Biology subjects

Medema, M. H.

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

49 records · Page 3Linked to original sources

bioRxiv2020

Integration of machine learning and pan-genomics expands the biosynthetic landscape of RiPP natural products

Most clinical drugs are based on microbial natural products, with compound classes including polyketides (PKS), non-ribosomal peptides (NRPS), fluoroquinones and ribosomally synthesized and post-translationally modified peptides (RiPPs). While variants of biosynthetic gene clusters (BGCs) for known classes of natural products are easy to identify in genome sequences, BGCs for new compound classes escape attention. In particular, evidence is accumulating that for RiPPs, subclasses known thus far may only represent the tip of an iceberg. Here, we present decRiPPter (Data-driven Exploratory Class-independent RiPP TrackER), a RiPP genome mining algorithm aimed at the discovery of novel RiPP classes. DecRiPPter combines a Support Vector Machine (SVM) that identifies candidate RiPP precursors with pan-genomic analyses to identify which of these are encoded within operon-like structures that are part of the accessory genome of a genus. Subsequently, it prioritizes such regions based on the presence of new enzymology and based on patterns of gene cluster and precursor peptide conservation across species. We then applied decRiPPter to mine 1,295 Streptomyces genomes, which led to the identification of 42 new candidate RiPP families that could not be found by existing programs. One of these was studied further and elucidated as a novel subfamily of lanthipeptides, designated Class V. Two previously unidentified modifying enzymes are proposed to create the hallmark lanthionine bridges. Taken together, our work highlights how novel natural product families can be discovered by methods going beyond sequence similarity searches to integrate multiple pathway discovery criteria. Code and data availabilityThe source code of DecRiPPter is freely available online at https://github.com/Alexamk/decRiPPter. Results of the data analysis are available online at http://www.bioinformatics.nl/~medem005/decRiPPter_strict/index.html and http://www.bioinformatics.nl/~medem005/decRiPPter_mild/index.html (for the strict and mild filters, respectively). All training data and code used to generate these, as well as outputs of the data analyses, are available on Zenodo at doi:10.5281/zenodo.3834818.

microbiology↗

bioRxiv2020

RRE-Finder: A Genome-Mining Tool for Class-Independent RiPP Discovery

Nearly half of the classes of natural products known as ribosomally synthesized and post-translationally modified peptides (RiPPs) are reliant on a protein domain called the RiPP recognition element (RRE) for peptide maturation. The RRE binds specifically to a linear precursor peptide and directs the post-translational modification enzymes to their substrate. Given its prevalence across various types of RiPP biosynthetic gene clusters (BGCs), the RRE could theoretically be used as a bioinformatic handle to identify novel classes of RiPPs. In addition, due to the high affinity and specificity of most RRE:precursor peptide complexes, a thorough understanding of the RRE domain could be exploited for biotechnological applications. However, sequence divergence of the RRE domain across RiPP classes has precluded automated identification of RREs based solely on sequence similarity. Here, we introduce RRE-Finder, a novel tool for identifying RRE domains with high sensitivity. RRE-Finder can be used in "precision" mode to confidently identify RREs in a class-specific manner or in "exploratory" mode, which was designed to assist in the discovery of novel RiPP classes. RRE-Finder operating in precision mode on the UniProtKB protein database retrieved over 30,000 high-confidence RREs spanning all characterized RRE-dependent RiPP classes, as well as several yet-uncharacterized RiPP, putatively novel gene cluster architectures that will require future experimental work. Finally, RRE-Finder was used in precision mode to explore a possible evolutionary origin of the RRE domain. Altogether, RRE-Finder provides a powerful new method to probe RiPP biosynthetic diversity and delivers a rich dataset of RRE sequences that will provide a foundation for deeper biochemical studies into this intriguing and versatile protein domain.

bioinformatics↗

bioRxiv2020

Computational genomic discovery of diverse gene clusters harboring Fe-S flavoenzymes in anaerobic gut microbiota

The gut contains an enormous diversity of simple as well as complex molecules from highly diverse food sources as well as host-secreted molecules. This presents a large metabolic opportunity for the gut microbiota, but little is known on how gut microbes are able to catabolize this large chemical diversity. Recently, Fe-S flavoenzymes were found to be key in the transformation of bile acids, catalysing the key step in the 7-dehydroxylation pathway that allows gut bacteria to transform cholic acid (CA) into deoxycholic acid (DCA), an exclusively microbe-derived molecule with major implications for human health. While this enzyme family has also been implicated in a limited number of other catalytic transformations, little is known about the extent to which it is of more global importance in gut microbial metabolism. Here, we use large-scale computational genomic analysis to show that this enzyme superfamily has undergone a remarkable expansion in Clostridiales, and occurs throughout a diverse array of >1,000 different families of putative metabolic gene clusters. Analysis of the enzyme content of these gene clusters suggests that they encode pathways with a wide range of predicted substrate classes, including saccharides, amino acids/peptides and lipids. Altogether, these results indicate a potentially important role of this protein superfamily in the human gut, and our dataset provides significant opportunities for the discovery of novel pathways that may have significant effects on human health.

