Systematic genome mining has revealed that microbes encode numerous uncharacterized secondary metabolite biosynthetic gene clusters (BGCs). The efficient and selective activation of these silent or cryptic BGCs is crucial for the high-throughput discovery of novel natural products. Recent influential studies have demonstrated that using small chemical elicitors is a practical and cost-effective method to unlock the secondary metabolic potential of microbes. However, the current approach mainly relies on high-throughput, non-targeted screening methods to discover chemical elicitors capable of activating these silent BGCs. Therefore, this study comprehensively reviews reported cases of small molecules that activate silent BGCs, covering the chemical structures of elicitors, resulting natural products, and target BGCs, thereby constructing an integrated knowledge graph. We also summarize the underlying activation mechanisms. Leveraging relationships captured in this graph, we outline directions for targeted activation of silent pathways using small molecules, thereby facilitating more efficient natural product discovery.

Gut microbial β-glucuronidase has attracted attention as a potential therapeutic target for mitigating irinotecan (CPT-11)-induced late-onset diarrhea and intestinal injury. Here, we designed and synthesized 60 thioester derivatives bearing a 1,3,4-thiadiazole moiety and evaluated their biological activities. In vitro, 3-5B (IC50 = 0.39 μM) exhibited potent inhibitory activity against Escherichia coliβ-glucuronidase (EcGUS). Kinetic studies showed that 3-5B acts as an uncompetitive EcGUS inhibitor (Ki = 0.60 μM). Furthermore, molecular docking analysis suggested that 3-5B binds EcGUS with high affinity through interactions with residues Asp163, Glu413, and Trp549. Notably, 3-5B showed no inhibitory effect on bovine liver β-glucuronidase, did not inhibit E. coli growth, and exhibited low cytotoxicity toward human Caco-2 cells. In vivo, oral administration of 3-5B (1 mg/kg/d) attenuated CPT-11-induced gastrointestinal toxicity in BALB/cJ mice, including weight loss, diarrhea, and colonic injury. Collectively, these findings identify 3-5B as a promising lead scaffold for the development of EcGUS inhibitors.

Aspergillus fungi are rich in secondary metabolites, yet their biosynthetic potential remains underexplored. Nonribosomal peptides (NRPs), an important class of natural products with diverse biological activities, are synthesized by nonribosomal peptide synthetases (NRPSs). In this study, we performed a large-scale bioinformatic analysis of NRPS adenylation (A) domains from 1,162 Aspergillus genomes to predict their substrate specificities. Using AdenylPred, we analyzed 40,684 A-domain sequences and found that large phenyl-derivative amino acids and small hydrophobic amino acids are the predominant substrates, accounting for 66

Objectives This study aimed to investigate the composition and alterations of gut microbiota in mice with comorbid diabetes and periodontitis. Materials and methods A total of 40 six-week-old male db/db and db/m mice were divided into four groups ( n = 10 per group): healthy control group, periodontitis group, diabetes group, and diabetes with periodontitis group. Fasting blood glucose (FBG) and oral glucose tolerance test (OGTT) were measured in all four groups to confirm the reliability of the diabetes model. Periodontitis was induced by silk thread ligation, and alveolar bone resorption was assessed using micro-computed tomography (Micro-CT) scanning. Fecal samples were collected from the four groups of mice, and the composition and structure of the gut microbiota were analyzed using 16S ribosomal DNA (16S rDNA) sequencing. Simultaneously, blood samples were collected and centrifuged to obtain serum for the measurement of blood biochemical parameters and inflammatory cytokines. Results Compared with the db/m group, the db/m + PD group showed no significant differences in fasting blood glucose and the area under the oral glucose tolerance test curve (AUC). In contrast, compared with the db/db group, the db/db + PD group exhibited significantly elevated fasting blood glucose and the area under the oral glucose tolerance test curve. Relative to the db/m group, the db/m + PD group demonstrated a significant increase in C-reactive protein (CRP). Compared with the db/db group, the db/db + PD group showed high density lipoprotein cholesterol (HDL-C) significantly reduced and Interleukin-10 (IL-10) significantly increased. Pancreatic histology revealed marked islet morphological alterations in both db/db and db/db + PD groups. Significant alveolar bone loss was observed in db/m + PD and db/db + PD groups, with pronounced inflammatory infiltration on periodontal histology, most severe in db/db + PD group. Alpha and beta diversity analyses indicated notable changes in microbial richness and community structure across the four groups. The gut microbiota dysbiosis index was significantly higher in the three experimental groups than in the db/m group. Intergroup comparisons revealed extensive compositional differences at both phylum and genus levels. Conclusion Our results showed that the degree of alveolar bone resorption and the microbial dysbiosis index (MDI) in the db/db + PD group were significantly higher than those in the db/m, db/m + PD, and db/db groups. Meanwhile, the destruction of pancreatic β cells and periodontal tissues was most severe in the db/db + PD group, and the structure and richness of the gut microbiota in this group were also markedly different from those in the other three groups. Diabetes and periodontitis exerted synergistic effects, inducing and exacerbating gut microbiota dysbiosis in mice, which was closely associated with abnormal blood glucose levels, periodontal inflammation, and alveolar bone resorption. These findings suggest that the gut microbiota may serve as a critical target mediating the bidirectional interaction between diabetes and periodontitis.

