Project

Chemical warfare in fungi

Many Fusarium species are plant and animal pathogens that produce a myriad of secondary metabolites, i.e. products that are not essential for growth or survival of the producing species. Most of these presumably serve as toxins to weaken the host during infection and compete with or defend against other microbes in the soil. Genes that encode the proteins involved in the metabolic pathways that produce these toxins, are often clustered on the genome in so-called biosynthetic gene clusters (BGCs). These clusters may horizontally transfer between different species.

In bacteria, but particularly in Fungi, BGCs, representing an enormous diversity
in metabolites, are difficult to identify and are poorly characterized. Here we
will use a new tool, named CLOCI, to identify putative secondary metabolites
clusters in publicly available high-quality Fusarium genome assemblies. This
tool is function-agnostic: unlike e.g. a tool like antiSMASH, it does not start
from a known metabolic enzyme or known protein domain, but detects conserved
clusters based on phylogenetic information.

This tool can be applied directly to a database of fungal genomes created with MycoTools. Most analyses will be performed using Bash and Snakemake, with custom scripts in language of choice to compare tools and visualize these results. The tools we use to predict BGCs are written in Python.  

Research aims

  • Predict candidate BGCs in Fusarium species
  • Compare two tools for prediction of BGCs
  • Study presence and absence patterns of predicted BGCs in Fusarium species: common versus rare clusters, horizontal transfer of BGCs

Used techniques

  • Create a genome database of Fusarium sequences with MycoTools
  • Predict BGCs with CLOCI
  • Perform a comparable analysis using antiSMASH-tools (in this case: fungiSMASH + Big-SCAPE)
  • Compare results between the two tools: e.g. number of overlapping clusters, partially overlapping clusters, differences in predicted key enzymes, etc.
  • Plot presence and absence of different clusters on a Fusarium phylogeny
  • If there's time: study co-expression of predicted components of BGCs using public RNA-seq data

Supervisor