Biology subjects

Medema, M. H.

Publications and source records attributed to Medema, M. H..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

MEANtools: multi-omics integration towards metabolite anticipation and biosynthetic pathway prediction

During evolution, plants have developed the ability to produce a vast array of specialized metabolites, which play crucial roles in helping plants adapt to different environmental niches. However, their biosynthetic pathways remain largely elusive. In the past decades, increasing numbers of plant biosynthetic pathways have been elucidated based on approaches utilizing genomics, transcriptomics, and metabolomics. These efforts, however, are limited by the fact that they typically adopt a target-based approach, requiring prior knowledge. Here, we present MEANtools, a systematic and unsupervised computational integrative omics workflow to predict candidate metabolic pathways de novo by leveraging knowledge of general reaction rules and metabolic structures stored in public databases. In our approach, possible connections between metabolites and transcripts that show correlated abundance across samples are identified using reaction rules linked to the transcript-encoded enzyme families. MEANtools thus assesses whether these reactions can connect transcript-correlated mass features within a candidate metabolic pathway. We validate MEANtools using a paired transcriptomic-metabolomic dataset recently generated to reconstruct the falcarindiol biosynthetic pathway in tomato. MEANtools correctly anticipated five out of seven steps of the characterized pathway and also identified other candidate pathways involved in specialized metabolism, which demonstrates its potential for hypothesis generation. Altogether, MEANtools represents a significant advancement to integrate multi-omics data for the elucidation of biochemical pathways in plants and beyond.

bioinformatics↗

bioRxiv2024

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↗

bioRxiv2024

Prediction of gene cluster function based on transcriptional regulatory networks uncovers a novel locus required for desferrioxamine B biosynthesis

Bacteria produce a plethora of natural products that are in clinical, agricultural and biotechnological use. Genome mining revealed millions of biosynthetic gene clusters (BGCs) that encode their biosynthesis, and the major challenge is to predict the bioactivities of the molecules these BGCs specify, and how to elicit their expression. Here, we present an innovative strategy whereby we harness the power of regulatory networks combined with global gene expression patterns to predict BGC functions. Studying the regulon of iron master regulator DmdR1 in Streptomyces coelicolor combined with co-expression data and large-scale comparative genome analysis identified the novel desJGH gene cluster. Mutational and metabolomics analysis showed that desJGH is required for biosynthesis of the clinical drug desferrioxamine B. DesJGH thereby dictate the balance between the structurally distinct desferrioxamines B and E. We propose regulation-based genome mining as a promising approach to functionally prioritize BGCs to accelerate the discovery of novel bioactive molecules.

genomics↗

bioRxiv2024

LogoMotif: a comprehensive database of transcription factor binding site profiles in Actinobacteria

Actinobacteria undergo a complex multicellular life cycle and produce a wide range of specialized metabolites, including the majority of the antibiotics. These biological processes are controlled by intricate regulatory pathways, and to better understand how they are controlled we need to augment our insights into the transcription factor binding sites. Here, we present LogoMotif (https://logomotif.bioinformatics.nl), an open-source database for characterized and predicted transcription factor binding sites in Actinobacteria, along with their cognate position weight matrices and hidden Markov models. Genome-wide predictions of binding site locations in Streptomyces model organisms are supplied and visualized in interactive regulatory networks. In the web interface, users can freely access, download and investigate the underlying data. With this curated collection of actinobacterial regulatory interactions, LogoMotif serves as a basis for binding site predictions, thus providing users with clues on how to elicit the expression of genes of interest and guide genome mining efforts. HighlightsO_LIActinobacterial regulatory networks are key for compound discovery, including antibiotics. C_LIO_LIContains [~]400 validated and [~]12,100 predicted interactions, presented in interactive networks. C_LIO_LIServes as foundation for regulatory predictions in the gene cluster detection tool, antiSMASH. C_LIO_LILogoMotifs data and algorithms provide knowledge on expression and functional inference of genes. C_LIO_LILogoMotif aids in the discovery of novel chemistry within Actinobacteria and beyond. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=74 SRC="FIGDIR/small/582527v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@cfb7cdorg.highwire.dtl.DTLVardef@12c017borg.highwire.dtl.DTLVardef@155096org.highwire.dtl.DTLVardef@15dcbcc_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2022

