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At least 19 recordsLinked to original sources
PubMed2026

Genome-Resolved Functional Profiling of Osteoporosis-Associated Gut Bacteria Highlights Putative Metabolic and Immunogenic Signatures of the Gut-Bone Axis.

The gut microbiota has emerged as a potential regulator of bone metabolism, but the genome-encoded functional repertoire of osteoporosis-associated gut bacteria remains insufficiently characterized. This study performed in silico functional profiling of gut bacterial taxa associated with osteoporosis, low bone mineral density, or comparator bone-related phenotypes. Twenty candidate taxa were selected from evidence in the human microbiome and represented by 26 curated bacterial reference genomes. Genome-wide annotations were used to map predicted gut-bone axis signatures, carbohydrate-active enzyme (CAZyme) repertoires, selected Kyoto Encyclopedia of Genes and Genomes pathways, and gutSMASH-predicted metabolic gene clusters. Functional burdens were normalized as hits per 1000 annotated proteins and integrated into metabolic, immunogenic, CAZyme, KEGG, and metabolic gene cluster profiles. Twelve predicted gut-bone axis signatures were identified, comprising 3337 primary candidate protein hits and a strict high-confidence subset of 2497 hits. Dominant signatures included vitamin B12/cobalamin metabolism, folate/one-carbon metabolism, peptidoglycan/cell-wall biosynthesis, and short-chain fatty acid-related functions. Dialister invisus, Dialister succinatiphilus, Megamonas funiformis, and Megamonas hypermegale showed the strongest normalized predicted gut-bone axis signal. These hypothesis-generating findings prioritize microbial metabolic and immunogenic features for future metagenomic, metabolomic, and experimental validation studies.

Osteoporosis↗
PubMed2026

Isolation, genomic characterization, and safety assessment of an O-desmethylangolensin-producing Clostridium beijerinckii strain from Chinese Stinky Tofu.

The health benefits of dietary soy isoflavones are largely mediated by specific microbial metabolites, such as O-desmethylangolensin (O-DMA). However, the diversity and application potential of O-DMA-producing strains remain poorly explored, primarily due to the limited availability of isolated strains, narrow ecological sources, and a lack of practical applications. In this study, an O-DMA-producing bacterium, designated strain FRJF5, was isolated from Chinese stinky tofu under anaerobic conditions and was identified as Clostridium beijerinckii. The biosynthesized O-DMA exhibited an enantiomeric excess (e.e.) of 78.6%. Based on phylogenetic and average nucleotide identity analyses against 235 public C. beijerinckii genomes, the clustering of FRJF5 with strains from diverse habitats-including industrial fermentation settings, animal feces, and soil-highlights the broad ecological diversity within this species. Functional gene mining and intra-species comparative genomics revealed a unique flavonoid metabolism gene cluster in FRJF5. Using apigenin as a representative flavonoid, we confirmed the successful conversion to 3-(4-hydroxyphenyl)-propionic acid. Moreover, the strain was predicted and verified to possess a substantial butyrate-producing capacity. Genomic screening for virulence or antibiotic resistance genes, combined with phenotypic tests (hemolysis, antibiotic susceptibility, and mouse gavage), revealed a favorable safety profile for strain FRJF5. Finally, intervention experiments in a mouse model of colitis supported its potential in alleviating the disease. Collectively, this study identifies C. beijerinckii FRJF5 as a strain capable of simultaneously producing O-DMA and butyrate, highlighting its potential for future applications in functional foods.IMPORTANCESoy isoflavones require gut bacterial conversion into bioactive metabolites-such as the anti-inflammatory compound O-desmethylangolensin (O-DMA)-to exert health benefits. Yet O-DMA-producing strains remain scarce, largely confined to fecal sources, and poorly characterized. Here, we isolated Clostridium beijerinckii FRJF5 from Chinese stinky tofu, an unexplored ecological niche. This strain not only produces enantiomerically enriched O-DMA but also co-produces butyrate, a metabolite known to strengthen gut barrier function. Genomic mining uncovered a unique flavonoid metabolism gene cluster responsible for this dual activity. Combined with favorable safety profiles, FRJF5 emerges as a strong candidate for functional food applications. This work expands the known diversity of O-DMA producers and bridges traditional fermented foods with next-generation probiotic development.

