Metabologenomics: Correlation of Microbial Gene Clusters with Metabolites Drives Discovery of a Nonribosomal Peptide with an Unusual Amino Acid Monomer

Identification of the Cirratiomycin Biosynthesis Gene Cluster in Streptomyces Cirratus: Elucidation of the Biosynthetic Pathways for 2,3-Diaminobutyric Acid and Hydroxymethylserine

Cirratiomycin, a heptapeptide with antibacterial activity, was isolated and characterized in 1981; however, its biosynthetic pathway has not been elucidated. It contains several interesting nonproteinogenic amino acids, such as (2S,3S)-2,3-diaminobutyric acid ((2S,3S)-DABA) and α-(hydroxymethyl)serine, as building blocks. Here, we report the identification of a cirratiomycin biosynthetic gene cluster in Streptomyces cirratus. Bioinformatic analysis revealed that several Streptomyces viridifaciens and Kitasatospora aureofaciens strains also have this cluster. One S. viridifaciens strain was confirmed to produce cirratiomycin. The biosynthetic gene cluster was shown to be responsible for cirratiomycin biosynthesis in S. cirratus in a gene inactivation experiment using CRISPR-cBEST. Interestingly, this cluster encodes a nonribosomal peptide synthetase (NRPS) composed of 12 proteins, including those with an unusual domain organization: a stand-alone adenylation domain, two stand-alone condensation domains, two type II thioesterases, and two NRPS modules that have no adenylation domain. Using heterologous expression and in vitro analysis of recombinant enzymes, we revealed the biosynthetic pathway of (2S,3S)-DABA: (2S,3S)-DABA is synthesized from l-threonine by four enzymes, CirR, CirS, CirQ, and CirB. In addition, CirH, a glycine/serine hydroxymethyltransferase homolog, was shown to synthesize α-(hydroxymethyl)serine from d-serine in vitro. These findings broaden our knowledge of nonproteinogenic amino acid biosynthesis.

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.

Scoary2: rapid association of phenotypic multi-omics data with microbial pan-genomes

Unraveling bacterial gene function drives progress in various areas, such as food production, pharmacology, and ecology. While omics technologies capture high-dimensional phenotypic data, linking them to genomic data is challenging, leaving 40-60% of bacterial genes undescribed. To address this bottleneck, we introduce Scoary2, an ultra-fast microbial genome-wide association studies (mGWAS) software. With its data exploration app and improved performance, Scoary2 is the first tool to enable the study of large phenotypic datasets using mGWAS. As proof of concept, we explore the metabolome of yogurts, each produced with a different Propionibacterium reichii strain and discover two genes affecting carnitine metabolism.

Review on computer-assisted biosynthetic capacities elucidation to assess metabolic interactions and communication within microbial communities

Microbial communities thrive through interactions and communication, which are challenging to study as most microorganisms are not cultivable. To address this challenge, researchers focus on the extracellular space where communication events occur. Exometabolomics and interactome analysis provide insights into the molecules involved in communication and the dynamics of their interactions. Advances in sequencing technologies and computational methods enable the reconstruction of taxonomic and functional profiles of microbial communities using high-throughput multi-omics data. Network-based approaches, including community flux balance analysis, aim to model molecular interactions within and between communities. Despite these advances, challenges remain in computer-assisted biosynthetic capacities elucidation, requiring continued innovation and collaboration among diverse scientists. This review provides insights into the current state and future directions of computer-assisted biosynthetic capacities elucidation in studying microbial communities.

Pan-Omics in Sheep: Unveiling Genetic Landscapes

Multi-omics-integrated analysis, known as panomics, represents an advanced methodology that harnesses various high-throughput technologies encompassing genomics, epigenomics, transcriptomics, proteomics, and metabolomics. Sheep, playing a pivotal role in agricultural sectors due to their substantial economic importance, have witnessed remarkable advancements in genetic breeding through the amalgamation of multiomics analyses, particularly with the evolution of high-throughput technologies. This integrative approach has established a robust theoretical foundation, enabling a deeper understanding of sheep genetics and fostering improvements in breeding strategies. The comprehensive insights obtained through this approach shed light on diverse facets of sheep development, including growth, reproduction, disease resistance, and the quality of livestock products. This review primarily focuses on the application of principal omics analysis technologies in sheep, emphasizing correlation studies between multiomics data and specific traits such as meat quality, wool characteristics, and reproductive features. Additionally, this paper anticipates forthcoming trends and potential developments in this field.

Proteomics and Metabolomics Analysis Reveal the Regulation Mechanism of Linoleate Isomerase Activity and Function in Propionibacterium acnes

Conjugated linoleic acid (CLA) holds significant application prospects due to its anticancer, anti-atherosclerosis, lipid-lowering, weight-loss, and growth-promoting functions. The key to its efficient production lies in optimizing the biocatalytic performance of linoleic acid isomerase (LAI). Here, we constructed a Propionibacterium acnes mutant library and screened positive mutants with high linoleate isomerase activity. The proteomics and metabolomics were used to explore the mechanism in the regulation of linoleic acid isomerase activity. High-throughput proteomics revealed 104 differentially expressed proteins unique to positive mutant strains of linoleic acid isomerase of which 57 were upregulated and 47 were downregulated. These differentially expressed proteins were primarily involved in galactose metabolism, the phosphotransferase system, starch metabolism, and sucrose metabolism. Differential metabolic pathways were mainly enriched in amino acid biosynthesis, including glutamate metabolism, the Aminoacyl-tRNA biosynthesis pathway, and the ABC transporter pathway. The upregulated metabolites include dl-valine and Acetyl coA, while the downregulated metabolites include Glutamic acid and Phosphoenolpyruvate. Overall, the activity of linoleic acid isomerase in the mutant strain was increased by the regulation of key proteins involved in galactose metabolism, sucrose metabolism, and the phosphotransferase system. This study provides a theoretical basis for the development of high-yield CLA food.