FERMO: a Dashboard for Streamlined Rationalized Prioritization of Molecular Features from Mass Spectrometry Data

Small molecules shape phenotypic variation by modulating biological processes, yet linking specific molecules to observed traits remains challenging due to the complexity of biological samples. Liquid chromatography-tandem mass spectrometry routinely detects hundreds of molecules per sample, and computational tools aid in the selection of the biologically relevant subset by organizing, annotating, and integrating orthogonal data. Existing tools typically focus on facilitating data-driven exploration to support manual interpretation, rather than more objective, data-driven prioritization and hypothesis-generation. Here, we introduce FERMO, a free online dashboard interface for prioritization of molecular features and samples associated with phenotypes of interest. FERMOs modular framework automates data processing, annotation, and integration of standardized phenotypic and other metadata. FERMO supports both exploratory and targeted analysis through efficient interactive visualization, reproducible prioritization, and data filtering. We demonstrate FERMOs utility in benchmarking studies prioritizing bioactive compounds from complex biological matrices. FERMO is freely available at https://fermo.bioinformatics.nl/.

bioinformatics↗

bioRxiv2022

Taxonomic and metabolic diversity of Actinobacteria isolated from faeces of a 28,000-year-old mammoth

Ancient microbial communities of permafrost soils and frozen animal remains represent an archive that has barely been explored. This yet unexplored microbial world is a vast resource that can provide us with new evolutionary insights, metabolic pathways and novel chemistry. Here, we reveal that Actinobacteria isolated from a faecal sample from the intestinal tract of a 28,000-year-old Siberian mammoth are phylogenetically and metabolically distinct from currently known modern siblings. Ancient Micromonospora, Oerskovia, Saccharopolyspora, Sanguibacter and Streptomyces species were successfully revived and their genome sequences resolved. Surprisingly, the genomes of the ancestors show a large phylogenetic distance to strains isolated today and harbour many novel biosynthetic gene clusters that may well represent uncharacterised biosynthetic potential. Metabolic profiles of the strains display production of known molecules like antimycin, conglobatin and macrotetrolides, but the majority of the mass features could not be dereplicated. Our work provides a snapshot into Actinobacteria of the past, yielding unexplored genomic information that is not yet present in current databases.

microbiology↗

bioRxiv2022

Automated genome mining predicts combinatorial diversity and taxonomic distribution of peptide metallophore structures

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 and transport them back into the cell. Our incomplete knowledge of metallophores diversity stymies our ability to fight infectious diseases and harness beneficial microbiome interactions. The majority of known metallophores are non-ribosomal peptides (NRPs), which feature metal-chelating moieties rarely found in other classes of natural products. NRP metallophore production may be predicted by genome mining, where genomes are scanned for homologs of known biosynthetic gene clusters (BGCs). However, accurately detecting NRP metallophore biosynthesis currently requires expert manual inspection. Here, we introduce automated identification of NRP metallophore BGCs through a comprehensive detection algorithm, newly implemented in antiSMASH. Custom-designed profile hidden Markov models detect genes encoding the biosynthesis of most known NRP metallophore chelating moieties (2,3-dihydroxybenzoate, hydroxamates, salicylate, {beta}-hydroxyamino acids, graminine, Dmaq, and the pyoverdine chromophore), achieving 97% precision and 78% recall against manual curation. We leveraged the algorithm, in combination with transporter gene detection, to detect NRP metallophore BGCs in 15,562 representative bacterial genomes and predict that 25% of all non-ribosomal peptide synthetases encode metallophore production. BiG-SCAPE clustering of 2,562 NRP metallophore BGCs revealed that significant diversity remains unexplored, including new combinations of chelating groups. Additionally, we find that Cyanobacteria are severely understudied and should be the focus of more metallophore isolation efforts. The inclusion of NRP metallophore detection in antiSMASH version 7 will aid non-expert researchers and facilitate large-scale investigations into metallophore biology.