bioinformatics↗

bioRxiv2019

Extracting the GEMs: Genotype, Environment and Microbiome interactions shaping host phenotypes

One of the fundamental tenets of biology is that the phenotype of an organism (Y) is determined by its genotype (G), the environment (E) and their interaction (GE). Quantitative phenotypes can then be modeled as Y=G+E+GE+e, where e is the biological variance. This simple and tractable model has long served as the basis for studies investigating the heritability of traits and decomposing the variability in fitness. Increasingly, the importance of microbe interactions on organismal phenotypes is being recognized, but it is currently unclear what the relative contribution of microbiomes to a given host phenotype is and how this translates into the traditional GE model. Here we address this fundamental question and propose an expansion of the original model, referred to as GEM, which explicitly incorporates the contribution of the microbiome (M) to the host phenotype, while maintaining the simplicity and tractability of the original GE model. We show that by keeping host, environment and microbiome as separate but interacting variables, the GEM model can capture the nuanced ecological interactions between these variables. Finally, we demonstrate with an in vitro experiment how the GEM model can be used to statistically disentangle the relative contributions of each component on specific host phenotypes.

microbiology↗

bioRxiv2019

Global analysis of adenylate-forming enzymes reveals β-lactone biosynthesis pathway in pathogenic Nocardia

Enzymes that cleave ATP to activate carboxylic acids play essential roles in primary and secondary metabolism in all domains of life. Class I adenylate-forming enzymes share a conserved structural fold but act on a wide range of substrates to catalyze reactions involved in bioluminescence, nonribosomal peptide biosynthesis, fatty acid activation, and {beta}-lactone formation. Despite their metabolic importance, the substrates and catalytic functions of the vast majority of adenylate-forming enzymes are unknown without tools available to accurately predict them. Given the crucial roles of adenylate-forming enzymes in biosynthesis, this also severely limits our ability to predict natural product structures from biosynthetic gene clusters. Here we used machine learning to predict adenylate-forming enzyme function and substrate specificity from protein sequence. We built a web-based predictive tool and used it to comprehensively map the biochemical diversity of adenylate-forming enzymes across >50,000 candidate biosynthetic gene clusters in bacterial, fungal, and plant genomes. Ancestral enzyme reconstruction and sequence similarity networking revealed a hub topology suggesting radial divergence of the adenylate-forming superfamily from a core enzyme scaffold most related to contemporary aryl-CoA ligases. Our classifier also predicted {beta}-lactone synthetases in novel biosynthetic gene clusters conserved across >90 different strains of Nocardia. To test our computational predictions, we purified a candidate {beta}-lactone synthetase from Nocardia brasiliensis and reconstituted the biosynthetic pathway in vitro to link the gene cluster to the {beta}-lactone natural product, nocardiolactone. We anticipate our machine learning approach will aid in functional classification of enzymes and advance natural product discovery.

biochemistry↗

bioRxiv2019

A metabolic pathway for bile acid dehydroxylation by the gut microbiome

The gut microbiota synthesize hundreds of molecules, many of which are known to impact host physiology. Among the most abundant metabolites are the secondary bile acids deoxycholic acid (DCA) and lithocholic acid (LCA), which accumulate at ~500 M and are known to block C. difficile growth1, promote hepatocellular carcinoma2, and modulate host metabolism via the GPCR TGR53. More broadly, DCA, LCA and their derivatives are a major component of the recirculating bile acid pool4; the size and composition of this pool are a target of therapies for primary biliary cholangitis and nonalcoholic steatohepatitis. Despite the clear impact of DCA and LCA on host physiology, incomplete knowledge of their biosynthetic genes and a lack of genetic tools in their native producer limit our ability to modulate secondary bile acid levels in the host. Here, we complete the pathway to DCA/LCA by assigning and characterizing enzymes for each of the steps in its reductive arm, revealing a strategy in which the A-B rings of the steroid core are transiently converted into an electron acceptor for two reductive steps carried out by Fe-S flavoenzymes. Using anaerobic in vitro reconstitution, we establish that a set of six enzymes is necessary and sufficient for the 8-step conversion of cholic acid to DCA. We then engineer the pathway into Clostridium sporogenes, conferring production of DCA and LCA on a non-producing commensal and demonstrating that a microbiome-derived pathway can be expressed and controlled heterologously. These data establish a complete pathway to two central components of the bile acid pool, and provide a road map for deorphaning and engineering pathways from the microbiome as a critical step toward controlling the metabolic output of the gut microbiota.