Two distinct non-ribosomal peptide synthetase-independent siderophore synthetase gene clusters identified in Armillaria and other species in the Physalacriaceae

Siderophores are important for ferric iron solubilization, sequestration, transportation, and storage, especially under iron-limiting conditions such as aerobic conditions at high pH. Siderophores are mainly produced by non-ribosomal peptide synthetase-dependent siderophore pathway, non-ribosomal peptide synthetase-independent siderophore synthetase pathway, or the hybrid non-ribosomal peptide synthetases/non-ribosomal peptide synthetases-independent siderophore pathway. Outcompeting or inhibition of plant pathogens, alteration of host defense mechanisms, and alteration of plant-fungal interactions have been associated with fungal siderophores. To understand these mechanisms in fungi, studies have been conducted on siderophore biosynthesis by ascomycetes with limited focus on the basidiomycetes. Armillaria includes several species that are pathogens of woody plants and trees important to agriculture, horticulture, and forestry. The aim of this study was to investigate the presence of non-ribosomal peptide synthetases-independent siderophore synthetase gene cluster(s) in genomes of Armillaria species using a comparative genomics approach. Iron-dependent growth and siderophore biosynthesis in strains of selected Armillaria spp. were also evaluated in vitro. Two distinct non-ribosomal peptide synthetases-independent siderophore synthetase gene clusters were identified in all the genomes. All non-ribosomal peptide synthetases-independent siderophore synthetase genes identified putatively encode Type A' non-ribosomal peptide synthetases-independent siderophore synthetases, most of which have IucA_IucC and FhuF-like transporter domains at their N- and C-terminals, respectively. The effect of iron on culture growth varied among the strains studied. Bioassays using the CAS assay on selected Armillaria spp. revealed in vitro siderophore biosynthesis by all strains irrespective of added FeCl3 concentration. This study highlights some of the tools that Armillaria species allocate to iron homeostasis. The information generated from this study may in future aid in developing molecular based methods to control these phytopathogens.

Integrating Taxonomic and Chemical Diversity of Mangrove-Associated Ascomycetes to Discover or Repurpose Bioactive Natural Products

Natural product reisolation is a bottleneck when discovering new bioactive chemical entities from nature. To overcome this issue, multi-informative approaches integrating several layers of data have been applied with promising results. In this study, integration of taxonomy, nontargeted metabolomics, and bioactivity information resulted in the selection of Scytalidium sp. IQ-074 and Diaporthe sp. IQ-053 to isolate new natural products active against hPTP1B1-400 and repurpose others as antibiotics. Strain IQ-074 was selected based on the hypothesis that investigating poorly studied and highly metabolic taxa could lead to the isolation of new chemical entities. A chemical investigation of IQ-074 resulted in the isolation of papyracillic acid A (14), 7-deoxypapyracillic acid A (15a and 15b), and linear polyketides scytalpolyols A-D (16-19). Compound 17 inhibited hPTP1B1-400 with a half-maximal inhibitory concentration of 27.0 ± 1.7 μM. Diaporthe sp. IQ-053 was selected based on its antibacterial properties against pathogenic strains. Its chemical investigation yielded dothiorelones A (20) and I (21), cytosporones B (22) and C (23), pestalotiopsone B (24), and diaporthalasin (25). Compounds 22 and 25 inhibited the growth of Staphylococcus aureus and Staphylococcus epidermidis 42R and moderately inhibited the growth of Acinetobacter baumannii A564, a pandrug-resistant bacterium.

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.

Correlative metabologenomics of 110 fungi reveals metabolite-gene cluster pairs

Natural products research increasingly applies -omics technologies to guide molecular discovery. While the combined analysis of genomic and metabolomic datasets has proved valuable for identifying natural products and their biosynthetic gene clusters (BGCs) in bacteria, this integrated approach lacks application to fungi. Because fungi are hyper-diverse and underexplored for new chemistry and bioactivities, we created a linked genomics-metabolomics dataset for 110 Ascomycetes, and optimized both gene cluster family (GCF) networking parameters and correlation-based scoring for pairing fungal natural products with their BGCs. Using a network of 3,007 GCFs (organized from 7,020 BGCs), we examined 25 known natural products originating from 16 known BGCs and observed statistically significant associations between 21 of these compounds and their validated BGCs. Furthermore, the scalable platform identified the BGC for the pestalamides, demystifying its biogenesis, and revealed more than 200 high-scoring natural product-GCF linkages to direct future discovery.

The use of nitrogen-15 in microbial natural product discovery and biosynthetic characterization

This mini-review covers the use of nitrogen-15 in bacterial and fungal natural product discovery and biosynthetic characterization from 1970 to 2022. Nitrogen is an important element in a number of bioactive and structurally intriguing natural products including alkaloids, non-ribosomal peptides, and hybrid natural products. Nitrogen-15 can be detected at natural abundance utilizing two-dimensional nuclear magnetic resonance and mass spectrometry. Additionally, it is a stable isotope that can be added to growth media for both filamentous fungi and bacteria. With stable isotope feeding, additional two-dimensional nuclear magnetic resonance and mass spectrometry strategies have become available, and there is a growing trend to use nitrogen-15 stable isotope feeding for the biosynthetic characterization of natural products. This mini-review will catalog the use of these strategies, analyze the strengths and weaknesses of the different approaches, and suggest future directions for the use of nitrogen-15 in natural product discovery and biosynthetic characterization.

High-Throughput Screening of Natural Product and Synthetic Molecule Libraries for Antibacterial Drug Discovery

Due to the continued emergence of resistance and a lack of new and promising antibiotics, bacterial infection has become a major public threat. High-throughput screening (HTS) allows rapid screening of a large collection of molecules for bioactivity testing and holds promise in antibacterial drug discovery. More than 50% of the antibiotics that are currently available on the market are derived from natural products. However, with the easily discoverable antibiotics being found, finding new antibiotics from natural sources has seen limited success. Finding new natural sources for antibacterial activity testing has also proven to be challenging. In addition to exploring new sources of natural products and synthetic biology, omics technology helped to study the biosynthetic machinery of existing natural sources enabling the construction of unnatural synthesizers of bioactive molecules and the identification of molecular targets of antibacterial agents. On the other hand, newer and smarter strategies have been continuously pursued to screen synthetic molecule libraries for new antibiotics and new druggable targets. Biomimetic conditions are explored to mimic the real infection model to better study the ligand-target interaction to enable the designing of more effective antibacterial drugs. This narrative review describes various traditional and contemporaneous approaches of high-throughput screening of natural products and synthetic molecule libraries for antibacterial drug discovery. It further discusses critical factors for HTS assay design, makes a general recommendation, and discusses possible alternatives to traditional HTS of natural products and synthetic molecule libraries for antibacterial drug discovery.