bioinformatics↗

bioRxiv2022

iPRESTO: automated discovery of biosynthetic sub-clusters linked to specific natural product substructures

Microbial specialised metabolism is full of valuable natural products that are applied clinically, agriculturally, and industrially. The genes that encode their biosynthesis are often physically clustered on the genome in biosynthetic gene clusters (BGCs). Many BGCs consist of multiple groups of co-evolving genes called sub-clusters that are responsible for the biosynthesis of a specific chemical moiety in a natural product. Sub-clusters therefore provide an important link between the structures of a natural product and its BGC, which can be leveraged for predicting natural product structures from sequence, as well as for linking chemical structures and metabolomics-derived mass features to BGCs. While some initial computational methodologies have been devised for sub-cluster detection, current approaches are not scalable, have only been run on small and outdated datasets, or produce an impractically large number of possible sub-clusters to mine through. Here, we constructed a scalable method for unsupervised sub-cluster detection, called iPRESTO, based on topic modelling and statistical analysis of co-occurrence patterns of enzyme-coding protein families. iPRESTO was used to mine sub-clusters across 150,000 prokaryotic BGCs from antiSMASH-DB. After annotating a fraction of the resulting sub-cluster families, we could predict a substructure for 16% of the antiSMASH-DB BGCs. Additionally, our method was able to confirm 83% of the experimentally characterised sub-clusters in MIBiG reference BGCs. Based on iPRESTO-detected sub-clusters, we could correctly identify the BGCs for xenorhabdin and salbostatin biosynthesis (which had not yet been annotated in BGC databases), as well as propose a candidate BGC for akashin biosynthesis. Additionally, we show for a collection of 145 actinobacteria how substructures can aid in linking BGCs to molecules by correlating iPRESTO-detected sub-clusters to MS/MS-derived Mass2Motifs substructure patterns. This work paves the way for deeper functional and structural annotation of microbial BGCs by improved linking of orphan molecules to their cognate gene clusters, thus facilitating accelerated natural product discovery. Author summaryIn this work, we introduce iPRESTO, a tool for scalable unsupervised sub-cluster detection in biosynthetic gene clusters. This detection is important because these biosynthetic hotspots encode many products useful for humanity, such as antibiotics, antitumor agents, or herbicides. Recent technological developments have made identification of biosynthetic loci in genomes straightforward. Yet, methods to connect these inferred biosynthetic genes to the final chemical structures of their cognate metabolites are largely lacking. Being able to reliably predict parts of the final product would constitute a real step forward in natural product genome mining. Therefore, we focussed on constructing a tool to systematically detect and annotate small regions called sub-clusters, which code for the biosynthesis of substructures in the final product, across all genomically inferred biosynthetic diversity. iPRESTO makes it possible to query unknown biosynthetic regions and infer which substructures are present in their metabolic products. This will facilitate more effective prioritization of chemical novelty, as well as linking activities from bioassays and microbiome-associated phenotypes to the metabolites responsible for them.

bioinformatics↗

bioRxiv2022

Comparative metagenomic analysis of biosynthetic diversity across sponge microbiomes highlights metabolic novelty, conservation and diversification

Marine sponges and their microbial symbiotic communities are rich sources of diverse natural products (NPs) that often display biological activity, yet little is known about their global distribution landscape and the symbionts that produce them. As the majority of sponge symbionts remain uncultured, it is a challenge to characterize their NP biosynthetic pathways, assess their prevalence within the holobiont and measure their diversity across sponge taxa and environments. Here, we explore the microbial biosynthetic landscapes of three sponge species from the Atlantic Ocean and the Mediterranean Sea. This dataset reveals striking novelty in its encoded biosynthetic potential, with less than 1% of the recovered gene cluster families (GCF) showing similarity to any characterized biosynthetic gene cluster (BGC). When zooming in on the microbial communities of each sponge, we observed higher variability of both secondary metabolic and taxonomic profiles between sponge species than within species. Nonetheless, we also identified conservation of GCFs, with 20% of sponge GCFs being shared between at least two sponge species, and a true GCF core comprised of 6% of GCFs shared across all species. Within this functional core, we identified a set of widespread and diverse GCFs encoding nonribosomal peptide synthetases (NRPS) that are potentially involved in the production of diversified ether lipids, as well as GCFs putatively encoding the production of highly modified proteusins. The present work contributes to the small, yet growing body of data characterizing NP landscapes of marine sponge symbionts, and to the cryptic biosynthetic potential contained in this environmental niche.