O-desmethylangolensin↗
PubMed2026

Comparative Genomics of Paenibacillus Secondary Metabolism: Unveiling the Putative Biosynthetic Gene Cluster for Paenialvins in Paenibacillus Alvei Strain 32.

In this study, we used comparative genomics and culture-based methods to investigate Biosynthetic Gene Clusters (BGCs) responsible for the production of antimicrobial peptides. Paenibacillus alvei strain 32 was isolated from a cystic fibrosis sputum. Its genome was sequenced using Illumina, showing a size of 6,584,590 bp with 239 contigs assembled in 26 scaffolds, an average coverage of 243X, and 6,832 coding sequences. ANI analysis and in silico DNA-DNA hybridization showed its affiliation inside Paenibacillus alvei, with a clear separation from other related strains, leading us to propose a distinct species-level genomic clade (genomospecies) within this group. AntiSMASH analysis predicted 22 putative BGCs in the genome of strain 32. Its culture supernatant exhibited inhibitory activity against Gram-positive pathogens, including methicillin-resistant Staphylococcus aureus (MRSA), Bacillus cereus, and Enterococcus faecalis. By comparing in silico BGC predictions with activities described in the literature, we propose that strain 32 harbours a specific 110-kb cluster (cluster 6.2) with five non-ribosomal peptide synthetase (NRPS) genes. These synthetases are predicted to direct the assembly of a 16-amino acid backbone that correlates with the structure of paenialvins, which are known anti-MRSA molecules. This study describes the putative biosynthetic pathway of the paenialvins and explains structural variations, bringing useful data on Paenibacillus secondary metabolism for future antibiotic development.

Paenibacillus alvei↗
PubMed2025

iNOME-seq: in vivo simultaneous genome-wide mapping of chromatin accessibility, nucleosome positioning, DNA-binding protein sites, and DNA methylation in Arabidopsis.

We present iNOMe-seq, a novel method for in vivo simultaneous profiling of chromatin accessibility, nucleosome occupancy, DNA-binding protein sites, and DNA methylation in living tissues. iNOMe-seq utilizes an m5C methyltransferase to mark accessible cytosines in a GpC context, bypassing nucleosome-restricted regions. Using Arabidopsis thaliana, we demonstrate that iNOMe-seq improves chromatin accessibility quantification compared to existing methods. Furthermore, it allows for the spatial and temporal analysis of chromatin dynamics, transcription factor binding, and DNA methylation, offering insight into the role of epigenetic components in transcriptional regulation across tissues and genetic variations in natural populations.

Arabidopsis↗
PubMed2026

Deciphering the Function and Structure of PA1216 as an S-Adenosyl-l-Methionine Binding Protein Using Differential Scanning Fluorimetry and Circular Dichroism.

Microbes produce bioactive secondary metabolites as toxins, pigments, or virulence factors. These specialized compounds are produced by nonribosomal peptide synthetases (NRPS), polyketide synthases (PKS), or hybrid NRPS/PKS pathways. The genes encoding NRPS and PKS reside in biosynthetic gene clusters (BGCs), some of which have no identified metabolite associated with them. Characterization of these orphan BGCs could provide insights into potential bioactive compounds that have yet to be discovered. Here, we characterize PA1216, a putative methyltransferase embedded within an NRPS BGC in Pseudomonas aeruginosa strain PAO1. We cloned, expressed, and purified PA1216, and developed an optimized differential scanning fluorimetry assay to measure its thermal stability, demonstrating concentration-dependent stabilization in the presence of established methyltransferase cofactors and inhibitors. We then adapted this assay for high-throughput screening of potential PA1216 substrates, identifying destabilizing compounds, including glycyl-glycine dipeptides, amino esters with aromatic or basic side chains, and N-Boc-protected amino acids. In contrast, sodium salts of organic acids stabilized PA1216. Lastly, we employed AlphaFold to construct a predictive model, revealing that PA1216 contains a Rossmann-like fold and a glycine-rich loop, typical of class I methyltransferases, and we corroborated these secondary structural elements using circular dichroism spectroscopy. Overall, these studies illuminate PA1216 function and establish a platform for characterizing cryptic gene clusters within secondary metabolic pathways.