Labelling studies in the biosynthesis of polyketides and non-ribosomal peptides

Covering: 2015 to 2022In this review, we discuss the recent advances in the use of isotopically labelled compounds to investigate the biosynthesis of polyketides, non-ribosomally synthesised peptides, and their hybrids. Also, we highlight the use of isotopes in the elucidation of their structures and investigation of enzyme mechanisms. The biosynthetic pathways of selected examples are presented in detail to reveal the principles of the discussed labelling experiments. The presented examples demonstrate that the application of isotopically labelled compounds is still the state of the art and can provide valuable information for the biosynthesis of natural products.

Nonribosomal peptide synthetase gene clusters and characteristics of predicted NRPS-dependent siderophore synthetases in Armillaria and other species in the Physalacriaceae

Fungal secondary metabolites are often pathogenicity or virulence factors synthesized by genes contained in secondary metabolite gene clusters (SMGCs). Nonribosomal polypeptide synthetase (NRPS) clusters are SMGCs which produce peptides such as siderophores, the high affinity ferric iron chelating compounds required for iron uptake under aerobic conditions. Armillaria spp. are mostly facultative necrotrophs of woody plants. NRPS-dependent siderophore synthetase (NDSS) clusters of Armillaria spp. and selected Physalacriaceae were investigated using a comparative genomics approach. Siderophore biosynthesis by strains of selected Armillaria spp. was evaluated using CAS and split-CAS assays. At least one NRPS cluster and other clusters were detected in the genomes studied. No correlation was observed between the number and types of SMGCs and reported pathogenicity of the species studied. The genomes contained one NDSS cluster each. All NDSSs were multi-modular with the domain architecture (ATC)₃(TC)₂. NDSS clusters of the Armillaria spp. showed a high degree of microsynteny. In the genomes of Desarmillaria spp. and Guyanagaster necrorhizus, NDSS clusters were more syntenic with NDSS clusters of Armillaria spp. than to those of the other Physalacriaceae species studied. Three A-domain orthologous groups were identified in the NDSSs, and atypical Stachelhaus codes were predicted for the A3 orthologous group. In vitro biosynthesis of mainly hydroxamate and some catecholate siderophores was observed. Hence, Armillaria spp. generally contain one highly conserved, NDSS cluster although some interspecific variations in the products of these clusters is expected. Results from this study lays the groundwork for future studies to elucidate the molecular biology of fungal phyto-pathogenicity.

Metabolomics and Genomics Enable the Discovery of a New Class of Nonribosomal Peptidic Metallophores from a Marine Micromonospora

Although microbial genomes harbor an abundance of biosynthetic gene clusters, there remain substantial technological gaps that impair the direct correlation of newly discovered gene clusters and their corresponding secondary metabolite products. As an example of one approach designed to minimize or bridge such gaps, we employed hierarchical clustering analysis and principal component analysis (hcapca, whose sole input is MS data) to prioritize 109 marine Micromonospora strains and ultimately identify novel strain WMMB482 as a candidate for in-depth "metabologenomics" analysis following its prioritization. Highlighting the power of current MS-based technologies, not only did hcapca enable the discovery of one new, nonribosomal peptide bearing an incredible diversity of unique functional groups, but metabolomics for WMMB482 unveiled 16 additional congeners via the application of Global Natural Product Social molecular networking (GNPS), herein named ecteinamines A-Q (1-17). The ecteinamines possess an unprecedented skeleton housing a host of uncommon functionalities including a menaquinone pathway-derived 2-naphthoate moiety, 4-methyloxazoline, the first example of a naturally occurring Ψ[CH2NH] "reduced amide", a methylsulfinyl moiety, and a d-cysteinyl residue that appears to derive from a unique noncanonical epimerase domain. Extensive in silico analysis of the ecteinamine (ect) biosynthetic gene cluster and stable isotope-feeding experiments helped illuminate the novel enzymology driving ecteinamine assembly as well the role of cluster collaborations or "duets" in producing such structurally complex agents. Finally, ecteinamines were found to bind nickel, cobalt, zinc, and copper, suggesting a possible biological role as broad-spectrum metallophores.

NPOmix: A machine learning classifier to connect mass spectrometry fragmentation data to biosynthetic gene clusters

Microbial specialized metabolites are an important source of and inspiration for many pharmaceuticals, biotechnological products and play key roles in ecological processes. Untargeted metabolomics using liquid chromatography coupled with tandem mass spectrometry is an efficient technique to access metabolites from fractions and even environmental crude extracts. Nevertheless, metabolomics is limited in predicting structures or bioactivities for cryptic metabolites. Efficiently linking the biosynthetic potential inferred from (meta)genomics to the specialized metabolome would accelerate drug discovery programs by allowing metabolomics to make use of genetic predictions. Here, we present a k-nearest neighbor classifier to systematically connect mass spectrometry fragmentation spectra to their corresponding biosynthetic gene clusters (independent of their chemical class). Our new pattern-based genome mining pipeline links biosynthetic genes to metabolites that they encode for, as detected via mass spectrometry from bacterial cultures or environmental microbiomes. Using paired datasets that include validated genes-mass spectral links from the Paired Omics Data Platform, we demonstrate this approach by automatically linking 18 previously known mass spectra (17 for which the biosynthesis gene clusters can be found at the MIBiG database plus palmyramide A) to their corresponding previously experimentally validated biosynthetic genes (e.g., via nuclear magnetic resonance or genetic engineering). We illustrated a computational example of how to use our Natural Products Mixed Omics (NPOmix) tool for siderophore mining that can be reproduced by the users. We conclude that NPOmix minimizes the need for culturing (it worked well on microbiomes) and facilitates specialized metabolite prioritization based on integrative omics mining.