microbiology↗

bioRxiv2021

Disentangling the genetic basis of rhizosphere microbiome assembly in tomato

Microbiomes play a pivotal role in plant growth and health, but the genetic factors involved in microbiome assembly remain largely elusive. Here, 16S amplicon and metagenomic features of the rhizosphere microbiome were mapped as quantitative traits of a recombinant inbred line population of a cross between wild and domesticated tomato. Gene content analysis of prioritized tomato QTLs suggested a genetic basis for differential recruitment of various rhizobacterial lineages, including a Streptomyces-associated 6.31-Mbp region harboring tomato domestication sweeps and encoding, among others, the iron regulator FIT and the aquaporin SlTIP2.3. Within metagenome-assembled genomes of the rhizobacterial lineages Streptomyces and Cellvibrio, we identified microbial genes involved in metabolism of plant polysaccharides, iron, sulfur, trehalose, and vitamins, whose genetic variation associated with either modern or wild tomato QTLs. Integrating microbiomics and quantitative plant genetics pinpointed putative plant and reciprocal microbial traits underlying microbiome assembly, thereby providing the first step towards plant-microbiome breeding programs.

microbiology↗

bioRxiv2021

TbasCO: Trait-based Comparative 'Omics Identifies Ecosystem-Level and Niche-Differentiating Adaptations of an Engineered Microbiome

A grand challenge in microbial ecology is disentangling the traits of individual populations within complex communities. Various cultivation-independent approaches have been used to infer traits based on the presence of marker genes. However, marker genes are not linked to traits with complete fidelity, nor do they capture important attributes, such as the timing of expression or coordination among traits. To address this, we present an approach for assessing the trait landscape of microbial communities by statistically defining a trait attribute as shared transcriptional pattern across multiple organisms. Leveraging the KEGG pathway database as a trait library and the Enhanced Biological Phosphorus Removal (EBPR) model microbial ecosystem, we demonstrate that a majority (65%) of traits present in 10 or more genomes have niche-differentiating expression attributes. For example, while 14 genomes containing the high-affinity phosphorus transporter pstABCS display a canonical attribute (e.g. up-regulation under phosphorus starvation), we identified another attribute shared by 11 genomes where transcription was highest under high phosphorus conditions. Taken together, we provide a novel framework for revealing hidden metabolic versatility when investigating genomic data alone by assigning trait-attributes through genome-resolved time-series metatranscriptomics.

genomics↗

bioRxiv2021

Whokaryote: distinguishing eukaryotic and prokaryotic contigs in metagenomes based on gene structure

Metagenomics has become a prominent technology to study the functional potential of all organisms in a microbial community. Most studies focus on the bacterial content of these communities, while ignoring eukaryotic microbes. Indeed, many metagenomics analysis pipelines silently assume that all contigs in a metagenome are prokaryotic. However, because of marked differences in gene structure, prokaryotic gene prediction tools fail to accurately predict eukaryotic genes. Here, we developed a classifier that distinguishes eukaryotic from prokaryotic contigs based on foundational differences between these taxa in gene structure. We first developed a random forest classifier that uses intergenic distance, gene density and gene length as the most important features. We show that, with an estimated accuracy of 97%, this classifier with principled features grounded in biology can perform almost as well as the classifiers EukRep and Tiara, which use k-mer frequencies as features. By re-training our classifier with Tiara predictions as additional feature, weaknesses of both types of classifiers are compensated; the result is an enhanced classifier that outperforms all individual classifiers, with an F1-score of 1.00 on precision, recall and accuracy for both eukaryotes and prokaryotes, while still being fast. In a reanalysis of metagenome data from a disease-suppressive plant endosphere microbial community, we show how using Whokaryote to select contigs for eukaryotic gene prediction facilitates the discovery of several biosynthetic gene clusters that were missed in the original study. Our enhanced classifier, which we call Whokaryote, is wrapped in an easily installable package and is freely available from https://git.wageningenur.nl/lotte.pronk/whokaryote.

