regarding throughput, model interpretability, and the lack of gold-standard data sets for equitable model evaluation. Additionally, we discussed future strategies for the mining and designing of novel flavor molecules based on multi-omics and artificial intelligence to provide a new foundation for flavor science research.

Uncovering the Cryptic Gene Cluster ahb for 3-amino-4-hydroxybenzoate Derived Ahbamycins, by Searching SARP Regulator Encoding Genes in the Streptomyces argillaceus Genome

Genome mining using standard bioinformatics tools has allowed for the uncovering of hidden biosynthesis gene clusters for specialized metabolites in Streptomyces genomes. In this work, we have used an alternative approach consisting in seeking "Streptomyces Antibiotic Regulatory Proteins" (SARP) encoding genes and analyzing their surrounding DNA region to unearth cryptic gene clusters that cannot be identified using standard bioinformatics tools. This strategy has allowed the unveiling of the new ahb cluster in Streptomyces argillaceus, which had not been retrieved before using antiSMASH. The ahb cluster is highly preserved in other Streptomyces strains, which suggests a role for their encoding compounds in specific environmental conditions. By combining overexpression of three regulatory genes and generation of different mutants, we were able to activate the ahb cluster, and to identify and chemically characterize the encoded compounds that we have named ahbamycins (AHBs). These constitute a new family of metabolites derived from 3-amino-4-hydroxybenzoate (3,4-AHBA) known for having antibiotic and antitumor activity. Additionally, by overexpressing three genes of the cluster (ahbH, ahbI, and ahbL2) for the synthesis and activation of 3,4-AHBA, a new hybrid compound, AHB18, was identified which had been produced from a metabolic crosstalk between the AHB and the argimycin P pathways. The identification of this new BGC opens the possibility to generate new compounds by combinatorial biosynthesis.

Homo medicus: The transition to meat eating increased pathogen pressure and the use of pharmacological plants in Homo

The human lineage transitioned to a more carnivorous niche 2.6 mya and evolved a large body size and slower life history, which likely increased zoonotic pathogen pressure. Evidence for this increase includes increased zoonotic infections in modern hunter-gatherers and bushmeat hunters, exceptionally low stomach pH compared to other primates, and divergence in immune-related genes. These all point to change, and probably intensification, in the infectious disease environment of Homo compared to earlier hominins and other apes. At the same time, the brain, an organ in which immune responses are constrained, began to triple in size. We propose that the combination of increased zoonotic pathogen pressure and the challenges of defending a large brain and body from pathogens in a long-lived mammal, selected for intensification of the plant-based self-medication strategies already in place in apes and other primates. In support, there is evidence of medicinal plant use by hominins in the middle Paleolithic, and all cultures today have sophisticated, plant-based medical systems, add spices to food, and regularly consume psychoactive plant substances that are harmful to helminths and other pathogens. We propose that the computational challenges of discovering effective plant-based treatments, the consequent ability to consume more energy-rich animal foods, and the reduced reliance on energetically-costly immune responses helped select for increased cognitive abilities and unique exchange relationships in Homo. In the story of human evolution, which has long emphasized hunting skills, medical skills had an equal role to play.

Bioinformatics toolbox for exploring target mutation-induced drug resistance

Drug resistance is increasingly among the main issues affecting human health and threatening agriculture and food security. In particular, developing approaches to overcome target mutation-induced drug resistance has long been an essential part of biological research. During the past decade, many bioinformatics tools have been developed to explore this type of drug resistance, and they have become popular for elucidating drug resistance mechanisms in a low cost, fast and effective way. However, these resources are scattered and underutilized, and their strengths and limitations have not been systematically analyzed and compared. Here, we systematically surveyed 59 freely available bioinformatics tools for exploring target mutation-induced drug resistance. We analyzed and summarized these resources based on their functionality, data volume, data source, operating principle, performance, etc. And we concisely discussed the strengths, limitations and application examples of these tools. Specifically, we tested some predictive tools and offered some thoughts from the clinician's perspective. Hopefully, this work will provide a useful toolbox for researchers working in the biomedical, pesticide, bioinformatics and pharmaceutical engineering fields, and a good platform for non-specialists to quickly understand drug resistance prediction.

