FAIR data for a better understanding of fungal epidemiology

Genetics

Anna Fensel

AI & Data science

2024-01-15

Relevance of fungi

  • Fungi are a diverse taxa.

  • A lot good things

    • Fermentation.
    • Plant health.
    • Nutrient cycling.
  • A lot of chaos too

    • Food insecurity.
    • Toxicity of food.
    • Diseases in animals.

The WHO priority pathogens

The priority fungal pathogens identified by the WHO [1]

A closer look at A. fumigatus

  • Why? Because the WUR has expertise on A. fumigatus.
  • It is imporant for Dutch public health.
    Due to the large tulip sector here.

Conidia of A. fumigatus [2]

The knowledge gap

What we need to do

Being able to track the evolution of fungal pathogens.

How do we do it?

phylogeography.

Phylogeography using nextstrain

Using nextstrain for fungal pathogens [3]. An example:

Overview of the spread of west-nile in the USA from nextstrain.

Bridging the gap

This has not yet been done because \(\dots\)

  • Whole genome sequences (WGS) of fungi are upcoming: [46]

  • Existing solutions don’t consider WGS data.

  • Standardisation is not a focus in the A. fumigatus domain.

Method

  1. Harmonise existent metadata.

  2. Develop a system for harmonising new metadata.

  3. Analyse phylogeography of A. fumigatus.

  4. Generalise the workflow to other fungi.

Outlook

  • A new method of data collection that is called for by literature [7].

  • Existing solutions, like globalfungi only focus on the ITS sequence.

  • The solution could be generalised to plant epidemiology also.

References

More information

I thank Eveline Snelders and Christopher Wandabwa for reviewing my work and providing critical feedback. The idea of standardising WGS data specifically was raised by Eveline. Christopher’s critical comments were helpful for developing the presentation further and making the importance clearer. Murambiwa Nyati helped with the final check of the pre-proposal.

Cited works

1.
2.
3.
Hadfield J, Megill C, Bell SM, et al (2018) Nextstrain: Real-time tracking of pathogen evolution. Bioinformatics 34(23):4121–4123. https://doi.org/10.1093/bioinformatics/bty407
4.
Fan Y, Wang Y, Xu J (2020) Comparative Genome Sequence Analyses of Geographic Samples of Aspergillus fumigatus—Relevance for Amphotericin B Resistance. Microorganisms 8(11):1673. https://doi.org/10.3390/microorganisms8111673
5.
Lofgren LA, Ross BS, Cramer RA, Stajich JE (2022) The pan-genome of Aspergillus fumigatus provides a high-resolution view of its population structure revealing high levels of lineage-specific diversity driven by recombination. PLOS Biology 20(11):e3001890. https://doi.org/10.1371/journal.pbio.3001890
6.
Rhodes J, Abdolrasouli A, Dunne K, et al (2022) Population genomics confirms acquisition of drug-resistant Aspergillus fumigatus infection by humans from the environment. Nat Microbiol 7(5, 5):663–674. https://doi.org/10.1038/s41564-022-01091-2
7.
Steenwyk JL, Rokas A, Goldman GH (2023) Know the enemy and know yourself: Addressing cryptic fungal pathogens of humans and beyond. PLOS Pathogens 19(10):e1011704. https://doi.org/10.1371/journal.ppat.1011704