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Ziemert, N.

Publications and source records attributed to Ziemert, N..

14 recordsLinked to original sources

BGC Atlas v2: biosynthetic gene clusters with taxonomic and environmental context at scale

Genomic and metagenomic studies have revealed that microbes have the capacity to produce an enormous diversity of secondary metabolites. These compounds play important roles in microbial interactions and are also a major source of medicines and other useful natural products, yet only a small fraction of this biosynthetic potential has been experimentally characterized. BGC Atlas was developed to explore this largely uncharacterized diversity by placing biosynthetic gene clusters (BGCs) into genomic and environmental context. Here, we present BGC Atlas v2, which expands the collection nearly ninefold to more than 16 million predicted BGCs and extends it from metagenomic assemblies to MAGs, single-amplified genomes and isolate genomes. The new release provides taxonomic assignments for nearly all BGCs, harmonized environmental metadata, nested searches across biosynthetic, taxonomic, environmental and geographic properties, and protein-sequence searches against BGC-encoded genes. Despite its scale, the collection reveals how much microbial biosynthetic diversity remains unexplored. By connecting pathways to related families, organisms and environments, BGC Atlas v2 supports natural-product discovery and investigations of microbial biosynthetic diversity across taxa and ecosystems. BGC Atlas v2 is freely available at https://bgc-atlas.cs.uni-tuebingen.de.

bioinformatics↗

metaSMASH: Scalable Biosynthetic Gene Cluster Detection for Large Metagenomic Assemblies

antiSMASH is widely used for biosynthetic gene cluster (BGC) detection and annotation, but its standard workflow is poorly suited to large metagenomic assemblies, where massive contig counts create severe runtime bottlenecks and complicate downstream result exploration. We present metaSMASH, a re-engineered fork of antiSMASH for metagenome-scale BGC analysis. metaSMASH preserves the original antiSMASH detection and annotation logic while introducing streaming, memory-bounded execution, record-level parallelisation, optional output filtering, and an interactive dashboard for large result sets. Across 25 benchmark metagenome datasets, metaSMASH reproduced identical BGC detection results while dramatically reducing computational cost. Relative to the default antiSMASH configuration, metaSMASH was a geometric-mean 38x faster. It also outperformed an ad hoc chunked antiSMASH workflow: in the default configuration it achieved a geometric-mean 2.9 x speed-up and 1.7 x lower peak memory, and with extended-analysis modules enabled it was 2.7 x faster and used 3.1 x less memory while completing all datasets, whereas the ad hoc workflow ran out of memory on the two largest assemblies. By substantially reducing the computational burden of large-scale metagenome analysis without sacrificing result equivalence, metaSMASH makes routine mining of assembled metagenomes more practical and provides a scalable foundation for natural product discovery from complex microbial communities. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=78 SRC="FIGDIR/small/739405v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@d224ccorg.highwire.dtl.DTLVardef@6ddbdcorg.highwire.dtl.DTLVardef@7d5d40org.highwire.dtl.DTLVardef@7537e1_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

Cooperative siderophore use stabilizes a protective leaf microbiome

Plant-associated microbial communities provide crucial protection against pathogens. Specialized metabolites play key roles in plant-microbe and microbe-microbe interactions and, ultimately, in plant health; however, the molecular mechanisms underlying their plant-protecting properties remain largely unknown. Nutrient deficiency (e.g., iron) on leaf surfaces creates intense competition among microbes, driving both antagonism and cooperation. Using a gnotobiotic Arabidopsis thaliana model and a synthetic leaf microbial community, we show that community stability and plant protection depend on cooperative siderophore exchange between the basidiomycete yeast Rhodotorula kratochvilovae and commensal Pseudomonas species. Removal of Pseudomonas caused a strong shift in the community metabolome and accumulation of the yeast siderophore rhodotorulic acid (RA). RA selectively promoted the growth of commensal Pseudomonas via TonB-dependent transporters, which are absent in pathogenic Pseudomonas strains. Inactivation of these transporter genes abolished RA uptake, destabilized the synthetic community, and eliminated protection against Pseudomonas syringae infection. RA and Rhodotorula also induced host iron-deficiency and jasmonate-related defense metabolites, linking microbial cooperation to plant stress responses. These findings reveal that microbial siderophore exchange acts as a key mechanism that maintains stability in the phyllosphere microbiome. Rather than solely promoting competition, iron-binding compounds can serve as cooperative currencies that align microbial fitness with host protection.