microbiology↗

bioRxiv2019

Coevolution-based prediction of protein-protein interactions in polyketide biosynthetic assembly lines

Polyketide synthases are multimodular enzymes that generate diverse molecules of great pharmaceutical importance, including a range of clinically used antimicrobials and antitumor agents. Many polyketides are synthesized by type I polyketide synthases (PKSs), which are organized in assembly lines, in which multiple enzymes line up in a specific order. This order is defined by specific protein-protein interactions. The unique modular structure and catalyzing mechanism of these assembly lines makes their products predictable and also spurred combinatorial biosynthesis studies to produce novel polyketides using synthetic biology. However, predicting the interactions of PKSs, and thereby inferring the order of their assembly line, is still challenging, especially for cases in which this order is not reflected by the ordering of the PKS-encoding genes in the genome. Here, we introduce PKSpop, which uses a coevolution-based protein-protein interaction prediction algorithm to infer protein order in PKS assembly lines. Our method accurately predicts protein orders (80% accuracy). Additionally, we identify new residue pairs that are key in determining interaction specificity, and show that coevolution of N- and C-terminal docking domains of PKSs is significantly more predictive for protein-protein interactions than coevolution between ketosynthase and acyl carrier protein domains.

bioinformatics↗

bioRxiv2019

MolNetEnhancer: enhanced molecular networks by integrating metabolome mining and annotation tools

Metabolomics has started to embrace computational approaches for chemical interpretation of large data sets. Yet, metabolite annotation remains a key challenge. Recently, molecular networking and MS2LDA emerged as molecular mining tools that find molecular families and substructures in mass spectrometry fragmentation data. Moreover, in silico annotation tools obtain and rank candidate molecules for fragmentation spectra. Ideally, all structural information obtained and inferred from these computational tools could be combined to increase the resulting chemical insight one can obtain from a data set. However, integration is currently hampered as each tool has its own output format and efficient matching of data across these tools is lacking. Here, we introduce MolNetEnhancer, a workflow that combines the outputs from molecular networking, MS2LDA, in silico annotation tools (such as Network Annotation Propagation or DEREPLICATOR) and the automated chemical classification through ClassyFire to provide a more comprehensive chemical overview of metabolomics data whilst at the same time illuminating structural details for each fragmentation spectrum. We present examples from four plant and bacterial case studies and show how MolNetEnhancer enables the chemical annotation, visualization, and discovery of the subtle substructural diversity within molecular families. We conclude that MolNetEnhancer is a useful tool that greatly assists the metabolomics researcher in deciphering the metabolome through combination of multiple independent in silico pipelines.

biochemistry↗

bioRxiv2018

Comprehensive mass spectrometry-guided plant specialized metabolite phenotyping reveals metabolic diversity in the cosmopolitan plant family Rhamnaceae

Plants produce a myriad of specialized metabolites to overcome their sessile habit and combat biotic as well as abiotic stresses. Evolution has shaped specialized metabolite diversity, which drives many other aspects of plant biodiversity. However, until recently, large-scale studies investigating specialized metabolite diversity in an evolutionary context have been limited by the impossibility to identify chemical structures of hundreds to thousands of compounds in a time-feasible manner. Here, we introduce a workflow for large-scale, semi-automated annotation of specialized metabolites, and apply it for over 1000 metabolites of the cosmopolitan plant family Rhamnaceae. We enhance the putative annotation coverage dramatically, from 2.5 % based on spectral library matches alone to 42.6 % of total MS/MS molecular features extending annotations from well-known plant compound classes into the dark plant metabolomics matter. To gain insights in substructural diversity within the plant family, we also extract patterns of co-occurring fragments and neutral losses, so-called Mass2Motifs, from the dataset; for example, only the Ziziphoid clade developed the triterpenoid biosynthetic pathway, whereas the Rhamnoid clade predominantly developed diversity in flavonoid glycosides, including 7-O-methyltransferase activity. Our workflow provides the foundations towards the automated, high-throughput chemical identification of massive metabolite spaces, and we expect it to revolutionize our understanding of plant chemoevolutionary mechanisms.

plant biology↗

bioRxiv2018

A computational framework for systematic exploration of biosynthetic diversity from large-scale genomic data