Circular Dichroism↗
PubMed2026

Hybrid genome assembly and phenotypic assays reveal carbohydrate metabolism diversity in Lacticaseibacillus strains.

Investigation of carbohydrate metabolism in lactic acid bacteria is essential for the rational selection of strains for fermentation processes, particularly in emerging applications involving non-conventional substrates or building of synthetic microbial consortia. However, establishing robust genotype-phenotype relationships remains challenging, as gene presence alone often fails to explain observed metabolic traits without considering the genomic context and regulatory architecture. In the present study, we combined hybrid genome assembly (Illumina and Oxford Nanopore) with high-throughput phenotype profiling (Biolog GENIII and PM2A) to investigate carbohydrate utilization in five Lacticaseibacillus strains. Phenotypic assays revealed clear intra- and inter-specific variability in substrate utilization. We therefore investigated whether such differences could be attributed to the organization and regulatory context of carbohydrate-associated loci, rather than to gene presence alone. Functional annotation based on COG and CAZyme databases revealed candidate genomic regions potentially involved in carbohydrate metabolism. Comparative analysis between predicted and experimentally observed substrate usage highlighted specific loci associated with carbohydrate utilization profile. The trehalose (tre) operon was conserved across all strains, while at least two distinct cellobiose-associated loci were detected in each genome. Despite the presence of these loci, L. paracasei strains were unable to metabolize cellobiose, a phenotype likely linked to the presence of a downstream TetR-type transcriptional repressor within the cellobiose (cel) operon. Additionally, a genomic region uniquely found in L. rhamnosus strains was associated with gentiobiose utilization, consistent with phenotypic observations. Overall, these findings highlight the importance of integrating phenotypic validation with complete genome context to support the identification of candidate structural and regulatory determinants of carbohydrate utilization in lactic acid bacteria. KEY POINTS: • Phenotype microarrays reveal metabolic traits of interest in isolated strains. • Regulatory context is key to understanding carbohydrate metabolism differences. • Basis of subspecies-dependent cellobiose metabolism in L. paracasei is provided.

Carbohydrate Metabolism↗
PubMed2026

Efficient rDNA-mediated multi-copy integration of gene clusters in Aureobasidium melanogenum.

Aureobasidium melanogenum is a promising non-conventional yeast chassis for synthetic biology. However, techniques recombining large genetic fragments, such as gene clusters, are still unavailable, hindering further metabolic reprogramming in this chassis. To achieve multi-copy integration of genes, we employed highly repetitive ribosomal DNA (rDNA) sequences in A. melanogenum as homologous recombination sites for large genetic fragments. First, integration efficiency of three different regions of A. melanogenum rDNA were investigated: RNA polymerase I promoter region (rDNA1, 1.0 kb), partial 26S rDNA region (rDNA2, 1.0 kb), and RNA polymerase I terminator region (rDNA3, 1.0 kb). Our findings revealed that the highest copy numbers and expression stability were observed for the short heterologous green fluorescent protein gene (gfp, 0.7 kb) and the long native polyketide synthase gene (pks, 7.0 kb) after rDNA1-mediated integration. Specifically, the copy numbers reached 7.0 and 8.0 for gfp and pks, respectively, and they remained stably expressed in the genome after 120-h subculturing. Furthermore, an 11.0 kb gene cluster (comprising the native pks, phosphopantetheinyl transferase (npg1), and scytalone dehydratase genes (scd) responsible for melanin biosynthesis) was integrated at the rDNA1 site, resulting in stable recombination with 15.0 copies and an approximately 12-fold increase in melanin production. Overall, the convenience and efficiency of the proposed rDNA-mediated multi-copy insertion strategy will facilitate superior metabolic engineering of A. melanogenum chassis cells.

Multigene Family↗
PubMed2025

Integrative multi-omics analysis proposes a metabolic classification of gliomas: distinct metabolic states, immune infiltration, and prognosis.