microbiology↗

Computational pipeline reveals nature's untapped reservoir of halogenating enzymes

Microbial halogenated natural products (hNPs) hold ecological, agricultural, and biomedical relevance. The hNP-producing potential of the organism can be assessed by the precise prediction of biosynthetic enzymes, yet the detailed annotations of halogenases are often missing from genomic and metagenomic data. We created a manually curated database (https://halogenases.secondarymetabolites.org/) containing information on the halide-specificity, role, and position of verified catalytic residues and results of the mutagenesis studies of more than 120 experimentally validated or in silico inferred halogenases. The collection of experimental data supports a computational pipeline that allows the family-, substrate-, and halide-scope-level annotation of halogenating enzymes by relying on catalytic residues, conserved motifs, and profile Hidden Markov Models (pHMMs). Our analysis with sequence similarity networks (SSNs) highlighted several underexplored clusters in the UniRef50 database. Such finding was a halogenase from Rhodopirellula baltica (RhobaVHPO) previously labelled as a hypothetical chloroperoxidase, which clustered apart from the known chloroperoxidases and bromoperoxidases, but accepted chloride and preferred bromide. Our database and workflow provide extensive and scalable solutions for the systematic and precise annotation of halogenating enzymes in genomic and metagenomic data. The in-depth categorization of halogenases will improve the chemical structure prediction of microbial hNPs, supporting ecological assessments and natural product discovery. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/700248v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@ebae51org.highwire.dtl.DTLVardef@10188f0org.highwire.dtl.DTLVardef@1c55684org.highwire.dtl.DTLVardef@b311bd_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

PhyloNaP: a user-friendly database of Phylogeny for Natural Product-producing enzymes

Phylogenetic analysis is widely used to predict enzyme function, yet building annotated and reusable trees is labor-intensive and requires extensive knowledge about the specific enzymes. Existing resources rarely cover biosynthetic enzymes and lack the context needed for meaningful analysis. We present PhyloNaP, the first large-scale resource dedicated to phylogenies of biosynthetic enzymes. PhyloNaP provides [~]18,500 annotated and interactive trees enriched with chemical, functional, and taxonomic information. Users can classify their own sequences via phylogenetic placement, enabling functional inference in an evolutionary context. A contribution portal allows the community to submit curated trees. By combining scale, breadth of annotation, and interactive functionality, PhyloNaP fills a major gap in bioinformatics resources for enzyme discovery and annotation, with immediate applications to secondary metabolism and beyond. Availabilityhttps://phylonap.cs.uni-tuebingen.de

bioinformatics↗

PanBGC: A Pangenome-inspired framework for comparative analysis of biosynthetic gene clusters

AO_SCPLOWBSTRACTC_SCPLOWBacterial secondary metabolites are a major source of therapeutics and play key roles in microbial ecology. These compounds are encoded by biosynthetic gene clusters (BGCs), which show extensive genetic diversity across microbial genomes. While recent advances have enabled clustering of BGCs into gene cluster families (GCFs), there is still a lack of frameworks for systematically analysing their internal diversity at a population scale. Here, we introduce PanBGC, a pangenome-inspired framework that treats each GCF as a population of related BGCs. This enables classification of biosynthetic genes into core, accessory, and unique categories and provides openness metrics to quantify compositional diversity. Applied to over 250 000 BGCs from more than 35 000 genomes, PanBGC maps biosynthetic diversity of more than 80 000 GCFs. To facilitate exploration, we present PanBGC-DB (https://panbgc-db.cs.uni-tuebingen.de), an interactive web platform for comparative BGC analysis. PanBGC-DB offers gene- and domain-level visualizations, phylogenetic tools, openness metrics, and custom query integration. Together, PanBGC and PanBGC-DB provide a scalable framework for exploring biosynthetic gene clusters at population resolution and for contextualizing newly discovered BGCs within the global landscape of secondary metabolism.

microbiology↗

Automated genome mining predicts structural diversity and taxonomic distribution of peptide metallophores across bacteria

