- Satria A. Kautsar (1)
- Theo A.J. Lee van der (1)
- Lena Barra (1)
- Samuel C. Epstein (1)
- Hernando G. Suarez Duran (1)
- Marnix H. Medema (4)
- Koen Hoogendoorn (1)
- Louise K. Charkoudian (1)
- Jeroen S. Dickschat (1)
- Cees Waalwijk (1)
A standardized workflow for submitting data to the Minimum Information about a Biosynthetic Gene cluster (MIBiG) repository : Prospects for research-based educational experiences
Epstein, Samuel C. ; Charkoudian, Louise K. ; Medema, Marnix H. - \ 2018
Standards in Genomic Sciences 13 (2018)1. - ISSN 1944-3277
Biosynthetic gene cluster - Course-based undergraduate research experience - MIBiG - Natural product - Specialized metabolism
Microorganisms utilize complex enzymatic pathways to biosynthesize structurally complex and pharmacologically relevant molecules. These pathways are encoded by gene clusters and are found in a diverse set of organisms. The Minimum Information about a Biosynthetic Gene cluster repository facilitates standardized and centralized storage of experimental data on these gene clusters and their molecular products, by utilizing user-submitted data to translate scientific discoveries into a format that can be analyzed computationally. This accelerates the processes of connecting genes to chemical structures, understanding biosynthetic gene clusters in the context of environmental diversity, and performing computer-assisted design of synthetic gene clusters. Here, we present a Standard Operating Procedure, Excel templates, a tutorial video, and a collection of relevant review literature to support scientists in their efforts to submit data into MiBIG. Further, we provide tools to integrate gene cluster annotation projects into the classroom environment, including workflows and assessment materials.
Evolution and diversity of biosynthetic gene clusters in Fusarium
Hoogendoorn, Koen ; Barra, Lena ; Waalwijk, Cees ; Dickschat, Jeroen S. ; Lee, Theo A.J. van der; Medema, Marnix H. - \ 2018
Frontiers in Microbiology 9 (2018). - ISSN 1664-302X
Ancestral state reconstruction (ASR) - Biosynthetic gene cluster - Fusarium - Koraiol - Supernumary chromosome
Plant pathogenic fungi in the Fusarium genus cause severe damage to crops, resulting in great financial losses and health hazards. Specialized metabolites synthesized by these fungi are known to play key roles in the infection process, and to provide survival advantages inside and outside the host. However, systematic studies of the evolution of specialized metabolite-coding potential across Fusarium have been scarce. Here, we apply a combination of bioinformatic approaches to identify biosynthetic gene clusters (BGCs) across publicly available genomes from Fusarium, to group them into annotated families and to study gain/loss events of BGC families throughout the history of the genus. Comparison with MIBiG reference BGCs allowed assignment of 29 gene cluster families (GCFs) to pathways responsible for the production of known compounds, while for 57 GCFs, the molecular products remain unknown. Comparative analysis of BGC repertoires using ancestral state reconstruction raised several new hypotheses on how BGCs contribute to Fusarium pathogenicity or host specificity, sometimes surprisingly so: for example, a gene cluster for the biosynthesis of hexadehydro-astechrome was identified in the genome of the biocontrol strain Fusarium oxysporum Fo47, while being absent in that of the tomato pathogen F. oxysporum f.sp. lycopersici. Several BGCs were also identified on supernumerary chromosomes; heterologous expression of genes for three terpene synthases encoded on the Fusarium poae supernumerary chromosome and subsequent GC/MS analysis showed that these genes are functional and encode enzymes that each are able to synthesize koraiol; this observed functional redundancy supports the hypothesis that localization of copies of BGCs on supernumerary chromosomes provides freedom for evolutionary innovations to occur, while the original function remains conserved. Altogether, this systematic overview of biosynthetic diversity in Fusarium paves the way for targeted natural product discovery based on automated identification of species-specific pathways as well as for connecting species ecology to the taxonomic distributions of BGCs.
Genomic identification and analysis of specialized metabolite biosynthetic gene clusters in plants using plantiSMASH
Kautsar, Satria A. ; Suarez Duran, Hernando G. ; Medema, Marnix H. - \ 2018
In: Plant Chemical Genomics / Fauser, Friedrich, Jonikas, Martin, New York : Humana Press Inc. (Methods in Molecular Biology ) - ISBN 9781493978731 - p. 173 - 188.
Bioinformatics - Biosynthetic gene cluster - Biosynthetic pathway - Genomic - Plant - Secondary metabolite - Specialized metabolite
Plants produce a vast diversity of specialized metabolites, which play important roles in the interactions with their microbiome, as well as with animals and other plants. Many such molecules have valuable biological activities that render them (potentially) useful as medicines, flavors and fragrances, nutritional ingredients, or cosmetics. Recently, plant scientists have discovered that the genes for many biosynthetic pathways for the production of such specialized metabolites are physically clustered on the chromosome within biosynthetic gene clusters (BGCs). The Plant Secondary Metabolite Analysis Shell (plantiSMASH) allows for the automated identification of such plant BGCs, facilitates comparison of BGCs across genomes, and helps users to predict the functional interactions of pairs of genes within and between BGCs based on coexpression analysis. In this chapter, we provide a detailed protocol on how to install and run plantiSMASH, and how to interpret its results to draw biological conclusions that are supported by the data.
Computational genomics of specialized metabolism : From natural product discovery to microbiome ecology
Medema, Marnix H. - \ 2018
mSystems 3 (2018)2. - ISSN 2379-5077
Bioinformatics - Biosynthetic gene cluster - Microbiome - Natural products - Specialized metabolism
Microbial and plant specialized metabolites, also known as natural products, are key mediators of microbe-microbe and host-microbe interactions and constitute a rich resource for drug development. In the past decade, genome mining has emerged as a prominent strategy for natural product discovery. Initially, such mining was performed on the basis of individual microbial genome sequences. Now, these efforts are being scaled up to fully genome-sequenced strain collections, pan-genomes of bacterial genera, and large sets of metagenome-assembled genomes from microbial communities. The Medema research group aims to play a leading role in these developments by developing and applying computational approaches to identify, classify, and prioritize specialized metabolite biosynthetic gene clusters and pathways and to connect them to specific molecules and microbiome-associated phenotypes. Moreover, we are extending the scope of genome mining from microbes to plants, which will allow more comprehensive interpretation of the chemical language between hosts and microbes in a microbiome setting.