BACKGROUND: The tumor microenvironment (TME) of glioma harbors diverse cell types; however, cell metabolic heterogeneity remains to be explored. This study aims to characterize the metabolic features of different cell types in the TME by integrating multiple datasets, including genomics, bulk and single-cell transcriptomics, and metabolomics. METHODS: Unsupervised machine learning was used to construct an energy metabolic classifier based on the metabolic pathways identified from bulk RNA-seq of gliomas in the TCGA dataset. The classifier was externally validated using multiple datasets, including genomics, bulk RNA-seq, snRNA-seq, and the metabolomics data. Furthermore, metabolic heterogeneity associated with the classifier was further characterized at single-cell resolution. RESULTS: The energy metabolism-based classifier stratified patients into two prognostic clusters: patients in cluster 1 were characterized by high pathway activity of glycolysis, the pentose phosphate pathway (PPP), and fatty acid oxidation (FAO), whereas patients in cluster 2 exhibited higher activity in glutaminolysis. This metabolic classifier revealed both intratumoral and intertumoral metabolic heterogeneity, and the complexity was further validated by the metabolomics profiling and snRNA-seq data from the CPTAC dataset. Notably, OSMR, highly expressed in cluster 1, showed significant co-expression with key glycolytic enzyme genes. The OSM/OSMR/JAK1/STAT3 axis potently drives malignant progression of glioma cells, specially enhancing their invasive and migratory capabilities. Single-cell resolution analyses demonstrated that tumor metabolic heterogeneity is primarily driven by malignant cells rather than non-malignant components, while tumor microenvironment (TME) factors were also found to modulate malignant cell metabolism. Significantly, glycolytic activity in glioma cells increased during the phenotypic transition from PN (proneural) to MES (mesenchymal), with cluster 1 metabolic phenotypes predominating in the tumor core. Compared to cluster 2, cluster 1 patients exhibited higher mRNA expression of immunosuppressive checkpoint genes, which correlated with pronounced immunosuppression in the TME. Furthermore, various immune cells demonstrated distinct metabolic preferences at single-cell resolution. CONCLUSIONS: This study developed an energy metabolic-based classifier for gliomas with prognostic and therapeutic potential. Metabolic reprogramming was linked with the PN-to-MES transition of glioma cells and immunosuppression in the tumor microenvironment. Multi-omics data, especially snRNA-seq, offered insights into metabolism heterogeneity at single-cell resolution, enabling personalized treatment strategies.

Humans↗
PubMed2026

Mechanistic Perspectives From Genomics and Pangenomics of Medicinal and Aromatic Plants: Linking Genome Architecture to Phytochemical Diversity.

Medicinal and aromatic plants (MAPs) produce a remarkable diversity of specialized metabolites with significant pharmaceutical, nutraceutical, and industrial value. Although advances in long-read sequencing, chromosome-scale genome assembly, and pangenomics have greatly expanded genomic resources, the mechanistic links between genome architecture and phytochemical diversity remain incompletely understood. The present review synthesizes current evidence describing how structural genomic variation may contribute to phytochemical diversity, while acknowledging that many proposed genome-to-metabolite relationships require further experimental validation. Examples illustrate how genome architecture is associated with specialized-metabolite biosynthesis through multiple regulatory processes. However, the strength of supporting evidence varies considerably among MAP species. Moreover, relatively few genome-to-metabolite relationships have been confirmed through direct functional validation. We further discuss how pangenomics, multiomics integration, genome editing, synthetic biology, and artificial intelligence support the discovery, validation, and engineering of specialized metabolic pathways. Casual conclusions are evaluated according to the strength of available evidence, highlighting where causal relationships have been experimentally established and where conclusions remain primarily association-based. Overall, this review provides an integrated conceptual and evidence-based perspective summarizing proposed relationships between genome architecture and phytochemical diversity and outlines future priorities for functional genomics, precision breeding, metabolic engineering, and sustainable utilization of MAPs.

artificial intelligence↗
PubMed2026

Pangenome of Streptomyces sampsonii and Relatives Highlights Horizontal Gene Transfer and Secondary Metabolism in Environmental Adaptation and Ecological Significance.