Microbial competition for trace metals shapes their communities and interactions with humans and plants. Many bacteria scavenge trace metals with metallophores, small molecules that chelate environmental metal ions. Metallophore production may be predicted by genome mining, where genomes are scanned for homologs of known biosynthetic gene clusters (BGCs). However, accurately detecting non-ribosomal peptide (NRP) metallophore biosynthesis requires expert manual inspection, stymieing large-scale investigations. Here, we introduce automated identification of NRP metallophore BGCs through a comprehensive algorithm, implemented in antiSMASH, that detects chelator biosynthesis genes with 97% precision and 78% recall against manual curation. We showcase the utility of the detection algorithm by experimentally characterizing metallophores from several taxa. High-throughput NRP metallophore BGC detection enabled metallophore detection across 69,929 genomes spanning the bacterial kingdom. We predict that 25% of all bacterial non-ribosomal peptide synthetases encode metallophore production and that significant chemical diversity remains undiscovered. A reconstructed evolutionary history of NRP metallophores supports that some chelating groups may predate the Great Oxygenation Event. The inclusion of NRP metallophore detection in antiSMASH will aid non-expert researchers and continue to facilitate large-scale investigations into metallophore biology.

biochemistry↗

Ultra-deep long-read metagenomics captures diverse taxonomic and biosynthetic potential of soil microbes

BackgroundSoil ecosystems have long been recognised as hotspots of microbial diversity, but most estimates of their microbial and functional complexity remain speculative despite decades of study, in part because conventional sequencing campaigns lack the depth and contiguity required to recover low-abundance and repetitive genomes. Here, we revisit this question using one of the deepest metagenomic sequencing efforts to date, applying 148 billion base pairs of Nanopore long-read and 122 billion base pairs of Illumina short-read data to a single forest soil sample. ResultsOur hybrid assembly reconstructed 837 metagenome-assembled genomes, including 466 that meet high- and medium-quality standards, nearly all lacking close relatives among cultivated taxa. Rarefaction and k-mer analyses reveal that, even at this depth, we capture only a fraction of the extant diversity: non-parametric models project that more than ten trillion base pairs of sequencing data would be required to approach saturation. These findings offer a quantitative, technology-enabled update to long-standing diversity estimates and demonstrate that conventional metagenomic sequencing efforts likely miss the majority of microbial and biosynthetic potential in soil. We further identify more than 11 000 biosynthetic gene clusters, over 99% of which have no match in current databases, underscoring the breadth of unexplored metabolic capacity. ConclusionsTaken together, our results emphasise both the power and the present limitations of metagenomics in resolving natural microbial complexity, and they provide a new baseline for evaluating future advances in microbial genome recovery, taxonomic classification, and natural product discovery. Data DescriptionIn order to quantify how much taxonomic and biosynthetic novelty present-day sequencing can recover from soil, we extracted high-molecular-weight DNA from a single Cambisol forest soil sample (Schonbuch, Germany) and generated an ultra-deep 270 Gbp dataset - 148 Gbp of Oxford Nanopore PromethION long reads (read-length N50 = 12.2 kb) plus 122 Gbp of Illumina NovaSeq reads. The hybrid metagenome assembly we conducted produced a 10.5 Gbp assembly, from which multi-tool binning and subsequent refinement recovered 837 MAGs, and antiSMASH/BiG-SCAPE annotation revealed more than 11 000 largely novel biosynthetic gene clusters, creating a resource for benchmarking assembly or binning pipelines, modelling diversity-coverage relationships and mining natural products; all raw reads are archived under ENA BioProject PRJEB89893 [1], and the polished assembly, MAG set and BGC catalogue are available via Zenodo for unrestricted reuse [2, 3]. Key PointsO_LIUltra-deep hybrid sequencing (148 Gbp Nanopore + 122 Gbp Illumina) of a single forest soil sample yielded 837 metagenome-assembled genomes, all lacking cultured counterparts. C_LIO_LIDespite this unprecedented 270 Gbp depth, rarefaction and coverage modelling indicate that more than 10 Tbp of data would still be needed to reach saturation in soil. C_LIO_LIOnly 0.7% and 16.7% of all assembled contigs can be assigned to a species and genus, respectively, with at least one cultured representative, highlighting an unprecedented level of novelty in soil. C_LIO_LIThe assembly uncovers 11 381 biosynthetic gene clusters forming over 10 000 mostly novel families, spotlighting an immense, untapped reservoir of microbial natural-product potential. C_LI

microbiology↗

BGC Atlas: A Web Resource for Exploring the Global Chemical Diversity Encoded in Bacterial Genomes