Identification of the Biosynthetic Gene Cluster for Pyracrimycin A, an Antibiotic Produced by Streptomyces sp

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

Integrated Metabolomic-Genomic Workflows Accelerate Microbial Natural Product Discovery

The pairing of analytical chemistry with genomic techniques represents a new wave in natural product chemistry. With an increase in the availability of sequencing and assembly of microbial genomes, interrogation into the biosynthetic capability of producers with valuable secondary metabolites is possible. However, without the development of robust, accessible, and medium to high throughput tools, the bottleneck in pairing metabolic potential and compound isolation will continue. Several innovative approaches have proven useful in the nascent stages of microbial genome-informed drug discovery. Here, we consider a number of these approaches which have led to prioritization of strain targets and have mitigated rediscovery rates. Likewise, we discuss integration of principles of comparative evolutionary studies and retrobiosynthetic predictions to better understand biosynthetic mechanistic details and link genome sequence to structure. Lastly, we discuss advances in engineering, chemistry, and molecular networking and other computational approaches that are accelerating progress in the field of omic-informed natural product drug discovery. Together, these strategies enhance the synergy between cutting edge omics, chemical characterization, and computational technologies that pitch the discovery of natural products with pharmaceutical and other potential applications to the crest of the wave where progress is ripe for rapid advances.

Chemometrics and genome mining reveal an unprecedented family of sugar acid-containing fungal nonribosomal cyclodepsipeptides

Xylomyrocins, a unique group of nonribosomal peptide secondary metabolites, were discovered in Paramyrothecium and Colletotrichum spp. fungi by employing a combination of high-resolution tandem mass spectrometry (HRMS/MS)-based chemometrics, comparative genome mining, gene disruption, stable isotope feeding, and chemical complementation techniques. These polyol cyclodepsipeptides all feature an unprecedented d-xylonic acid moiety as part of their macrocyclic scaffold. This biosynthon is derived from d-xylose supplied by xylooligosaccharide catabolic enzymes encoded in the xylomyrocin biosynthetic gene cluster, revealing a novel link between carbohydrate catabolism and nonribosomal peptide biosynthesis. Xylomyrocins from different fungal isolates differ in the number and nature of their amino acid building blocks that are nevertheless incorporated by orthologous nonribosomal peptide synthetase (NRPS) enzymes. Another source of structural diversity is the variable choice of the nucleophile for intramolecular macrocyclic ester formation during xylomyrocin chain termination. This nucleophile is selected from the multiple available alcohol functionalities of the polyol moiety, revealing a surprising polyspecificity for the NRPS terminal condensation domain. Some xylomyrocin congeners also feature N-methylated amino acid residues in positions where the corresponding NRPS modules lack N-methyltransferase (M) domains, providing a rare example of promiscuous methylation in the context of an NRPS with an otherwise canonical, collinear biosynthetic program.

Biological Dark Matter Exploration using Data Mining for the Discovery of Antimicrobial Natural Products

The discovery of novel antimicrobials has significantly slowed down over the last three decades. At the same time, humans rely increasingly on antimicrobials because of the progressive antimicrobial resistance in medical practices, human communities, and the environment. Data mining is currently considered a promising option in the discovery of new antibiotics. Some of the advantages of data mining are the ability to predict chemical structures from sequence data, anticipation of the presence of novel metabolites, the understanding of gene evolution, and the corroboration of data from multiple omics technologies. This review analyzes the state-of-the-art for data mining in the fields of bacteria, fungi, and plant genomic data, as well as metabologenomics. It also summarizes some of the most recent research accomplishments in the field, all pinpointing to innovation through uncovering and implementing the next generation of antimicrobials.

Genomic and Chemical Decryption of the Bacteroidetes Phylum for Its Potential to Biosynthesize Natural Products

With progress in genome sequencing and data sharing, 1,000s of bacterial genomes are publicly available. Genome mining-using bioinformatics tools in terms of biosynthetic gene cluster (BGC) identification, analysis, and rating-has become a key technology to explore the capabilities for natural product (NP) biosynthesis. Comprehensively, analyzing the genetic potential of the phylum Bacteroidetes revealed Chitinophaga as the most talented genus in terms of BGC abundance and diversity. Guided by the computational predictions, we conducted a metabolomics and bioactivity driven NP discovery program on 25 Chitinophaga strains. High numbers of strain-specific metabolite buckets confirmed the upfront predicted biosynthetic potential and revealed a tremendous uncharted chemical space. Mining this data set, we isolated the new iron chelating nonribosomally synthesized cyclic tetradeca- and pentadecalipodepsipeptide antibiotics chitinopeptins with activity against Candida, produced by C. eiseniae DSM 22224 and C. flava KCTC 62435, respectively. IMPORTANCE The development of pipelines for anti-infectives to be applied in plant, animal, and human health management are dried up. However, the resistance development against compounds in use calls for new lead structures. To fill this gap and to enhance the probability of success for the discovery of new bioactive natural products, microbial taxa currently underinvestigated must be mined. This study investigates the potential within the bacterial phylum Bacteroidetes. A combination of omics-technologies revealed taxonomical hot spots for specialized metabolites. Genome- and metabolome-based analyses showed that the phylum covers a new chemical space compared with classic natural product producers. Members of the Bacteroidetes may thus present a promising bioresource for future screening and isolation campaigns.

Strategies to access biosynthetic novelty in bacterial genomes for drug discovery

Bacteria provide a rich source of natural products with potential therapeutic applications, such as novel antibiotic classes or anticancer drugs. Bioactivity-guided screening of bacterial extracts and characterization of biosynthetic pathways for drug discovery is now complemented by the availability of large (meta)genomic collections, placing researchers into the postgenomic, big-data era. The progress in next-generation sequencing and the rise of powerful computational tools provide unprecedented insights into unexplored taxa, ecological niches and 'biosynthetic dark matter', revealing diverse and chemically distinct natural products in previously unstudied bacteria. In this Review, we discuss such sources of new chemical entities and the implications for drug discovery with a particular focus on the strategies that have emerged in recent years to identify and access novelty.