Streptomyces sampsonii is a promising biocontrol bacterium, but its genomic basis of adaptation and secondary metabolism remains unclear. Here, we present a chromosome-level genome assembly of S. sampsonii (7.20 Mb, 6015 protein-coding genes) and perform comparative analyses with 95 related Streptomyces species. Phylogenomic and synteny analyses revealed its closest relationship with S. albidoflavus, while extensive structural variations distinguished more distant lineages. Pangenome analysis uncovered 84,178 gene clusters, with pan_shell and pan_cloud genes predominantly enriched in xenobiotic biodegradation, metabolism, and antibiotic biosynthesis, highlighting their roles in ecological adaptation and biocontrol potential. Biosynthetic gene cluster (BGC) analysis identified numerous NRPS, PKS, and terpene pathways, many of which belong to pan_shell and pan_cloud regions, suggesting dynamic evolutionary origins. We further detected 66,260 horizontally transferred (HGT) genes, including 438 in BGCs, underscoring HGT as a major driver of metabolic innovation. Together, these findings provide novel insights into the genomic diversity, adaptive capacity, and secondary metabolic potential of S. sampsonii and its close relatives.

BGCs↗
PubMed2026

Genome mining of alkaliphilic cyanobacterial consortia: identification of biosynthetic gene clusters in Sodalinema and associated heterotrophs.

Alkaline soda lakes are high-pH environments that host specialized microbial communities with potential for biotechnology and natural product discovery. We characterized three Sodalinema-dominated cyanobacterial consortia enriched from Canadian soda lakes over 510 days. Using hybrid metagenomic sequencing and metatranscriptomics across pH, alkalinity, and temperature gradients, we reconstructed high-quality metagenome-assembled genomes and assessed functional activity. All consortia converged toward cyanobacteria dominance and exhibited temperature optima between 21°C and 30°C. Phylogenetic analysis placed Sodalinema genomes within a distinct clade affiliated with Candidatus Sodalinema alkaliphilum. Genomic analysis indicated complete biosynthetic pathways for vitamin B5, vitamin B7, and the molybdenum cofactor, but incomplete pathways for vitamins B1, B9, and B12, consistent with patterns observed in Sodalinema yuhuli. Metatranscriptomic profiles showed increased expression of genes involved in phycocyanin and carotenoid biosynthesis at pH 10.2 relative to pH 8.5. Biosynthetic gene cluster analysis revealed that most secondary metabolic potential resided in heterotrophic community members. Roseinatronobacter encoded pathways for N-acyl homoserine lactones, osmoprotectants, betalactones, and prodigiosin, while Alkalimonas, Wenzhouxiangella, and members of the Kiloniellales encoded clusters for lanthipeptides, cyclodipeptides, hydrogen cyanide, and pyrroloquinoline quinone. These findings indicate functional partitioning within the consortia and highlight the contribution of heterotrophs to secondary metabolism.IMPORTANCEAlkaline soda lakes contain microbial communities adapted to high pH that remain underexplored for biotechnology. This study focuses on Sodalinema, a filamentous cyanobacterium that dominates enriched consortia from Canadian soda lakes, and its associated heterotrophic partners. We show that while Sodalinema drives primary productivity, heterotrophic bacteria encode most of the pathways for antimicrobial and signaling compounds. These interactions may support community stability and defense against competing microorganisms. By linking genomic potential with gene expression, this work identifies alkaline cyanobacterial consortia as a source of bioactive compounds and provides a framework for exploring extremophilic microbial communities for natural product discovery.

Sodalinema↗
PubMed2026

Comparative genomics reveals hidden biosynthetic diversity in Streptomyces spp. and metal-dependent regulatory features associated with untapped specialized metabolites.