Secondary metabolites are compounds not essential for an organisms development, but provide significant ecological and physiological benefits. These compounds have applications in medicine, biotechnology, and agriculture. Their production is encoded in biosynthetic gene clusters (BGCs), groups of genes collectively directing their biosynthesis. The advent of metagenomics has allowed researchers to study BGCs directly from environmental samples, identifying numerous previously unknown BGCs encoding unprecedented chemistry. Here, we present the BGC Atlas (https://bgc-atlas.cs.uni-tuebingen.de), a web resource that facilitates the exploration and analysis of BGC diversity in metagenomes. The BGC Atlas identifies and clusters BGCs from publicly available datasets, offering a centralized database and a web interface for metadata-aware exploration of BGCs and gene cluster families (GCFs). We analyzed over 35,000 datasets from MGnify, identifying nearly 1.8 million BGCs, which were clustered into GCFs. The analysis showed that ribosomally synthesized and post-translationally modified peptides (RiPPs) are the most abundant compound class, with most GCFs exhibiting high environmental specificity. We believe that our tool will enable researchers to easily explore and analyze the BGC diversity in environmental samples, significantly enhancing our understanding of bacterial secondary metabolites, and promote the identification of ecological and evolutionary factors shaping the biosynthetic potential of microbial communities.

bioinformatics↗

Strong pairwise Interactions do not Drive Interactions in a Plant Leaf Associated Microbial Community

Microbial communities that promote plant growth show promise in reducing the impacts of climate change on plant health and productivity. Understanding microbe-microbe interactions in a community context is paramount for designing effective microbial consortia that enhance plant resilience. In this study, we investigated the dynamics of a synthetic microbial community (SynCom) assembled from Arabidopsis thaliana leaves to elucidate factors shaping community composition and stability. We found notable disparities between in vitro pairwise interactions and those inferred from correlation networks in planta. Our findings suggested that secondary metabolites, particularly antimicrobials, might mediate interactions in vitro, but fade into the background in the community context. Through co-cultivation experiments, we identified the siderophore pseudobactin as a potent antimicrobial agent against several SynCom members, but its impact on community composition in planta was negligible. Notably, dominant SynCom members, such as Pseudomonas koreensis, Flavobacterium pectinovorum, and Sporobolomyces roseus, exhibited only positive correlations, suggesting synergism based on for example exopolysaccharides and biotransformation might drive community dynamics rather than competition. Two correlations between SynCom members in the co-abundance network corresponded with their pairwise in vitro interactions, highlighting the potential for further research, and demonstrating the usefulness of correlation networks in identifying key microbe-microbe interactions. Our findings highlight the importance of considering microbiome-wide interaction studies and synthetic communities in understanding and manipulating plant microbiomes.

microbiology↗

Goldfinder: Unraveling Networks of Gene Co-occurrence and Avoidance in Bacterial Pangenomes

The pangenome is the set of all genes present in a prokaryotic species. Most pangenomes contain many accessory genes that are present in only some of the species members. Genes need to function together, and it has been suggested that selection for certain gene combinations affects the structure of prokaryotic pangenomes. Nevertheless, genes might also co-occur simply due to being linked on the genome, and efficient tools are needed to distinguish linkage from co-selection. Here we present Goldfinder, an approach to infer co-occurrence and co-avoidance between gene pairs by taking the phylogenetic relationships of the species into account. The approach is implemented in an efficient Python script available at https://github.com/fbaumdicker/goldfinder. We also provide scripts for clustering co-occurring genes and for visualizing the resulting co-occurrence and co-avoidance networks in Cytoscape. In comparison to the co-occurrence inference tool Coinfinder, Goldfinder finds fewer co-occurring pairs in a real species pangenome, suggesting that fewer spurious associations due to phylogenetic dependencies are detected. We conclude that Goldfinder is a fast and accurate tool to infer gene co-occurrence and co-avoidance, which will enable large-scale analyses to infer co-selected genes across bacterial pangenomes.

bioinformatics↗

Genomic insights into the evolution of secondary metabolism of Escovopsis and its allies, specialized fungal symbionts of fungus-farming ants

