Surveying fungal biodiversity: A genome mining approach

Background/Question/Methods

Fungal associations with plants have wide-ranging economic and ecological impacts in both agricultural and natural systems. A pathogenic or mutualistic fungus may impact a plant community in several ways, including via disease-mediated plant invasions and changes to the competitive fitness of a plant host population. The global fungal species count is estimated to be in the millions, with a broad majority not taxonomically described or the associated plant host(s) unknown. Linking fungal taxa with associated host plants and determining phylogenetic host range has widespread ecological utility, including plant pathogen detection and spread prevention. Genome sequencing of plants may capture associated fungi, which are typically viewed as undesirable genomic contaminants for plant-centered research. However, such “contaminant” data may be harnessed to identify and document previously unknown plant-fungal associations. Here, I present a pipeline that detects fungal sequences in non-targeted, low coverage, and short-read plant genomic DNA with high accuracy and taxonomic resolution. This pipeline can be employed for fungal biodiversity surveys and to determine plant-fungal associations.

Results/Conclusions

The completed bioinformatics pipeline was optimized to be rapid, computationally light, and sensitive to fungal detection, and serves as a nearly no-cost alternative to fungal surveying. Using the pipeline, I have identified hundreds of fungal taxa in publicly available plant genomic data. The pipeline has detected taxonomically described and undescribed fungi, and known and unknown associates of the respective plant. Further, I have detected core fungal microbiomes within intraspecific populations of plant hosts. This tool may be used to aid taxonomic descriptions of characterized and uncharacterized fungal species, enhance biodiversity databases with additional species occurrence data, bolster plant-fungal link predictions, and test ecological hypotheses in an understudied biological kingdom.