Metallophores are metal-chelating natural products that enable microorganisms to acquire essential metal ions and mediate processes such as iron uptake, quorum sensing, and interspecies competition. Metallophores also display potent antimicrobial and anticancer activities, highlighting their biomedical and biotechnological potential. Despite Streptomyces being prolific producers of bioactive metabolites, their metallophore pathways remain largely unexplored. Here, we systematically mined 519 reference Streptomyces genomes to elucidate the distribution, diversity, and structural features of metallophores and identified a new metallophore biosynthetic gene cluster (BGC) (ser) from sponge-derived Streptomyces sp. HB-R818. Using a metabologenomics-based strategy, five new siderophore analogs serobactins A-E (1-5) and known enterobactin (6) were isolated. These compounds show potential to inhibit tumor invasion and feature a unique dibenzo-α-pyrone scaffold in structure, formed through the cyclization of an extra 2,3-dihydroxybenzoic acid with 2,3-dihydroxybenzoyl serine. The BGC (ser) was validated by the nonribosomal peptide synthetase gene knockout; the biosynthesis of 1-6 was proposed.

The global rise of methicillin-resistant Staphylococcus aureus (MRSA) has highlighted the urgent need for alternative therapeutic strategies beyond conventional bactericidal antibiotics. Targeting bacterial virulence rather than viability represents a promising approach to mitigate selective pressure and delay resistance development. Sortase A (SrtA), a membrane-associated transpeptidase responsible for anchoring virulence-associated surface proteins, is an attractive anti-virulence target due to its non-essential role in bacterial survival. Here, we report a machine learning-guided strategy for the discovery of novel covalent SrtA inhibitors based on a 1,2-benzoselenazol-3-one (BSEA) scaffold featuring a tunable electrophilic Se-N bond. A scaffold-aware classification model with a Tanimoto similarity constraint trained on 529 SrtA inhibitors enabled prospective virtual screening of over 35,000 BSEA and BTA derivatives, leading to a high hit rate of 89% upon experimental validation. Representative compounds exhibited submicromolar SrtA inhibition (IC50 = 0.84-1.04 μM) while showing minimal effects on bacterial growth (MIC = 8-32 μM), indicating effective functional decoupling of virulence and viability. Mechanistic studies demonstrated time-dependent irreversible inhibition kinetics, supported by jump dilution assays and Nano-LC-MS/MS identification of covalent modification at the catalytic residue Cys184. These inhibitors effectively disrupted MRSA biofilm formation at sub-inhibitory concentrations and significantly improved host survival in a Galleria mellonella infection model. Collectively, this study establishes a data-driven framework integrating machine learning and covalent chemistry for anti-virulence drug discovery and provides promising lead compounds targeting SrtA to combat MRSA infections.