The metabolic intimacy of symbiosis often demands the work of specialists. Natural products and defensive secondary metabolites can drive specificity by ensuring infection and propagation across host generations. But in contrast to bacteria, little is known about the diversity and distribution of natural product biosynthetic pathways among fungi and how they evolve to facilitate symbiosis and adaptation to their host environment. In this study, we define the secondary metabolism of Escovopsis and closely related genera, members of which are specialized, diverse ascomycete fungi best known as mycoparasites of the fungal cultivars grown by fungus-growing ants. We ask how the gain and loss of various biosynthetic pathways corresponds to divergent lifestyles. Long-read sequencing allowed us to define the chromosomal features of representative Escovopsis strains, revealing highly reduced genomes (21.4-38.3 Mb) composed of 7-8 chromosomes. Escovopsis genomes are highly co-linear, with genes localizing not only in the same chromosome, but also in the same order. Macrosynteny is high within Escovopsis clades, and decreases with increasing phylogenetic distance, while maintaining a high degree of mesosynteny. To explore the evolutionary history of biosynthetic pathways in this group of symbionts relative to their encoding lineages, we performed an ancestral state reconstruction analysis, which revealed that, while many secondary metabolites are shared with non-ant associated sordariomycetes, 56 pathways are unique to the symbiotic genera. Reflecting adaptation to diverging ant agricultural systems, we observe that the stepwise acquisition of these pathways mirrors the ecological radiations of attine ants and the dynamic recruitment and replacement of their fungal cultivars. As different clades encode characteristic combinations of biosynthetic gene clusters, these delineating profiles provide important insights into the possible mechanisms underlying specificity between these symbionts and their hosts. Collectively, our findings shed light on the evolutionary dynamic nature of secondary metabolism in Escovopsis and its allies, reflecting adaptation of the symbionts to an ancient agricultural system.

evolutionary biology↗

Phylogenetic distance and structural diversity directing a reclassification of glycopeptide antibiotics

Glycopeptide antibiotics (GPAs) are key agents against multidrug-resistant Gram-positive pathogens, yet both the term "glycopeptide" and the current GPA type I-V classification framework have become increasingly strained as structurally and mechanistically divergent members continue to be discovered. In particular, compounds historically grouped as "type V GPAs" differ from classical GPAs in features such as glycosylation, peptide length, and reported mode of action, raising the question of whether they belong to the same natural product class. Here, a curated dataset of GPA-associated biosynthetic gene clusters (BGCs) is analysed by combining fingerprint similarity of the products with phylogenetic analysis of the BGCs. Fingerprint-based structural similarity networks and BGC similarity comparisons reveal a pronounced separation between classical lipid II-binding GPAs (types I-IV) and type V GPAs. Multi-locus phylogenetic analyses of conserved biosynthetic components further support two deeply divergent evolutionary subclasses, consistent with subclass-specific biosynthetic signatures. Together, these results motivate a revised, unambiguous framework in which the broader class is termed xyclopeptides, comprising the subclasses dalabactins (legacy GPA types I-IV) and murobactins (legacy type V).

genetics↗

A global survey of specialized metabolic diversity encoded in bacterial genomes

Bacterial specialized metabolites are a proven source of antibiotics and cancer therapies, but whether we have sampled all the secondary metabolite chemical diversity of cultivated bacteria is not known. We analysed ~ 170,000 bacterial genomes and ~ 47,000 metagenome assembled genomes (MAGs) using a modified BiG-SLiCE and the new clust-o-matic algorithm. We found that only 3% of the natural products potentially encoded in bacterial genomes have been experimentally characterized. We show that the variation of secondary metabolite biosynthetic diversity drops significantly at the genus level, identifying it as an appropriate taxonomic rank for comparison. Equal comparison of genera based on Relative Evolutionary Distance revealed that Streptomyces bacteria encode the largest biosynthetic diversity by far, with Amycolatopsis, Kutzneria and Micromonospora also encoding substantial diversity. Finally we find that several less-well-studied taxa, such as Weeksellaceae (Bacteroidota), Myxococcaceae (Myxococcota), Pleurocapsa and Nostocaceae (Cyanobacteria), have potential to produce highly diverse sets of secondary metabolites that warrant further investigation.

genomics↗