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Bagci, C.

Publications and source records attributed to Bagci, C..

12 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↗

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↗

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↗

Using AnnoTree to get more assignments, faster, in DIAMOND+MEGAN microbiome analysis

In microbiome analysis, one main approach is to align metagenomic sequencing reads against a protein-reference database such as NCBI-nr, and then to perform taxonomic and functional binning based on the alignments. This approach is embodied, for example, in the standard DIAMOND+MEGAN analysis pipeline, which first aligns reads against NCBI-nr using DIAMOND and then performs taxonomic and functional binning using MEGAN. Here we propose the use of the AnnoTree protein database, rather than NCBI-nr, in such alignment-based analyses to determine the prokaryotic content of metagenomic samples. We demonstrate a 2-fold speedup over the usage of the prokaryotic part of NCBI-nr, and increased assignment rates, in particular, assigning twice as many reads to KEGG. In addition to binning to the NCBI taxonomy, MEGAN now also bins to the GTDB taxonomy. IMPORTANCEThe NCBI-nr database is not explicitly designed for the purpose of microbiome analysis and its increasing size makes its unwieldy and computationally expensive for this purpose. The AnnoTree protein database is only one quarter the size of the full NCBI-nr database and is explicitly designed for metagenomic analysis, and so should be supported by alignment-based pipelines.

bioinformatics↗

Phylogenetic context using phylogenetic outlines

AO_SCPLOWBSTRACTC_SCPLOWMicrobial studies typically involve the sequencing and assembly of draft genomes for individual microbes or whole microbiomes. Given a draft genome, one first task is to determine its phylogenetic context, that is, to place it relative to the set of related reference genomes. We provide a new interactive graphical tool that addresses this task using Mash sketches to compare against all bacterial and archaeal representative genomes in the GTDB taxonomy, all within the framework of SplitsTree5. The phylogenetic context of the query sequences is then displayed as a phylogenetic outline, a new type of phylogenetic network that is more general that a phylogenetic tree, but significantly less complex than other types of phylogenetic networks. We propose to use such networks, rather than trees, to represent phylogenetic context, because they can express uncertainty in the placement of taxa, whereas a tree must always commit to a specific branching pattern. We illustrate the new method using a number of draft genomes of different assembly quality.

bioinformatics↗

The isolate Caproiciproducens sp. 7D4C2 produces n-caproate at mildly acidic conditions from hexoses: genome and rBOX comparison with related strains and chain-elongating bacteria

BackgroundBulk production of medium-chain carboxylates (MCCs) with 6-12 carbon atoms is of great interest to biotechnology. Open cultures (e.g., reactor microbiomes) have been utilized to generate MCCs in bioreactors. When in-line MCC extraction and prevention of product inhibition is required, the bioreactors have been operated at mildly acidic pH (5.0-5.5). However, model chain-elongating bacteria grow optimally at neutral pH values. Here, we isolated a chain-elongating bacterium (strain 7D4C2) that thrives at mildly acidic pH. We studied its metabolism and compared its whole genome and the reverse {beta}-oxidation (rBOX) genes to other bacteria. ResultsStrain 7D4C2 produces lactate, acetate, n-butyrate, n-caproate, biomass, and H2/CO2 from hexoses. With only fructose as substrate (pH 5.5), the maximum n-caproate specificity (i.e., products per other carboxylates produced) was 60.9 {+/-} 1.5%. However, this was considerably higher at 83.1 {+/-} 0.44% when both fructose and n-butyrate (electron acceptor) were combined as a substrate. A comparison of serum bottles with fructose and n-butyrate with an increasing pH value from 4.5 to 9.0 showed a decreasing n-caproate specificity from [~]92% at mildly acidic pH (pH 4.5-5.0) to [~]24% at alkaline pH (pH 9.0). Moreover, when carboxylates were extracted from the broth (undissociated n-caproic acid was [~]0.3 mM), the n-caproate selectivity (i.e., product per substrate fed) was 42.6 {+/-} 19.0% higher compared to serum bottles without extraction. Based on the 16S rRNA gene sequence, strain 7D4C2 is most closely related to the isolates Caproicibacter fermentans (99.5%) and Caproiciproducens galactitolivorans (94.7%), which are chain-elongating bacteria that are also capable of lactate production. Whole-genome analyses indicate that strain 7D4C2, C. fermentans, and C. galactitolivorans belong to the same genus of Caproiciproducens. Their rBOX genes are conserved and located next to each other, forming a gene cluster, which is different than for other chain-elongating bacteria such as Megasphaera spp. ConclusionsCaproiciproducens spp., comprising strain 7D4C2, C. fermentans, C. galactitolivorans, and several unclassified strains, are chain-elongating bacteria that encode a highly conserved rBOX gene cluster. Caproiciproducens sp. 7D4C2 (DSM 110548) was studied here to understand n-caproate production better at mildly acidic pH within microbiomes and has the additional potential as a pure-culture production strain to convert sugars into n-caproate.

microbiology↗

Annotated bacterial chromosomes from frame-shift-corrected long read metagenomic data

BackgroundShort-read sequencing technologies have long been the work-horse of microbiome analysis. Continuing technological advances are making the application of long-read sequencing to metagenomic samples increasingly feasible.\n\nResultsWe demonstrate that whole bacterial chromosomes can be obtained from a complex community, by application of MinION sequencing to a sample from an EBPR bio-reactor, producing 6Gb of sequence that assembles in to multiple closed bacterial chromosomes. We provide a simple pipeline for processing such data, which includes a new approach to correcting erroneous frame-shifts.\n\nConclusionsAdvances in long read sequencing technology and corresponding algorithms will allow the routine extraction of whole chromosomes from environmental samples, providing a more detailed picture of individual members of a microbiome.

bioinformatics↗

MEGAN-LR: New algorithms allow accurate binning and easy interactive exploration of metagenomic long reads and contigs

BackgroundThere are numerous computational tools for taxonomic or functional analysis of microbiome samples, optimized to run on hundreds of millions of short, high quality sequencing reads. Programs such as MEGAN allow the user to interactively navigate these large datasets. Long read sequencing technologies continue to improve and produce increasing numbers of longer reads (of varying lengths in the range of 10k-1M bps, say), but of low quality. There is an increasing interest in using long reads in microbiome sequencing and there is a need to adapt short read tools to long read datasets.\n\nMethodsWe describe a new LCA-based algorithm for taxonomic binning, and an interval-tree based algorithm for functional binning, that are explicitly designed for long reads and assembled contigs. We provide a new interactive tool for investigating the alignment of long reads against reference sequences. For taxonomic and functional binning, we propose to use LAST to compare long reads against the NCBI-nr protein reference database so as to obtain frame-shift aware alignments, and then to process the results using our new methods.\n\nResultsAll presented methods are implemented in the open source edition of MEGAN and we refer to this new extension as MEGAN-LR (MEGAN long read). We evaluate the LAST+MEGAN-LR approach in a simulation study, and on a number of mock community datasets consisting of Nanopore reads, PacBio reads and assembled PacBio reads. We also illustrate the practical application on a Nanopore dataset that we sequenced from an anammox bio-rector community.

bioinformatics↗