The genus Streptomyces is one of the richest sources of bioactive natural products; however, a substantial proportion of its biosynthetic gene clusters (BGCs) remain cryptic and their metabolic products are unresolved. Advances in genome mining and computational prediction now enable comprehensive exploration of this hidden biosynthetic repertoire. In this study, whole-genome sequencing and comparative genomic analyses were performed on three three newly isolated Streptomyces strains to evaluate their specialized metabolic potential. Genome assemblies were annotated and systematically analyzed using antiSMASH, DeepBGC, GECCO, and PRISM to identify, cross-validate, and functionally characterize BGCs while predicting their associated secondary metabolite scaffolds. Taxonomic analyses based on Average Nucleotide Identity (ANI), phylogenomics, and BLAST identified the isolates as Streptomyces thinghirensis, Streptomyces novocaesareae, and Streptomyces griseorubens. Applying the consensus framework across the three Streptomyces genomes yielded 43 cryptic BGCs, lacking close similarity to reference BGCs in the MIBiG database, of which 26 were classified as HIGH, 10 as MEDIUM, and 7 as LOW confidence. Notably, numerous BGCs exhibited low abundance to characterized reference clusters, indicating a high potential for previously undescribed biosynthetic pathways and novel metabolite scaffolds. Comparative analyses further revealed strain-specific biosynthetic architectures together with putative metal-responsive regulatory systems; Fur, Zur, and Nur, which were frequently associated with specialized metabolite biosynthetic loci. Collectively, these findings demonstrate the effectiveness of integrated genome-mining strategies for prioritizing cryptic biosynthetic gene clusters and highlight the remarkable biosynthetic potential of newly identified Streptomyces isolates as a source of novel natural products.

comparative genomics↗
PubMed2024

Developing a machine learning-based prognosis and immunotherapeutic response signature in colorectal cancer: insights from ferroptosis, fatty acid dynamics, and the tumor microenvironment.

INSTRUCTION: Colorectal cancer (CRC) poses a challenge to public health and is characterized by a high incidence rate. This study explored the relationship between ferroptosis and fatty acid metabolism in the tumor microenvironment (TME) of patients with CRC to identify how these interactions impact the prognosis and effectiveness of immunotherapy, focusing on patient outcomes and the potential for predicting treatment response. METHODS: Using datasets from multiple cohorts, including The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), we conducted an in-depth multi-omics study to uncover the relationship between ferroptosis regulators and fatty acid metabolism in CRC. Through unsupervised clustering, we discovered unique patterns that link ferroptosis and fatty acid metabolism, and further investigated them in the context of immune cell infiltration and pathway analysis. We developed the FeFAMscore, a prognostic model created using a combination of machine learning algorithms, and assessed its predictive power for patient outcomes and responsiveness to treatment. The FeFAMscore signature expression level was confirmed using RT-PCR, and ACAA2 progression in cancer was further verified. RESULTS: This study revealed significant correlations between ferroptosis regulators and fatty acid metabolism-related genes with respect to tumor progression. Three distinct patient clusters with varied prognoses and immune cell infiltration were identified. The FeFAMscore demonstrated superior prognostic accuracy over existing models, with a C-index of 0.689 in the training cohort and values ranging from 0.648 to 0.720 in four independent validation cohorts. It also responses to immunotherapy and chemotherapy, indicating a sensitive response of special therapies (e.g., anti-PD-1, anti-CTLA4, osimertinib) in high FeFAMscore patients. CONCLUSION: Ferroptosis regulators and fatty acid metabolism-related genes not only enhance immune activation, but also contribute to immune escape. Thus, the FeFAMscore, a novel prognostic tool, is promising for predicting both the prognosis and efficacy of immunotherapeutic strategies in patients with CRC.

Ferroptosis↗
PubMed2026

Secondary metabolite profiling of rare Micromonospora spp. from cold desert of NW Himalayas via multi-omics analysis.

INTRODUCTION: The genus Micromonospora is a prolific producer of specialized metabolites with pharmacological and agronomic relevance. Natural products derived from the genus Micromonospora have a distinctive chemical diversity and enormous therapeutic potential, thus represent a potential source for drugs and drug leads. OBJECTIVE: To explore the biosynthetic potential of four Micromonospora strains isolated from cold desert of NW Himalayas through genome mining and to correlate predicted biosynthetic gene clusters with chemical features detected by untargeted LC-HRMS metabolomics. METHOD: High-quality genomes were annotated for BGCs and matched against untargeted LC-HRMS features (peak picking, alignment, and annotation to chemical classes). Each isolate was grown in triplicate, and fermented broth was pooled for further metabolomic studies. RESULTS: By integrating genomic and metabolomic approaches, specialized biosynthetic gene clusters and strain-based putative metabolite classes were identified. LRS1 showed elevated xanthines (RiPP/siderophore), LRS3 had phenolic glycosides (hybrid PKS/NRPS), LRS4 showed 70-fold hydroxycinnamate enrichment (Type II PKS), and LRS5 displayed p-benzoquinone enrichment (Type III PKS). The metabolite profile of each strain aligned with its predicted biosynthetic gene cluster composition. CONCLUSION: Under a single growth regime, each Micromonospora strain exhibits a distinct metabolomic profile. This metabologenomics workflow can be further explored to isolate specialized metabolites with potential therapeutic and agricultural value.