The application of C–H bond functionalization in the total syntheses of indole natural products

The recent advances in total synthesis of indole natural products focusing on the application of C–H bond functionalization are summarized.

Reactivity-based screening for natural product discovery

Natural products have traditionally been a fruitful source of chemical matter that has been developed into novel therapeutics. Actinomycetes and several other bacterial taxa are especially gifted in biosynthesizing natural products. However, many decades of intense bioactivity-based screening led to a large rediscovery problem, rendering industrial natural product discovery pipelines uneconomical. Numerous methods for circumventing the rediscovery problem have been developed, among them various chemistry-focused strategies, including reactivity-based screening. Emerging from the field of chemical proteomics, reactivity-based screening relies on a reactive probe that chemoselectively modifies a functional group of interest in the context of a complex biological sample. Reactivity-based probes for several distinct functional groups have been deployed to discover new polyketide and peptidic natural products. This chapter describes the protocols to conduct a reactivity-based screening campaign, including bacteria cultivation and screening of cellular extracts with phenylglyoxal-, tetrazine-, thiol-, and aminooxy-functionalized probes, which respectively target primary uriedo, electron-rich olefins, Michael acceptors, and reactive carbonyls. In addition, a recent case study is presented that employs reactivity-based screening as a component of a forward genetics screen to identify a previously unknown peptidyl arginine deiminase. We anticipate that these methods will be useful for those interested in discovering natural products that evade detection by traditional, bioassay-guided methods and others who wish to rapidly connect metabolic chemotype with genotype.

Genome mining for drug discovery: progress at the front end

Microbial genome mining for drug discovery and development has been accelerating in recent years, driven by technical advancements in genome sequencing, bioinformatics, metabolomics/metabologenomics, and synthetic biology. Microbial genome mining is a multistep process that starts with the sequencing of microbes that encode multiple secondary metabolites and identifying new and novel secondary metabolite biosynthetic gene clusters (BGCs) to pursue. The initial steps in the process are critical for the overall success, and they encompass the most innovative new technologies to revitalize natural product discovery. As microbial genome mining has matured in recent years, unvalidated conjectures about what microbes to pursue, how to identify legitimate secondary metabolite BGCs, and how to sequence DNA to satisfactory levels of completion have been identified. The solutions to correct the misconceptions around these topics are beginning to be implemented.

Enzymatic Cβ-H Functionalization of l-Arg and l-Leu in Nonribosomally Derived Peptidyl Natural Products: A Tale of Two Oxidoreductases

Muraymycins are peptidyl nucleoside antibiotics that contain two C_β-modified amino acids, (2S,3S)-capreomycidine and (2S,3S)-β-OH-Leu. The former is also a component of chymostatins, which are aldehyde-containing peptidic protease inhibitors that─like muraymycin─are derived from nonribosomal peptide synthetases (NRPSs). Using feeding experiments and in vitro characterization of 12 recombinant proteins, the biosynthetic mechanism for both nonproteinogenic amino acids is now defined. The formation of (2S,3S)-capreomycidine is shown to involve an FAD-dependent dehydrogenase:cyclase that requires an NRPS-bound pathway intermediate as a substrate. This cryptic dehydrogenation strategy is both temporally and mechanistically distinct in comparison to the biosynthesis of other capreomycidine diastereomers, which has previously been shown to proceed by C_β-hydroxylation of free l-Arg catalyzed by a member of the nonheme Fe²⁺- and α-ketoglutarate (αKG)-dependent dioxygenase family and (eventually) a dehydration-mediated cyclization process catalyzed by a distinct enzyme(s). Contrary to our initial expectation, the sole nonheme Fe²⁺- and αKG-dependent dioxygenase candidate Mur15 encoded within the muraymycin gene cluster is instead demonstrated to catalyze specific C_β hydroxylation of the Leu residue to generate (2S,3S)-β-OH-Leu that is found in most muraymycin congeners. Importantly, and in contrast to known l-Arg-C_β-hydroxylases, the Mur15-catalyzed reaction occurs after the NRPS-mediated assembly of the peptide scaffold. This late-stage functionalization affords the opportunity to exploit Mur15 as a biocatalyst, proof of concept of which is provided.

Metabolomics and genomics in natural products research: complementary tools for targeting new chemical entities

Covering: 2010 to 2021Organisms in nature have evolved into proficient synthetic chemists, utilizing specialized enzymatic machinery to biosynthesize an inspiring diversity of secondary metabolites. Often serving to boost competitive advantage for their producers, these secondary metabolites have widespread human impacts as antibiotics, anti-inflammatories, and antifungal drugs. The natural products discovery field has begun a shift away from traditional activity-guided approaches and is beginning to take advantage of increasingly available metabolomics and genomics datasets to explore undiscovered chemical space. Major strides have been made and now enable -omics-informed prioritization of chemical structures for discovery, including the prospect of confidently linking metabolites to their biosynthetic pathways. Over the last decade, more integrated strategies now provide researchers with pipelines for simultaneous identification of expressed secondary metabolites and their biosynthetic machinery. However, continuous collaboration by the natural products community will be required to optimize strategies for effective evaluation of natural product biosynthetic gene clusters to accelerate discovery efforts. Here, we provide an evaluative guide to scientific literature as it relates to studying natural product biosynthesis using genomics, metabolomics, and their integrated datasets. Particular emphasis is placed on the unique insights that can be gained from large-scale integrated strategies, and we provide source organism-specific considerations to evaluate the gaps in our current knowledge.

Mining genomes to illuminate the specialized chemistry of life

All organisms produce specialized organic molecules, ranging from small volatile chemicals to large gene-encoded peptides, that have evolved to provide them with diverse cellular and ecological functions. As natural products, they are broadly applied in medicine, agriculture and nutrition. The rapid accumulation of genomic information has revealed that the metabolic capacity of virtually all organisms is vastly underappreciated. Pioneered mainly in bacteria and fungi, genome mining technologies are accelerating metabolite discovery. Recent efforts are now being expanded to all life forms, including protists, plants and animals, and new integrative omics technologies are enabling the increasingly effective mining of this molecular diversity.