bioinformatics↗

bioRxiv2021

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↗

bioRxiv2021

Generation of lineage-resolved complete metagenome-assembled genomes by precision phasing

Microbial communities in many environments include distinct lineages of closely related organisms which have proved challenging to separate in metagenomic assembly, preventing generation of complete metagenome-assembled genomes (MAGs). The advent of long and accurate HiFi reads presents a possible means to address this challenge by generating complete MAGs for nearly all sufficiently abundant bacterial genomes in a microbial community. We present a metagenomic HiFi assembly of a complex microbial community from sheep fecal material that resulted in 428 high-quality MAGs from a single sample, the highest resolution achieved with metagenomic deconvolution to date. We applied a computational approach to separate distinct haplotype lineages and identified haplotypes of hundreds of variants across hundreds of kilobases of genomic sequence. Analysis of these haplotypes revealed 220 lineage-resolved complete MAGs, including 44 in single circular contigs, and demonstrated improvement in overall assembly compared to error-prone long reads. We report the characterization of multiple, closely-related microbes within a sample with potential to improve precision in assigning mobile genetic elements to host genomes within complex microbial communities.

microbiology↗

bioRxiv2021

A systematic analysis of metabolic pathways in the human gut microbiota

The gut microbiota produce hundreds of small molecules, many of which modulate host physiology. Although efforts have been made to identify biosynthetic genes for secondary metabolites, the chemical output of the gut microbiome consists predominantly of primary metabolites. Here, we systematically profile primary metabolic genes from the gut microbiome, identifying 19,885 gene clusters in 4,240 high-quality microbial genomes. We find marked differences in pathway distribution among phyla, reflecting distinct strategies for energy capture. These data explain taxonomic differences in short-chain fatty acid production and suggest a characteristic metabolic niche for each taxon. Analysis of 1,135 subjects from a Dutch population-based cohort shows that the level of 14 microbiome-derived metabolites in plasma is almost completely uncorrelated with the metagenomic abundance of the corresponding biosynthetic genes, revealing a crucial role for pathway-specific gene regulation and metabolite flux. This work is a starting point for understanding differences in how bacterial taxa contribute to the chemistry of the microbiome.

bioinformatics↗

bioRxiv2020

BiG-MAP: an automated pipeline to profile metabolic gene cluster abundance and expression in microbiomes

Microbial gene clusters encoding the biosynthesis of primary and secondary metabolites play key roles in shaping microbial ecosystems and driving microbiome-associated phenotypes. Although effective approaches exist to evaluate the metabolic potential of such bacteria through identification of metabolic gene clusters in their genomes, no automated pipelines exist to profile the abundance and expression levels of such gene clusters in microbiome samples to generate hypotheses about their functional roles and to find associations with phenotypes of interest. Here, we describe BiG-MAP, a bioinformatic tool to profile abundance and expression levels of gene clusters across metagenomic and metatranscriptomic data and evaluate their differential abundance and expression between different conditions. To illustrate its usefulness, we analyzed 47 metagenomic samples from healthy and caries-associated human oral microbiome samples and identified 58 gene clusters, including unreported ones, that were significantly more abundant in either phenotype. Among them, we found the muc operon, a gene cluster known to be associated to tooth decay. Additionally, we found a putative reuterin biosynthetic gene cluster from a Streptococcus strain to be enriched but not exclusively found in healthy samples; metabolomic data from the same samples showed masses with fragmentation patterns consistent with (poly)acrolein, which is known to spontaneously form from the products of the reuterin pathway and has been previously shown to inhibit pathogenic Streptococcus mutans strains. Thus, we show how BiG-MAP can be used to generate new hypotheses on potential drivers of microbiome-associated phenotypes and prioritize the experimental characterization of relevant gene clusters that may mediate them. ImportanceMicrobes play an increasingly recognized role in determining host-associated phenotypes by producing small molecules that interact with other microorganisms or host cells. The production of these molecules is often encoded in syntenic genomic regions, also known as gene clusters. With the increasing numbers of (multi-)omics datasets that can help understanding complex ecosystems at a much deeper level, there is a need to create tools that can automate the process of analyzing these gene clusters across omics datasets. The current study presents a new software tool called BiG-MAP, which allows assessing gene cluster abundance and expression in microbiome samples using metagenomic and metatranscriptomic data. In this manuscript, we describe the tool and its functionalities, and how it has been validated using a mock community. Finally, using an oral microbiome dataset, we show how it can be used to generate hypotheses regarding the functional roles of gene clusters in mediating host phenotypes.