Excessive pesticide exposure is increasingly associated with oxidative stress mediated reproductive toxicity. Pyriproxyfen (PPF), a widely used juvenile hormone analog insecticide, has been implicated in oxidative stress mediated reproductive toxicity. This study investigated the mechanisms underlying PPF-induced testicular damage and evaluated the protective potential of Ephedra pachyclada extract (EPE), focusing on modulation of the Nrf2/ARE and NF-κB pathways. Adult male rats were exposed to PPF (20 mg/kg body weight) with or without EPE administration (140 mg/kg body weight). Oxidative stress biomarkers, inflammatory mediators, steroidogenic gene expression, serum testosterone, sperm parameters, and histopathological alterations were assessed. LC-MS analysis characterized the phytochemical composition of EPE. PPF exposure significantly increased lipid peroxidation and reduced antioxidant enzyme activities, accompanied by downregulation of Nrf2 and its downstream targets. In parallel, NF-κB expression and pro-inflammatory cytokines were elevated. These molecular disturbances were associated with suppression of StAR, SR-B1, CYP11A1, 3b-HSD, and 17b-HSD expression, decreased testosterone levels, reduced sperm count and motility, increased sperm abnormalities, and structural degeneration of seminiferous tubules. EPE administration markedly attenuated oxidative stress, restored Nrf2 signaling, suppressed inflammatory responses, normalized steroidogenic gene expression and testosterone levels, and improved sperm quality and testicular histology. LC-MS profiling revealed a predominance of flavonol glycosides, flavone C-glycosides, flavan-3-ols, and biflavonoids. These findings suggest that EPE mitigates PPF-induced reproductive toxicity through restoration of redox and inflammatory homeostasis.

Mangrove sediments harbor highly diverse microbial communities essential to biogeochemical cycling and ecosystem functioning. However, island-scale patterns linking environmental gradients, bacterial diversity, community distribution, and secondary metabolic potential remain unclear. We used high-throughput 16S rRNA amplicon sequencing and sediment physicochemical analyses to investigate bacterial communities across 12 representative mangrove habitats on Hainan Island, China. Bacterial diversity varied substantially among sites (Shannon indices: 5.24–6.98); and community composition was consistently dominated by Proteobacteria, Firmicutes, and Actinobacteria. Electrical conductivity, available nitrogen, and total potassium were identified as the primary environmental variables associated with community structure. Significant correlations between microbial β-diversity and environmental dissimilarity indicated that environmental variation was associated with bacterial community differentiation at the island scale. Biosynthetic gene clusters (BGCs) were predicted based on taxonomic profiles. The predicted biosynthetic landscape revealed spatial differentiation across sampling sites and was classified into nonribosomal peptide, post-translationally modified peptide, and terpene-enriched metabolic types. Environmental factors, including available phosphorus, water content, and pH, were associated with variation in specific predicted BGC classes. This island-scale survey shows that environmental gradients across Hainan Island are associated with bacterial diversity, community distribution, and predicted secondary metabolic potential in mangrove sediments. These findings provide insights into the ecological organization of mangrove sediment microbiomes and highlight mangrove sediments as reservoirs of microbial and predicted biosynthetic diversity.

AIMS:Marine-derived Aspergillus species are prolific producers of bioactive secondary metabolites, yet the majority of their biosynthetic gene clusters (BGCs) remain silent. This study aimed to integrate genome mining with High-throughput Elicitor Screening (HiTES) to unlock the metabolic potential of Aspergillus sp. WHUF0304 and identify elicitors that promote the accumulation of previously undetected metabolites. METHODS AND RESULTS:A high-quality genome of Aspergillus sp. WHUF0304 was assembled and annotated using multiple functional databases, revealing substantial secondary metabolic potential. antiSMASH analysis identified diverse BGCs, including NRPS/indole-related clusters potentially associated with indole diketopiperazine biosynthesis. A HiTES-inspired elicitor screening strategy was then applied to evaluate 42 small molecules for their ability to alter the metabolite profile of this strain. Among the tested elicitors, fluconazole was identified as the optimal inducer, triggering the production of several indole diketopiperazine-related differential metabolites. Subsequent activity-guided isolation led to the identification of a bioactive indole diketopiperazine dimer, cristatumin E, which exhibited antibacterial activity against Escherichia coli and Bacillus subtilis with minimum inhibitory concentrations (MICs) of 32 µg mL-1 and 256 µg mL-1, respectively. CONCLUSIONS:These findings demonstrate that integrating genomic and functional approaches effectively activates silent BGCs in marine fungi. The fluconazole-associated accumulation and subsequent isolation of cristatumin E, a bioactive indole diketopiperazine dimer, highlight the potential of elicitor-mediated activation to expand the detectable metabolite profile of Aspergillus sp. WHUF0304.