Genome Analysis of Streptomyces nojiriensis JCM 3382 and Distribution of Gene Clusters for Three Antibiotics and an Azasugar across the Genus Streptomyces

Streptomyces spp. have been major contributors of novel natural products that are used in many application areas. We found that the nojirimycin (NJ) producer JCM 3382 has antimicrobial activity against Staphylococcus aureus via cellular degradation. Genome analysis revealed 30 biosynthetic gene clusters, including those responsible for producing antibiotics, including an azasugar NJ. In-depth MS/MS analysis confirmed the production of 1-deoxynojirimycin (DNJ) along with NJ. In addition, the production of tambromycins, setomimycin, and linearmycins was verified by spectroscopic analyses, including LC-MS and NMR. The distribution of the clusters of genes coding for antibiotics in 2061 Streptomyces genomes suggested potential producers of tambromycin, setomimycin, and linearmycin. For a DNJ gene cluster, homologs of gabT1 and gutB1 were commonly found; however, yktC1 was identified in only 112 genomes. The presence of several types of clusters suggests that different strains may produce different types of azasugars. Chemical-profile-inspired comparative genome analysis may facilitate a more accurate assessment of the biosynthetic potential to produce secondary metabolites.

The copper-catalyzed synthesis of dihydrooxazoles from α,β-unsaturated ketoximes and activated ketones

The first copper-catalyzed [3+2]-type condensation reaction of α,β-unsaturated ketoximes with activated ketones has been described for the synthesis of dihydrooxazoles, especially trifluoromethyl-decorated dihydrooxazoles. Notable features of this method include its broad substrate scope, good functional group tolerance, and simple operation.

Ranking microbial metabolomic and genomic links in the NPLinker framework using complementary scoring functions

Specialised metabolites from microbial sources are well-known for their wide range of biomedical applications, particularly as antibiotics. When mining paired genomic and metabolomic data sets for novel specialised metabolites, establishing links between Biosynthetic Gene Clusters (BGCs) and metabolites represents a promising way of finding such novel chemistry. However, due to the lack of detailed biosynthetic knowledge for the majority of predicted BGCs, and the large number of possible combinations, this is not a simple task. This problem is becoming ever more pressing with the increased availability of paired omics data sets. Current tools are not effective at identifying valid links automatically, and manual verification is a considerable bottleneck in natural product research. We demonstrate that using multiple link-scoring functions together makes it easier to prioritise true links relative to others. Based on standardising a commonly used score, we introduce a new, more effective score, and introduce a novel score using an Input-Output Kernel Regression approach. Finally, we present NPLinker, a software framework to link genomic and metabolomic data. Results are verified using publicly available data sets that include validated links.

An interpreted atlas of biosynthetic gene clusters from 1,000 fungal genomes

Fungi are prolific producers of natural products, compounds which have had a large societal impact as pharmaceuticals, mycotoxins, and agrochemicals. Despite the availability of over 1,000 fungal genomes and several decades of compound discovery efforts from fungi, the biosynthetic gene clusters (BGCs) encoded by these genomes and the associated chemical space have yet to be analyzed systematically. Here, we provide detailed annotation and analyses of fungal biosynthetic and chemical space to enable genome mining and discovery of fungal natural products. Using 1,037 genomes from species across the fungal kingdom (e.g., Ascomycota, Basidiomycota, and non-Dikarya taxa), 36,399 predicted BGCs were organized into a network of 12,067 gene cluster families (GCFs). Anchoring these GCFs with reference BGCs enabled automated annotation of 2,026 BGCs with predicted metabolite scaffolds. We performed parallel analyses of the chemical repertoire of fungi, organizing 15,213 fungal compounds into 2,945 molecular families (MFs). The taxonomic landscape of fungal GCFs is largely species specific, though select families such as the equisetin GCF are present across vast phylogenetic distances with parallel diversifications in the GCF and MF. We compare these fungal datasets with a set of 5,453 bacterial genomes and their BGCs and 9,382 bacterial compounds, revealing dramatic differences between bacterial and fungal biosynthetic logic and chemical space. These genomics and cheminformatics analyses reveal the large extent to which fungal and bacterial sources represent distinct compound reservoirs. With a >10-fold increase in the number of interpreted strains and annotated BGCs, this work better regularizes the biosynthetic potential of fungi for rational compound discovery.

Recent Advances in Discovery of Lead Structures from Microbial Natural Products: Genomics- and Metabolomics-Guided Acceleration

Natural products (NPs) are evolutionarily optimized as drug-like molecules and remain the most consistently successful source of drugs and drug leads. They offer major opportunities for finding novel lead structures that are active against a broad spectrum of assay targets, particularly those from secondary metabolites of microbial origin. Due to traditional discovery approaches’ limitations relying on untargeted screening methods, there is a growing trend to employ unconventional secondary metabolomics techniques. Aided by the more in-depth understanding of different biosynthetic pathways and the technological advancement in analytical instrumentation, the development of new methodologies provides an alternative that can accelerate discoveries of new lead-structures of natural origin. This present mini-review briefly discusses selected examples regarding advancements in bioinformatics and genomics (focusing on genome mining and metagenomics approaches), as well as bioanalytics (mass-spectrometry) towards the microbial NPs-based drug discovery and development. The selected recent discoveries from 2015 to 2020 are featured herein.

Synthetic biology-inspired strategies and tools for engineering of microbial natural product biosynthetic pathways

Microbial-derived natural products (NPs) and their derivative products are of great importance and used widely in many fields, especially in pharmaceutical industries. However, there is an immediate need to establish innovative approaches, strategies, and techniques to discover new NPs with novel or enhanced biological properties, due to the less productivity and higher cost on traditional drug discovery pipelines from natural bioresources. Revealing of untapped microbial cryptic biosynthetic gene clusters (BGCs) using DNA sequencing technology and bioinformatics tools makes genome mining possible for NP discovery from microorganisms. Meanwhile, new approaches and strategies in the area of synthetic biology offer great potentials for generation of new NPs by engineering or creating synthetic systems with improved and desired functions. Development of approaches, strategies and tools in synthetic biology can facilitate not only exploration and enhancement in supply, and also in the structural diversification of NPs. Here, we discussed recent advances in synthetic biology-inspired strategies, including bioinformatics and genetic engineering tools and approaches for identification, cloning, editing/refactoring of candidate biosynthetic pathways, construction of heterologous expression hosts, fitness optimization between target pathways and hosts and detection of NP production.