Genome mining has become a key technology to explore and exploit natural product diversity through the identification and analysis of biosynthetic gene clusters (BGCs). Initially, this was performed on a single-genome basis; currently, the process is being scaled up to large-scale mining of pan-genomes of entire genera, complete strain collections and metagenomic datasets from which thousands of bacterial genomes can be extracted at once. However, no bioinformatic framework is currently available for the effective analysis of datasets of this size and complexity. Here, we provide a streamlined computational workflow, tightly integrated with antiSMASH and MIBiG, that consists of two new software tools, BiG-SCAPE and CORASON. BiG-SCAPE facilitates rapid calculation and interactive visual exploration of BGC sequence similarity networks, grouping gene clusters at multiple hierarchical levels, and includes a glocal alignment mode that accurately groups both complete and fragmented BGCs. CORASON employs a phylogenomic approach to elucidate the detailed evolutionary relationships between gene clusters by computing high-resolution multi-locus phylogenies of all BGCs within and across gene cluster families (GCFs), and allows researchers to comprehensively identify all genomic contexts in which particular biosynthetic gene cassettes are found. We validate BiG-SCAPE by correlating its GCF output to metabolomic data across 403 actinobacterial strains. Furthermore, we demonstrate the discovery potential of the platform by using CORASON to comprehensively map the phylogenetic diversity of the large detoxin/rimosamide gene cluster clan, prioritizing three new detoxin families for subsequent characterization of six new analogs using isotopic labeling and analysis of tandem mass spectrometric data.

bioinformatics↗

bioRxiv2018

Did a plant-herbivore arms race drive chemical diversity in Euphorbia?

The genus Euphorbia is among the most diverse and species-rich plant genera on Earth, exhibiting a near-cosmopolitan distribution and extraordinary chemical diversity, especially across highly toxic macro-and polycyclic diterpenoids. However, very little is known about drivers and evolutionary origins of chemical diversity within Euphorbia. Here, we investigate 43 Euphorbia species to understand how geographic separation over evolutionary time has impacted chemical differentiation. We show that the structurally highly diverse Euphorbia diterpenoids are significantly reduced in species native to the Americas, compared to the Eurasian and African continents, where the genus originated. The localization of these compounds to young stems and roots suggest ecological relevance in herbivory defense and immunomodulatory defense mechanisms match diterpenoid levels, indicating chemoevolutionary adaptation to reduced herbivory pressure.\n\nOne Sentence SummaryGlobal chemo-evolutionary adaptation of Euphorbia affected immunomodulatory defense mechanisms.

evolutionary biology↗

bioRxiv2018

HEx: a heterologous expression platform for the discovery of fungal natural products

For decades, fungi have been a source of FDA-approved natural products such as penicillin, cyclosporine, and the statins. Recent breakthroughs in DNA sequencing suggest that millions of fungal species exist on Earth with each genome encoding pathways capable of generating as many as dozens of natural products. However, the majority of encoded molecules are difficult or impossible to access because the organisms are uncultivable or the genes are transcriptionally silent. To overcome this bottleneck in natural product discovery, we developed the HEx (Heterologous EXpression) synthetic biology platform for rapid, scalable expression of fungal biosynthetic genes and their encoded metabolites in Saccharomyces cerevisiae. We applied this platform to 41 fungal biosynthetic gene clusters from diverse fungal species from around the world, 22 of which produced detectable compounds. These included novel compounds with unexpected biosynthetic origins, particularly from poorly studied species. This result establishes the HEx platform for rapid discovery of natural products from any fungal species, even those that are uncultivable, and opens the door to discovery of the next generation of natural products.\n\nSummaryHere we present the largest scale effort reported to date toward the complete refactoring and heterologous expression of fungal biosynthetic gene clusters utilizing HEx, a novel synthetic biology platform.

synthetic biology↗

bioRxiv2016

plantiSMASH: automated identification, annotation and expression analysis of plant biosynthetic gene clusters

Plant specialized metabolites are chemically highly diverse, play key roles in host-microbe interactions, have important nutritional value in crops and are frequently applied as medicines. It has recently become clear that plant biosynthetic pathway-encoding genes are sometimes densely clustered in specific genomic loci: biosynthetic gene clusters (BGCs). Here, we introduce plantiSMASH, a versatile online analysis platform that automates the identification of candidate plant BGCs. Moreover, it allows integration of transcriptomic data to prioritize candidate BGCs based on the coexpression patterns of predicted biosynthetic enzyme-coding genes, and facilitates comparative genomic analysis to study the evolutionary conservation of each cluster. Applied on 48 high-quality plant genomes, plantiSMASH identifies a rich diversity of candidate plant BGCs. These results will guide further experimental exploration of the nature and dynamics of gene clustering in plant metabolism. Moreover, spurred by the continuing decrease in costs of plant genome sequencing, they will allow genome mining technologies to be applied to plant natural product discovery.\n\nThe plantiSMASH web server, precalculated results and source code are freely available from http://plantismash.secondarymetabolites.org.

bioinformatics↗