Natural products (NPs) and their analogues have long underpinned therapies in humans, animals, and plants health, yet, discovering truly novel scaffolds remains a formidable challenge, even with the enormous diversity offered. Over the last two decades, breakthroughs in bioinformatics, cheminformatics, advanced analytical methods, synthetic biology toolkits, and optimized microbial culture have surmounted many of the bottlenecks that stalled NP research in the 1990s and 2000s. Researchers now deploy innovative extraction and purification protocols alongside high-throughput dereplication tools to fish trace metabolites out of complex matrices. These combined approaches not only enable the discovery and rigorous characterization of biosynthesized metabolites, bio-transformed analogues and new chemical entities but also allow precise tuning of biosynthetic gene clusters (BGCs) and culture conditions- modulation and optimization, dramatically improving yield, scalability, and cost-efficiency. Several of these newly unearthed compounds exhibit unique bioactivities that directly inspire drug-development programs against metabolic disorders, cancer drug resistance, and infectious diseases. In this review, we present an up-to-date, concise roadmap of natural product discovery (NPD), majorly covering strategies for awakening silent BGCs, genome mining, and late-stage diversification systems, and we discuss the current limitations and perspectives of rational NPD.

Non-ribosomal peptides (NRPs) are promising lead compounds for novel antibiotics. Bioinformatic mining of silent microbial NRPS gene clusters provide crucial insights for the discovery and de novo design of bioactive peptides. Here, we describe the efficient discovery and antibacterial evaluation of novel peptides inspired by metabolite scaffolds encoded by NRPS gene clusters from 216,408 bacterial genomes. In total, 335,024 NRPS gene clusters were identified and dereplicated, yielding 328 unique peptide scaffolds. Using deep learning-based scoring, five antimicrobial peptide candidates (P1-P5) were synthesized via solid-phase chemical synthesis. Among them, peptide P2 exhibited potent antibacterial activity with MIC50 values of 1-2 μM against two pathogenic strains. Subsequent amino acid optimization guided by deep learning algorithms produced P2.2, a derivative with significantly enhanced antibacterial activity. Mechanistic studies revealed that P2.2 disrupts bacterial membranes and increases permeability by modulating proteins involved in the type VI and III secretion systems. Furthermore, P2.2 demonstrated synergistic effects when combined with conventional antibiotics and exhibited reduced hemolytic activity, improving its therapeutic potential. These findings underscore the immense potential of deep learning to accelerate the discovery of naturally inspired antimicrobial peptides from silent biosynthetic gene clusters.

Rheum tanguticum plays a key role in treating acute pancreatitis, though its bioactive components and mechanisms remain unclear. This study investigates the structural characteristics and therapeutic effects of rhubarb polysaccharides (RP) in a mouse model of hypertriglyceridemia-induced acute pancreatitis (HTGAP). The isolated polysaccharide, (26 kDa, primarily glucose and arabinose) significantly reduced serum amylase and lipase levels, improved pancreatic and intestinal histopathology and upregulated tight junction proteins Zonula Occludens-1 and Occludin. High throughput 16S rRNA sequencing demonstrated that RP supplementation modulated gut microbiota by enriching beneficial genera (e.g., Lactobacillus and Akkermansia) while reducing pathogenic genera (Lachnoclostridium, Desulfovibrio). Untargeted metabolomics revealed alterations in microbial metabolites, particularly in the tryptophan metabolism. Integrated analysis revealed significant microbiota-metabolite correlations. These findings suggest that RP improve HTGAP by restoring intestinal barrier integrity and regulating gut microbiota-mediated tryptophan metabolism, highlighting its potential as a therapeutic agent.