Micromonospora↗
PubMed2026

Trichoderma specialized metabolites in biocontrol: gene-metabolite links, ecological functions, and translational bottlenecks.

Trichoderma spp. produce a diverse repertoire of metabolites with specific activities that contribute to biocontrol through direct antagonism, ecological signalling, and modulation of plant responses. However, current knowledge remains uneven: many metabolites are chemically described, whereas fewer are supported by robust gene-metabolite associations, experimentally validated ecological functions, and realistic translational evidence. Progress in this field will depend less on expanding compound catalogues than on integrating mechanistic, ecological, and translational evidence. This review examines the specialized metabolism of Trichoderma with emphasis on biosynthetic gene clusters, regulatory networks, ecological roles, and biosafety constraints relevant to biocontrol. Major metabolite classes, including polyketides, terpenoids, peptaibols, siderophores, diketopiperazines, and volatile organic compounds, are discussed together with representative case studies for which genetic and functional evidence is available. We further propose a translational framework to distinguish metabolites with mainly descriptive support from those approaching application readiness, based on four criteria: gene-level validation, demonstrated ecological role, manageable biosafety profile, and feasible delivery/stability. This perspective helps explain why metabolite inventories continue to expand faster than field translation. Recent advances in genomics, transcriptomics, metabolomics, genome editing, and formulation science are reshaping how Trichoderma metabolites are prioritized for future development.

Biosafety↗
PubMed2026

Comparative genomics approaches to identify genomic regions associated with the antimicrobial activity of Pseudomonas protegens PBL3.

The environmental bacterium Pseudomonas protegens PBL3 has antagonistic activity against the plant pathogenic bacterium Burkholderia glumae, an important pathogen in rice. The antimicrobial activity of P. protegens PBL3 was found in the bacteria-free secreted fraction (secretome), but the specific molecules, as well as the genetic basis of that activity, have not been identified. In this study, we integrated genomic information with antimicrobial assays on P. protegens PBL3 and additional six Pseudomonas spp. strains, to identify putative genomic regions in P. protegens PBL3 associated with antimicrobial activity. We hypothesized that Pseudomonas spp. strains with antimicrobial activity against B. glumae have conserved genes with P. protegens PBL3 that are absent in strains lacking activity. Comparative genomics analyses with anvi'o and progressiveMauve, and using P. protegens PBL3 as the reference genome, revealed 188 genes uniquely present in antimicrobial-producing strains. Seven of those genes were annotated as biosynthetic gene clusters predicted to encode secondary metabolites; additional genes were grouped into 25 contiguous clusters with functions annotated as secretion, signal transduction, regulation, transport/efflux, carbohydrate metabolism and one with an additional uncharacterized function. Altogether, this study uncovered a complex and multi-functional network of candidate genes, suggesting that the antimicrobial activity in P. protegens PBL3 is not limited to biosynthetic pathways but also involves additional regulatory, metabolic and export modules to synthesize and deploy antimicrobials.

Pseudomonas↗
PubMed2026

A novel glutamine metabolism-based classification system for characterizing the heterogeneity of hepatocellular carcinoma.

