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Marnix H Medema  - - - 
Top co-authors See all
Arnold J. M. Driessen

391 shared publications

Department of Molecular Microbiology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Nijenborgh 7, 9747 AG Groningen, The Netherlands

Huimin Zhao

330 shared publications

Department of Biochemistry

Lubbert Dijkhuizen

318 shared publications

Microbial Physiology, Groningen Biomolecular Sciences and Biotechnology Institute (GBB), University of Groningen, Nijenborgh 7, 9747 AG Groningen, The Netherlands

Oscar P. Kuipers

282 shared publications

Department of Molecular Genetics, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands

Pieter C. Dorrestein

259 shared publications


Publication Record
Distribution of Articles published per year 
(2008 - 2019)
Total number of journals
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Publications See all
Article 0 Reads 0 Citations A genetical metabolomics approach for bioprospecting plant biosynthetic gene clusters Lotte Witjes, Rik Kooke, Justin J. J. Van Der Hooft, Ric C. ... Published: 02 April 2019
BMC Research Notes, doi: 10.1186/s13104-019-4222-3
DOI See at publisher website PubMed View at PubMed ABS Show/hide abstract
Plants produce a plethora of specialized metabolites to defend themselves against pathogens and insects, to attract pollinators and to communicate with other organisms. Many of these are also applied in the clinic and in agriculture. Genes encoding the enzymes that drive the biosynthesis of these metabolites are sometimes physically grouped on the chromosome, in regions called biosynthetic gene clusters (BGCs). Several algorithms have been developed to identify plant BGCs, but a large percentage of predicted gene clusters upon further inspection do not show coexpression or do not encode a single functional biosynthetic pathway. Hence, further prioritization is needed. Here, we introduce a strategy to systematically evaluate potential functions of predicted BGCs by superimposing their locations on metabolite quantitative trait loci (mQTLs). We show the feasibility of such an approach by integrating automated BGC prediction with mQTL datasets originating from a recombinant inbred line (RIL) population of Oryza sativa and a genome-wide association study (GWAS) of Arabidopsis thaliana. In these data, we identified several links for which the enzyme content of the BGCs matches well with the chemical features observed in the metabolite structure, suggesting that this method can effectively guide bioprospecting of plant BGCs. The online version of this article (10.1186/s13104-019-4222-3) contains supplementary material, which is available to authorized users.
Article 0 Reads 0 Citations Computational identification of co-evolving multi-gene modules in microbial biosynthetic gene clusters Francesco Del Carratore, Konrad Zych, Matthew Cummings, Erik... Published: 28 February 2019
Communications Biology, doi: 10.1038/s42003-019-0333-6
DOI See at publisher website PubMed View at PubMed ABS Show/hide abstract
The biosynthetic machinery responsible for the production of bacterial specialised metabolites is encoded by physically clustered group of genes called biosynthetic gene clusters (BGCs). The experimental characterisation of numerous BGCs has led to the elucidation of subclusters of genes within BGCs, jointly responsible for the same biosynthetic function in different genetic contexts. We developed an unsupervised statistical method able to successfully detect a large number of modules (putative functional subclusters) within an extensive set of predicted BGCs in a systematic and automated manner. Multiple already known subclusters were confirmed by our method, proving its efficiency and sensitivity. In addition, the resulting large collection of newly defined modules provides new insights into the prevalence and putative biosynthetic role of these modular genetic entities. The automated and unbiased identification of hundreds of co-evolving group of genes is an essential breakthrough for the discovery and biosynthetic engineering of high-value compounds.
Article 0 Reads 2 Citations The antiSMASH database version 2: a comprehensive resource on secondary metabolite biosynthetic gene clusters. Kai Blin, Victòria Pascal Andreu, Emmanuel L C De Los Santos... Published: 08 January 2019
Nucleic Acids Research, doi: 10.1093/nar/gky1060
DOI See at publisher website PubMed View at PubMed ABS Show/hide abstract
Natural products originating from microorganisms are frequently used in antimicrobial and anticancer drugs, pesticides, herbicides or fungicides. In the last years, the increasing availability of microbial genome data has made it possible to access the wealth of biosynthetic clusters responsible for the production of these compounds by genome mining. antiSMASH is one of the most popular tools in this field. The antiSMASH database provides pre-computed antiSMASH results for many publicly available microbial genomes and allows for advanced cross-genome searches. The current version 2 of the antiSMASH database contains annotations for 6200 full bacterial genomes and 18,576 bacterial draft genomes and is available at
Article 0 Reads 0 Citations Mining bacterial genomes to reveal secret synergy Mohammad Alanjary, Marnix H. Medema Published: 28 December 2018
Journal of Biological Chemistry, doi: 10.1074/jbc.H118.006669
DOI See at publisher website PubMed View at PubMed ABS Show/hide abstract
The medical treatment of infectious diseases often requires combination therapies that blend two molecules to enhance drug efficacy. Nature does the same. In a new article, Mrak et al. identify and functionally characterize natural products from Streptomyces rapamycinicus that show synergistic antifungal activity with the well-known immunosuppressant metabolite rapamycin, produced by the same strain. The genomic co-association of the two biosynthetic gene clusters paves the way toward new strategies to discover synergistic pairs of antibiotics through large-scale genome mining.
PREPRINT-CONTENT 0 Reads 0 Citations Comprehensive mass spectrometry-guided plant specialized metabolite phenotyping reveals metabolic diversity in the cosmo... Kyo Bin Kang, Madeleine Ernst, Justin J. J. Van Der Hooft, R... Published: 07 November 2018
bioRxiv, doi: 10.1101/463620
DOI See at publisher website ABS Show/hide abstract
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.
PREPRINT-CONTENT 1 Read 2 Citations A computational framework for systematic exploration of biosynthetic diversity from large-scale genomic data Jorge Navarro-Muñoz, Nelly Selem-Mojica, Michael Mullowney, ... Published: 17 October 2018
bioRxiv, doi: 10.1101/445270
DOI See at publisher website ABS Show/hide abstract
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.