Bacterial genomes encode numerous cryptic biosynthetic gene clusters (BGCs) that represent an untapped potential source of drugs or biopesticides. However, the limited analysis of BGCs in a specific genus has limited the discovery of their natural products through genome mining. Herein, we report the systematic analysis of the biosynthetic potential of 136 genomes of the Chromobacterium genus. A total of 1713 BGCs were identified and grouped into 190 gene cluster families (GCFs). However, only eight BGCs from seven GCFs have been functionally characterized, highlighting the vast underexplored potential of the genus for the production of novel natural products. Guided by this analysis, we investigated a cryptic BGC of GCF9 featuring a glycosyltransferase and a nonribosomal peptide synthetase (NRPS) with a starter condensation domain, leading to the identification of a class of glycolipopeptides chromorhipeptins from Chromobacterium rhizoryzae. Chromorhipeptins A and B demonstrated broad-spectrum antifungal activity against several plant pathogenic fungi, including Valsa mali, with minimum inhibitory concentrations (MICs) at 0.04 and 0.16 μM, respectively, outperforming the fungicide carbendazim (MIC = 0.78 μM). These findings revealed the biosynthetic potential of Chromobacterium and underscored the power of genome mining to unlock cryptic bacterial natural products for crop protection against plant pathogenic fungi.

Recently, few-shot molecular property prediction (FSMPP) has garnered increasing attention. Despite existing methods that have focused on mining the many-to-many relationships between molecules and properties, they failed to capture the local similarity of molecules and the relative nature of property-property relations, which limits their performance. To this end, this paper proposes a novel meta-learning FSMPP framework (KRGTS), which comprises the Knowledge-enhanced Relation Graph module and the Task Sampling module. The knowledge-enhanced relation graph module captures the local similarity of molecules with molecular substructures to construct the molecule-property multi-relation graph (MPMRG) to capture the many-to-many relationships between molecules and properties. The task sampling module includes a meta-training task sampler and an auxiliary task sampler, which are responsible for scheduling the meta-training process and sampling high-related auxiliary tasks, respectively, thereby achieving efficient meta-knowledge learning and reducing noise introduction. Empirically, extensive experiments on five datasets demonstrate the superiority of KRGTS over 20 state-of-the-art methods.

Gut microbial β-glucuronidase (GUS) plays a key role in metabolizing compounds and influencing disease and drug metabolism, highlighting the need for potent inhibitors to improve drug efficacy and intestinal health. To identify Escherichia coli β-glucuronidase (EcGUS) inhibitors, we designed and synthesized fifty 1,2-benzoselenazol-3-one (BSEA) derivatives using a bioisosterism strategy. Among these, twenty-five BSEA derivatives demonstrated greater inhibitory efficacy than the most potent known EcGUS inhibitor, amoxapine (AMX), with compound 49 showing the strongest activity, achieving an IC50 of 12.9 nM. Structure-inhibitory activity relationship analysis suggested that modifications such as adding benzene rings or nitrogenous heterocycles to the BSEA scaffold enhanced inhibitory activity, influenced by the type and position of substituents. The LC-MS analysis confirmed that compounds 31 and 49 covalently modify Cys197 in EcGUS, and additional covalent linkage of compound 49 was observed on Cys28 and Cys443. In addition, the jump dilution assays proved that compounds 31 was irreversible covalent inhibitors, and its kinetic parameter kinact/KI were determined to be 21292.9 M-1s-1. The compounds 49 was reversible covalent inhibitors and its apparent steady-state inhibition constant Ki∗app were determined to be 23.33 nM. Molecular docking predicted specific interactions, such as hydrogen bonds involving Se and the pyrazole NH of compound 49 with Cys28 and Cys449, which may contribute to its inhibitory action. This study reports the first discovery of covalent inhibitors for EcGUS, with optimized BSEA derivatives acting as novel allosteric covalent inhibitors, revealing structure-activity relationships and molecular determinants that establish their potential in drug development.