Terpene biosynthesis in marine sponge animals

Sea sponges are the largest marine source of small-molecule natural products described to date. Sponge-derived molecules, such as the chemotherapeutic eribulin, the calcium-channel blocker manoalide, and antimalarial compound kalihinol A, are renowned for their impressive medicinal, chemical, and biological properties. Sponges contain microbiomes that control the production of many natural products isolated from these marine invertebrates. In fact, all genomic studies to date investigating the metabolic origins of sponge-derived small molecules concluded that microbes-not the sponge animal host-are the biosynthetic producers. However, early cell-sorting studies suggested the sponge animal host may play a role particularly in the production of terpenoid molecules. To investigate the genetic underpinnings of sponge terpenoid biosynthesis, we sequenced the metagenome and transcriptome of an isonitrile sesquiterpenoid-containing sponge of the order Bubarida. Using bioinformatic searches and biochemical validation, we identified a group of type I terpene synthases (TSs) from this sponge and multiple other species, the first of this enzyme class characterized from the sponge holobiome. The Bubarida TS-associated contigs consist of intron-containing genes homologous to sponge genes and feature GC percentage and coverage consistent with other eukaryotic sequences. We identified and characterized TS homologs from five different sponge species isolated from geographically distant locations, thereby suggesting a broad distribution amongst sponges. This work sheds light on the role of sponges in secondary metabolite production and speaks to the possibility that other sponge-specific molecules originate from the animal host.

Current status of secondary metabolite pathways linked to their related biosynthetic gene clusters in Aspergillus section Nigri

Covering: up to the end of 2021Aspergilli are biosynthetically 'talented' micro-organisms and therefore the natural products community has continually been interested in the wealth of biosynthetic gene clusters (BGCs) encoding numerous secondary metabolites related to these fungi. With the rapid increase in sequenced fungal genomes combined with the continuous development of bioinformatics tools such as antiSMASH, linking new structures to unknown BGCs has become much easier when taking retro-biosynthetic considerations into account. On the other hand, in most cases it is not as straightforward to prove proposed biosynthetic pathways due to the lack of implemented genetic tools in a given fungal species. As a result, very few secondary metabolite biosynthetic pathways have been characterized even amongst some of the most well studied Aspergillus spp., section Nigri (black aspergilli). This review will cover all known biosynthetic compound families and their structural diversity known from black aspergilli. We have logically divided this into sub-sections describing major biosynthetic classes (polyketides, non-ribosomal peptides, terpenoids, meroterpenoids and hybrid biosynthesis). Importantly, we will focus the review on metabolites which have been firmly linked to their corresponding BGCs.

Genome Mining and Heterologous Expression Guided the Discovery of Antimicrobial Naphthocyclinones from Streptomyces eurocidicus CGMCC 4.1086

A type II polyketide synthase biosynthetic gene cluster (nap) was identified in Streptomyces eurocidicus CGMCC 4.1086 via genome mining. The heterologous expression of the cryptic nap gene cluster in Streptomyces albus J1074 generated dimerized aromatic polyketide naphthocyclinones (1-3), whose structures were determined via extensive analysis using nuclear magnetic resonance and high-resolution electrospray ionization mass spectroscopy. The biological pathway of naphthocyclinone synthesis was revealed via in vivo gene deletion, in vitro biochemical reactions, and comparative genomics. Remarkably, 3 played a crucial role in inhibiting Phytophthora capsici and Phytophthora sojae, with EC₅₀ values of 6.1 and 20.2 μg/mL, respectively. Furthermore, 3 exhibited a potent protective effect against P. capsici and P. sojae in greenhouse tests.

Biosynthesis of Lyngbyastatins 1 and 3, Cytotoxic Depsipeptides from an Okeania sp. Marine Cyanobacterium

Lyngbyastatins (Lbns) 1 (1) and 3 (2) belong to a group of cyclic depsipeptides that inhibit cancer cell proliferation. These compounds have been isolated from different marine cyanobacterial collections, while further development of these compounds relies on their lengthy total synthesis. Biosynthetic studies of these compounds can provide viable strategies to access these compounds and develop new analogs. In this study, we report the identification and characterization of one Lbn biosynthetic gene cluster (BGC) from the marine cyanobacterium Okeania sp. VPG18-21. We initially identified 1 and 2 in the organic extract by mass spectrometry and performed the targeted isolation of these compounds, which feature a (2S,3R)-3-amino-2-methylpentanoic acid (MAP) and a (2S,3R)-3-amino-2-methylhexanoic acid (Amha) moiety, respectively. Parallel metagenomic sequencing of VPG18-21 led to the identification of a putative Lbn BGC that encodes six megaenzymes (LbnA-F), including one polyketide synthase (PKS, LbnE), four nonribosomal peptide synthetases (NRPSs, LbnB-D and -F), and one PKS-NRPS hybrid (LbnA). Bioinformatic analysis of these enzymes suggested that the BGC produces 1 and 2. Furthermore, our biochemical studies of three recombinant adenylation domains uncovered their substrate specificities, supporting the identity of the BGC. Finally, we identified near-complete Lbn-like BGCs in the genomes of two other marine cyanobacteria.

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.