A community resource for paired genomic and metabolomic data mining

Genomics and metabolomics are widely used to explore specialized metabolite diversity. The Paired Omics Data Platform is a community initiative to systematically document links between metabolome and (meta)genome data, aiding identification of natural product biosynthetic origins and metabolite structures.

Synthetic studies toward inducamide C

The alkaloid inducamide C is proposed to contain a very rare benzoxazepine ring. Herein, we report that the benzoxazepine ring in inducamide C is unstable and prone to rearrangement, indicating that structural revision of the natural product may be necessary. In a first-generation synthetic approach, attempts to assemble the benzoxazepine by cyclization of 4-hydroxyinducamide A led to the regioisomeric oxepanoindole, a result of the 4-hydroxyindole (C4-OH) undergoing preferential cyclization instead of the desired chlorosalicylic acid C15-OH. A second-generation approach involved dealkylation of O-isopropylinducamide C, but the same oxepanoindole formed via rearrangement of the proposed inducamide C structure. Computational studies validate preferential formation of the oxepanoindole and the lactone in O-isopropylinducamide C is susceptible to nucleophilic attack. Thus, inducamide C is either highly unstable or in need of structural revision.

Combining NMR-Based Metabolic Profiling and Genome Mining for the Accelerated Discovery of Archangiumide, an Allenic Macrolide from the Myxobacterium Archangium violaceum SDU8

An unprecedented 19-membered allenic macrolide archangiumide (1) was discovered from the myxobacterium Archangium violaceum SDU8 by integrating NMR-based metabolic profiling and genome mining. Its biosynthesis pathway was proposed based on the architectural analysis of the encoding trans-AT PKS genes and validated by isotope labeling. The methodology of combing 2D NMR-based metabolic profiling and bioinformatics-aided structure prediction, as exemplified by this study, is anticipated to improve discovery efficiency of a broader range of microbial "dark matter".

Coupling Mass Spectral and Genomic Information to Improve Bacterial Natural Product Discovery Workflows

Bacterial natural products possess potent bioactivities and high structural diversity and are typically encoded in biosynthetic gene clusters. Traditional natural product discovery approaches rely on UV- and bioassay-guided fractionation and are limited in terms of dereplication. Recent advances in mass spectrometry, sequencing and bioinformatics have led to large-scale accumulation of genomic and mass spectral data that is increasingly used for signature-based or correlation-based mass spectrometry genome mining approaches that enable rapid linking of metabolomic and genomic information to accelerate and rationalize natural product discovery. In this mini-review, these approaches are presented, and discovery examples provided. Finally, future opportunities and challenges for paired omics-based natural products discovery workflows are discussed.

The need to innovate sample collection and library generation in microbial drug discovery: a focus on academia

The question of whether culturable microorganisms will continue to be a viable source of new drug leads is inherently married to the strategies used to collect samples from the environment, the methods used to cultivate microorganisms from these samples, and the processes used to create microbial libraries. An academic microbial natural products (NP) drug discovery program with the latest innovative chromatographic and spectroscopic technology, high-throughput capacity, and bioassays will remain at the mercy of the quality of its microorganism source library. This viewpoint will discuss limitations of sample collection and microbial strain library generation practices. Additionally, it will offer suggestions to innovate these areas, particularly through the targeted cultivation of several understudied bacterial phyla and the untargeted use of mass spectrometry and bioinformatics to generate diverse microbial libraries. Such innovations have potential to impact downstream therapeutic discovery, and make its front end more informed, efficient, and less reliant on serendipity. This viewpoint is not intended to be a comprehensive review of contributing literature and was written with a focus on bacteria. Strategies to discover NPs from microbial libraries, including a variety of genomics and "OSMAC" style approaches, are considered downstream of sample collection and library creation, and thus are out of the scope of this viewpoint.

Reinvigorating the Chiral Pool: Chemoenzymatic Approaches to Complex Peptides and Terpenoids

Biocatalytic transformations that leverage the selectivity and efficiency of enzymes represent powerful tools for the construction of complex natural products. Enabled by innovations in genome mining, bioinformatics, and enzyme engineering, synthetic chemists are now more than ever able to develop and employ enzymes to solve outstanding chemical problems, one of which is the reliable and facile generation of stereochemistry within natural product scaffolds. In recognition of this unmet need, our group has sought to advance novel chemoenzymatic strategies to both expand and reinvigorate the chiral pool. Broadly defined, the chiral pool comprises cheap, enantiopure feedstock chemicals that serve as popular foundations for asymmetric total synthesis. Among these building blocks, amino acids and enantiopure terpenes, whose core structures can be mapped onto several classes of structurally and pharmaceutically intriguing natural products, are of particular interest to the synthetic community.In this Account, we summarize recent efforts from our group in leveraging biocatalytic transformations to expand the chiral pool, as well as efforts toward the efficient application of these transformations in natural products total synthesis, the ultimate testing ground for any novel methodology. First, we describe several examples of enzymatic generation of noncanonical amino acids as means to simplify the synthesis of peptide natural products. By extracting amino acid hydroxylases from native biosynthetic pathways, we obtain efficient access to hydroxylated variants of proline, lysine, arginine, and their derivatives. The newly installed hydroxyl moiety then becomes a chemical handle that can facilitate additional complexity generation, thereby expanding the pool of amino acid-derived building blocks available for peptide synthesis. Next, we present our efforts in enzymatic C-H oxidations of diverse terpene scaffolds, in which traditional chemistry can be combined with strategic applications of biocatalysis to selectively and efficiently derivatize several commercial terpenoid skeletons. The synergistic logic of this approach enables a small handful of synthetic intermediates to provide access to a plethora of terpenoid natural product families. Taken together, these findings demonstrate the advantages of applying enzymes in total synthesis in conjunction with established methodologies, as well as toward the expansion of the chiral pool to enable facile incorporation of stereochemistry during synthetic campaigns.