bioinformatics↗

bioRxiv2020

BiG-SLiCE: A Highly Scalable Tool Maps the Diversity of 1.2 Million Biosynthetic Gene Clusters

BackgroundGenome mining for Biosynthetic Gene Clusters (BGCs) has become an integral part of natural product discovery. The >200,000 microbial genomes now publicly available hold information on abundant novel chemistry. One way to navigate this vast genomic diversity is through comparative analysis of homologous BGCs, which allows identification of cross-species patterns that can be matched to the presence of metabolites or biological activities. However, current tools suffer from a bottleneck caused by the expensive network-based approach used to group these BGCs into Gene Cluster Families (GCFs). ResultsHere, we introduce BiG-SLiCE, a tool designed to cluster massive numbers of BGCs. By representing them in Euclidean space, BiG-SLiCE can group BGCs into GCFs in a non-pairwise, near-linear fashion. We used BiG-SLiCE to analyze 1,225,071 BGCs collected from 209,206 publicly available microbial genomes and metagenome-assembled genomes (MAGs) within ten days on a typical 36-cores CPU server. We demonstrate the utility of such analyses by reconstructing a global map of secondary metabolic diversity across taxonomy to identify uncharted biosynthetic potential. BiG-SLiCE also provides a "query mode" that can efficiently place newly sequenced BGCs into previously computed GCFs, plus a powerful output visualization engine that facilitates user-friendly data exploration. ConclusionsBiG-SLiCE opens up new possibilities to accelerate natural product discovery and offers a first step towards constructing a global, searchable interconnected network of BGCs. As more genomes get sequenced from understudied taxa, more information can be mined to highlight their potentially novel chemistry. BiG-SLiCE is available via https://github.com/medema-group/bigslice.

bioinformatics↗

bioRxiv2020

A biaryl-linked tripeptide from Planomonospora leads to widespread class of minimal RiPP gene clusters

Microbial natural products impress by their bioactivity, structural diversity and ingenious biosynthesis. While screening the rare actinobacterial genus Planomonospora, cyclopeptides 1A and 1B were discovered, featuring an unusual Tyr-His biaryl-bridging across a tripeptide scaffold, with the sequences N-acetyl-Tyr-Tyr-His (1A) and N-acetyl-Tyr-Phe-His (1B). Genome analysis of the 1A producing strain pointed to-wards a ribosomal synthesis of 1A, from a pentapeptide precursor encoded by the tiny 18-nucleotide gene bycA, to our knowledge the smallest gene ever reported. Further, biaryl instalment is performed by the closely linked gene bycB, encoding a cytochrome P450 monooxygenase. Biosynthesis of 1A was confirmed by heterologous production in Streptomyces, yielding the mature product. Bioinformatic analysis of related cytochrome P450 monooxygenases indicated that they constitute a widespread family of pathways, associated to 5-aa coding sequences in approximately 200 (actino)bacterial genomes, all with potential for a biaryl linkage between amino acids 1 and 3. We propose the name biarylicins for this newly discovered family of RiPPs.

microbiology↗