Solanimycin: Biosynthesis and Distribution of a New Antifungal Antibiotic Regulated by Two Quorum-Sensing Systems

The increasing emergence of drug-resistant fungal infections has necessitated a search for new compounds capable of combating fungal pathogens of plants, animals, and humans. Microorganisms represent the main source of antibiotics with applicability in agriculture and in the clinic, but many aspects of their metabolic potential remain to be explored. This report describes the discovery and characterization of a new antifungal compound, solanimycin, produced by a hybrid polyketide/nonribosomal peptide (PKS/NRPS) system in Dickeya solani, the enterobacterial pathogen of potato. Solanimycin was active against a broad range of plant-pathogenic fungi of global economic concern and the human pathogen Candida albicans. The genomic cluster responsible for solanimycin production was defined and analyzed to identify the corresponding biosynthetic proteins, which include four multimodular PKS/NRPS proteins and several tailoring enzymes. Antifungal production in D. solani was enhanced in response to experimental conditions found in infected potato tubers and high-density fungal cultures. Solanimycin biosynthesis was cell density dependent in D. solani and was controlled by both the ExpIR acyl-homoserine lactone and Vfm quorum-sensing systems of the bacterial phytopathogen. The expression of the solanimycin cluster was also regulated at the post-transcriptional level, with the regulator RsmA playing a major role. The solanimycin biosynthetic cluster was conserved across phylogenetically distant bacterial genera, and multiple pieces of evidence support that the corresponding gene clusters were acquired by horizontal gene transfer. Given its potent broad-range antifungal properties, this study suggests that solanimycin and related molecules may have potential utility for agricultural and clinical exploitation. IMPORTANCE Fungal infections represent a major clinical, agricultural, and food security threat worldwide, which is accentuated due to the difficult treatment of these infections. Microorganisms represent a prolific source of antibiotics, and current data support that this enormous biosynthetic potential has been scarcely explored. To improve the performance in the discovery of novel antimicrobials, there is a need to diversify the isolation niches for new antibiotic-producing microorganisms as well as to scrutinize novel phylogenetic positions. With the identification of the antifungal antibiotic solanimycin in a broad diversity of phytopathogenic Dickeya spp., we provide further support for the potential of plant-associated bacteria for the biosynthesis of novel antimicrobials. The complex regulatory networks involved in solanimycin production reflect the high metabolic cost of bacterial secondary metabolism. This metabolic regulatory control makes many antibiotics cryptic under standard laboratory conditions, and mimicking environmental conditions, as shown here, is a strategy to activate cryptic antibiotic clusters.

Navigating and expanding the roadmap of natural product genome mining tools

Natural products are structurally highly diverse and exhibit a wide array of biological activities. As a result, they serve as an important source of new drug leads. Traditionally, natural products have been discovered by bioactivity-guided fractionation. The advent of genome sequencing technology has resulted in the introduction of an alternative approach towards novel natural product scaffolds: Genome mining. Genome mining is an in-silico natural product discovery strategy in which sequenced genomes are analyzed for the potential of the associated organism to produce natural products. Seemingly universal biosynthetic principles have been deciphered for most natural product classes that are used to detect natural product biosynthetic gene clusters using pathway-encoded conserved key enzymes, domains, or motifs as bait. Several generations of highly sophisticated tools have been developed for the biosynthetic rule-based identification of natural product gene clusters. Apart from these hard-coded algorithms, multiple tools that use machine learning-based approaches have been designed to complement the existing genome mining tool set and focus on natural product gene clusters that lack genes with conserved signature sequences. In this perspective, we take a closer look at state-of-the-art genome mining tools that are based on either hard-coded rules or machine learning algorithms, with an emphasis on the confidence of their predictions and potential to identify non-canonical natural product biosynthetic gene clusters. We highlight the genome mining pipelines' current strengths and limitations by contrasting their advantages and disadvantages. Moreover, we introduce two indirect biosynthetic gene cluster identification strategies that complement current workflows. The combination of all genome mining approaches will pave the way towards a more comprehensive understanding of the full biosynthetic repertoire encoded in microbial genome sequences.

NPASS database update 2023: quantitative natural product activity and species source database for biomedical research

Quantitative activity and species source data of natural products (NPs) are important for drug discovery, medicinal plant research, and microbial investigations. Activity values of NPs against specific targets are useful for discovering targeted therapeutic agents and investigating the mechanism of medicinal plants. Composition/concentration values of NPs in individual species facilitate the assessments and investigations of the therapeutic quality of herbs and phenotypes of microbes. Here, we describe an update of the NPASS natural product activity and species source database previously featured in NAR. This update includes: (i) new data of ∼95 000 records of the composition/concentration values of ∼1 490 NPs/NP clusters in ∼390 species, (ii) extended data of activity values of ∼43 200 NPs against ∼7 700 targets (∼40% and ∼32% increase, respectively), (iii) extended data of ∼31 600 species sources of ∼94 400 NPs (∼26% and ∼32% increase, respectively), (iv) new species types of ∼440 co-cultured microbes and ∼420 engineered microbes, (v) new data of ∼66 600 NPs without experimental activity values but with estimated activity profiles from the established chemical similarity tool Chemical Checker, (vi) new data of the computed drug-likeness properties and the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties for all NPs. NPASS update version is freely accessible at http://bidd.group/NPASS.