Comparative Metabologenomics Analysis of Polar Actinomycetes

Biosynthetic and chemical datasets are the two major pillars for microbial drug discovery in the omics era. Despite the advancement of analysis tools and platforms for multi-strain metabolomics and genomics, linking these information sources remains a considerable bottleneck in strain prioritisation and natural product discovery. In this study, molecular networking of the 100 metabolite extracts derived from applying the OSMAC approach to 25 Polar bacterial strains, showed growth media specificity and potential chemical novelty was suggested. Moreover, the metabolite extracts were screened for antibacterial activity and promising selective bioactivity against drug-persistent pathogens such as Klebsiella pneumoniae and Acinetobacter baumannii was observed. Genome sequencing data were combined with metabolomics experiments in the recently developed computational approach, NPLinker, which was used to link BGC and molecular features to prioritise strains for further investigation based on biosynthetic and chemical information. Herein, we putatively identified the known metabolites ectoine and chrloramphenicol which, through NPLinker, were linked to their associated BGCs. The metabologenomics approach followed in this study can potentially be applied to any large microbial datasets for accelerating the discovery of new (bioactive) specialised metabolites.

BiG-SLiCE: A highly scalable tool maps the diversity of 1.2 million biosynthetic gene clusters

BACKGROUND

Genome mining for biosynthetic gene clusters (BGCs) has become an integral part of natural product discovery. The >200,000 microbial genomes now publicly available hold information on abundant novel chemistry. One way to navigate this vast genomic diversity is through comparative analysis of homologous BGCs, which allows identification of cross-species patterns that can be matched to the presence of metabolites or biological activities. However, current tools are hindered by a bottleneck caused by the expensive network-based approach used to group these BGCs into gene cluster families (GCFs).

RESULTS

Here, we introduce BiG-SLiCE, a tool designed to cluster massive numbers of BGCs. By representing them in Euclidean space, BiG-SLiCE can group BGCs into GCFs in a non-pairwise, near-linear fashion. We used BiG-SLiCE to analyze 1,225,071 BGCs collected from 209,206 publicly available microbial genomes and metagenome-assembled genomes within 10 days on a typical 36-core CPU server. We demonstrate the utility of such analyses by reconstructing a global map of secondary metabolic diversity across taxonomy to identify uncharted biosynthetic potential. BiG-SLiCE also provides a "query mode" that can efficiently place newly sequenced BGCs into previously computed GCFs, plus a powerful output visualization engine that facilitates user-friendly data exploration.

CONCLUSIONS

BiG-SLiCE opens up new possibilities to accelerate natural product discovery and offers a first step towards constructing a global and searchable interconnected network of BGCs. As more genomes are sequenced from understudied taxa, more information can be mined to highlight their potentially novel chemistry. BiG-SLiCE is available via https://github.com/medema-group/bigslice.

BiG-FAM: the biosynthetic gene cluster families database

Computational analysis of biosynthetic gene clusters (BGCs) has revolutionized natural product discovery by enabling the rapid investigation of secondary metabolic potential within microbial genome sequences. Grouping homologous BGCs into Gene Cluster Families (GCFs) facilitates mapping their architectural and taxonomic diversity and provides insights into the novelty of putative BGCs, through dereplication with BGCs of known function. While multiple databases exist for exploring BGCs from publicly available data, no public resources exist that focus on GCF relationships. Here, we present BiG-FAM, a database of 29,955 GCFs capturing the global diversity of 1,225,071 BGCs predicted from 209,206 publicly available microbial genomes and metagenome-assembled genomes (MAGs). The database offers rich functionalities, such as multi-criterion GCF searches, direct links to BGC databases such as antiSMASH-DB, and rapid GCF annotation of user-supplied BGCs from antiSMASH results. BiG-FAM can be accessed online at https://bigfam.bioinformatics.nl.

Facile synthesis of 4-acetoxyindoles via PhI(OAc)2-mediated dearomatization of 2-alkynylanilines

A simple process for rapid synthesis of 4-aetoxyindoles from readily available 2-alkynylanilines via dearomatization was reported.

Microbial natural product databases: moving forward in the multi-omics era

Covering: 2010-2020The digital revolution is driving significant changes in how people store, distribute, and use information. With the advent of new technologies around linked data, machine learning and large-scale network inference, the natural products research field is beginning to embrace real-time sharing and large-scale analysis of digitized experimental data. Databases play a key role in this, as they allow systematic annotation and storage of data for both basic and advanced applications. The quality of the content, structure, and accessibility of these databases all contribute to their usefulness for the scientific community in practice. This review covers the development of databases relevant for microbial natural product discovery during the past decade (2010-2020), including repositories of chemical structures/properties, metabolomics, and genomic data (biosynthetic gene clusters). It provides an overview of the most important databases and their functionalities, highlights some early meta-analyses using such databases, and discusses basic principles to enable widespread interoperability between databases. Furthermore, it points out conceptual and practical challenges in the curation and usage of natural products databases. Finally, the review closes with a discussion of key action points required for the field moving forward, not only for database developers but for any scientist active in the field.

Exploration of Iron- and a-Ketoglutarate-Dependent Dioxygenases as Practical Biocatalysts in Natural Product Synthesis

Catalytic C─H oxidation is a powerful transformation with enormous promise to streamline access to complex molecules. In recent years, biocatalytic C─H oxidation strategies have received tremendous attention due to their potential to address unmet regio- and stereoselectivity challenges that are often encountered with the use of small-molecule-based catalysts. This Account provides an overview of recent contributions from our laboratory in this area, specifically in the use of iron- and α-ketoglutarate-dependent dioxygenases in the chemoenzymatic synthesis of complex natural products.

Copper-catalyzed regioselective [3+2] annulation of malonate-tethered acyl oximes with isatins

A copper-catalyzed regioselective [3+2] annulation of malonate-tethered acyl oximes with isatins was developed, affording valuable 2,3-dihydrooxazole-spirooxindoles in moderate to good yields with excellent diastereoselectivity.