BACKGROUND: Glutamine dependence is a hallmark of tumor cell metabolism, and further molecular classification based on glutamine metabolism in patients with hepatocellular carcinoma (HCC) may provide clinical value. This study thus comprehensively examined the patterns of HCC-specific alterations in glutamine metabolism. METHODS: Consensus clustering analysis was conducted on samples from The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) dataset based on glutamine metabolism-related genes, which was validated in the GSE76427, the Liver Cancer-France (LICA-FR) cohort, and the Liver Cancer-Japan (LIRI-JP) cohort from the ICGC. Somatic mutation features were evaluated with the Maftools package in R. The activity of oncogenic pathways was estimated via gene set enrichment analysis (GSEA) or single-sample GSEA (ssGSEA). The tumor microenvironment was analyzed using both the CIBERSORT algorithm (for immune cell infiltration estimation) and the ESTIMATE algorithm (for stromal and immune score calculation). Drug sensitivity and immune checkpoint blockade (ICB) response were also analyzed, for which a classifier was built via least absolute shrinkage and selection operator (LASSO). Immunohistochemistry (IHC) was performed to validate the protein expression levels of key differentially expressed genes (DEGs). Intracellular glutamine content under different glutamine concentrations was measured. The viability of HCC cell lines under varying glutamine concentrations was assessed via Cell Counting Kit-8 (CCK-8) assays. Cell migration and invasion were evaluated through Transwell assays, and protein expression was analyzed via Western blotting. RESULTS: HCC samples were classified into two glutamine metabolism-based clusters, with cluster 1 having a more advanced stage of disease and shorter survival than cluster 2. A higher frequency of genetic mutations and stronger activation of oncogenic pathways was found in cluster 1. There were substantial differences in immune cell infiltration and stromal scores between clusters 1 and 2. Cluster 1 exhibited significantly higher infiltration of immunosuppressive cells and lower stromal scores compared to cluster 2. Cluster 1 had a stronger response to ICB due as indicated by a higher tumor mutation burden (TMB) and T cell-inflamed gene expression profile score, immune checkpoints, and Tumor Immune Dysfunction and Exclusion (TIDE)-predicted data. Moreover, the LASSO classifier accurately differentiated the two clusters. The DEGs between the two clusters were validated in clinical samples. IHC confirmed the differential expression of glutamine metabolism-related genes in HCC samples. CCK-8 assays showed no significant effect of glutamine concentration on cell proliferation. However, Transwell assays revealed that glutamine deprivation (0.2 mM) reduced migration and invasion, while high-glutamine conditions (10 mM) promoted them. Western blotting showed increased expression of metabolism-related proteins under high-glutamine conditions and reduced expression under deprivation. CONCLUSIONS: Altogether, these findings indicate the involvement of glutamine metabolism in HCC and may help inform patient stratification and the formulation of precision therapeutics for this population.

Hepatocellular carcinoma (HCC)↗
PubMed2025

South African Myxococcota: an untapped resource for microbial ecolo gy and biotechnology.

An extraordinary multicellular life cycle, ecological versatility, and prolific production of bioactive secondary metabolites characterise the phylum Myxococcota. While research has predominantly focused on Myxococcota in Asia, Europe, and North America, their potential occurrence in Sub-Saharan Africa remains largely unexplored. To date, only one study has isolated Myxococcota in South Africa, with additional findings limited to incidental detection through metagenomic studies. Considering South Africa's ecological diversity, its biomes may represent promising but under-examined environments for systematic bioprospecting aimed at discovering novel Myxococcota with ecological or biotechnological potential. The recent reclassification of Myxococcota from the former Deltaproteobacteria has provided a more coherent taxonomic framework to guide future ecological and systematic studies. This review presents an overview of the taxonomic revision and explores the potential occurrence of Myxococcota in South African biomes. It covers the challenges associated with conventional culture-based isolation methods and highlights potential genome- and metagenome-based approaches, including the use of metagenome-assembled genomes (MAGs) to identify cryptic biosynthetic gene clusters (BGCs), while acknowledging current limitations. Considering the increasing resistance to chemical fungicides in South African agriculture, this review further explores the potential of Myxococcota-derived secondary metabolites as candidate bioprotective alternatives. By identifying current research gaps, it aims to support future efforts towards systematic bioprospecting to investigate the ecological and biotechnological potential of Myxococcota in South Africa. KEY POINTS: • South African biomes may harbour novel Myxococcota with biosynthetic potential. • Genome mining could reveal cryptic biosynthetic gene clusters (BGCs). • Myxococcota metabolites may help control resistant fungal phytopathogens.

South Africa↗