Fungal secondary metabolites are considered as important resources for drug discovery. Despite various methods being employed to facilitate the discovery of new fungal secondary metabolites, the trend of identifying novel secondary metabolites from fungi is inevitably slowing down. Under laboratory conditions, the majority of biosynthetic gene clusters, which store information for secondary metabolites, remain inactive. Therefore, establishing the link between biosynthetic gene clusters and secondary metabolites would contribute to understanding the genetic logic underlying secondary metabolite biosynthesis and alleviating the current challenges in discovering novel natural products. Bioinformatics methods have garnered significant attention due to their powerful capabilities in data mining and analysis, playing a crucial role in various aspects. Thus, we have summarized successful cases since 2016 in which bioinformatics methods were utilized to establish the link between fungal biosynthetic gene clusters and secondary metabolites, focusing on their biosynthetic gene clusters and associated secondary metabolites, with the goal of aiding the field of natural product discovery.

AIMS:This study aimed to explore the secondary metabolic potential of Kutzneria viridogrisea DSM 43850 by conducting whole-genome sequencing and utilizing bioinformatics tools to analyze its biosynthetic gene clusters (BGCs). Additionally, the secondary metabolites produced by this strain were investigated under various chemical elicitors using untargeted metabolomics techniques. METHODS AND RESULTS:The complete genome of K. viridogrisea DSM 43850 was obtained by re-sequencing, followed by in-depth bioinformatics analysis to assess its secondary metabolic potential. The genome was found to encode a circular 10.2 Mb chromosome, with 4.3% of its functional genes involved in secondary metabolism. The strain harbors 52 BGCs, of which only 4 are associated with known products. Among these, eight gene clusters were identified as ribosomally synthesized and post-translationally modified peptides, and the precursor peptide structures of four were predicted, all featuring novel scaffolds. Untargeted metabolomics analysis using liquid chromatography-mass spectrometry revealed that the strain could produce a series of novel secondary metabolites when induced with kanamycin and an ebselen derivative. CONCLUSIONS:This study highlights the significant secondary metabolic potential of K. viridogrisea DSM 43850, uncovering several novel BGCs and metabolic products.

The genus Archangium, a cryptic group of myxobacteria, is a rich source of diverse secondary metabolites. This study reviews the chemical structures and discovery history of 55 secondary metabolites, analyzing the relationship between the chemical structures of these compounds and their bioactivity profiles through molecular networking. Notably, 63.6% of the compounds exhibit potent antimicrobial (MIC < 1 μg/mL) and/or cytotoxic activities (IC50 < 1 μg/mL). Advances in the biosynthetic gene clusters and biosynthetic pathways of seven classes of identified compounds are also presented. Finally, genomic mining approaches are applied to analyze the potential for Archangium strains to synthesize analogs of identified bioactive natural products, uncovering that 98.7% of their secondary metabolic potential remains unexplored. This study highlights the vast potential of Archangium bacteria in synthesizing clade-specific novel secondary metabolites, particularly ribosomally synthesized and post-translationally modified peptide natural products, offering valuable insights for the targeted discovery and biosynthesis of new natural products from this genus.

Fungi are a vast reservoir of structurally diverse natural products, yet their biosynthetic potential remains underexplored. Here, we present the most comprehensive fungal biosynthetic gene cluster (BGC) atlas, comprising 303,983 BGCs predicted from 13,125 fungal genomes, revealing numerous underexplored taxa harboring extensive biosynthetic diversity. These BGCs were classified into 43,984 gene cluster families (GCFs), of which 99.6 % remain uncharacterized. Gene-centric analysis revealed the presence of 359 cyclodipeptide synthases (CDPSs) of three distinct subcategories and 9,482 nonribosomal peptide synthetases (NRPSs) responsible for diketopiperazine biosynthesis in the fungal BGC atlas. Notably, 304 type I CDPSs were exclusively found in Fusarium, with one confirmed to be responsible for the synthesis of cyclo(Leu-Ile) and cyclo(Pro-Leu). Bioinformatics analysis suggests that three newly identified indole diketopiperazine alkaloids, isolated from a marine-derived Aspergillus strain, are synthesized by an NRPS. This study presents the most comprehensive fungal BGC atlas and highlights the diversification of diketopiperazine biosynthesis in fungi.