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Fungal Genomics and Antagonistic Community Interactions

Fungal infections kill nearly one million people worldwide each year, yet our ability to track and combat these pathogens lags far behind what we have achieved for bacteria and viruses. FuGACI (Fungal Genomics and Antagonistic Community Interactions) aims to change that.

This JPIAMR-funded initiative (2025-2028) brings together seven institutions across six countries to harness the power of genomics in the fight against one fungal genus known as Candida, which is commonly associated with hospital outbreaks. Led by Prof. Ed Feil at the University of Bath, we're answering critical questions:

1- How do Candida species spread and adapt within healthcare environments?

2- How prevalent is Candida across different environmental settings and human communities?

3- Can protective gut bacteria be harnessed to prevent Candida infections?

Explore our researchPublications
FuGACI initiative mark.

By combining cutting-edge genome sequencing with environmental surveillance and microbiome research, FuGACI is creating new pathways to prevent and treat drug-resistant Candida infections that threaten millions of people worldwide.

7
Institutions
6
Countries
650+
Genomes to be sequenced
2025-2028
Project Duration

About FuGACI

Challenges

Despite the growing threat posed by Candida infections, significant knowledge gaps limit our ability to effectively manage these pathogens. FuGACI addresses three critical challenges:

Challenge 01

Genomic epidemiology lags behind

Whole genome sequencing transformed bacterial pathogen tracking, but fungal genomics still lacks standardized tools and shared pipelines. Candida genomes are larger and shifts in ploidy complicate interpretation, slowing surveillance.

What we need
  • Standardized bioinformatics pipelines for quality control and assembly
  • Community-oriented platforms for rapid data analysis and visualization
  • Algorithms to infer transmission networks from fungal genomes
  • Methods to link resistance genotypes to phenotypes
Challenge 02

Unknown transmission dynamics

We still do not know if hospital outbreaks stem from repeated environmental introductions or from adapted, persistent strains. Without clarity, infection control strategies remain blunt.

What we need
  • Identify hospital-adapted endemic strains
  • Distinguish environmental introductions from hospital transmission
  • Map how Candida species spread between patients and surroundings
  • Predict which strains drive the greatest outbreak risk
Challenge 03

Limited understanding of microbiome interactions

A healthy microbiome guards against Candida infections, but antibiotic use and immune suppression disrupt that balance. We need to pinpoint which bacteria protect against which high-risk strains.

What we need
  • Systematic identification of protective bacterial species across body sites
  • Mechanistic understanding of strain-specific inhibitory pathways
  • Evidence on which bacteria protect against high-risk Candida strains
  • Data to support microbiome-based therapeutic interventions

Goals

FuGACI delivers a connected set of goals to prevent, track, and treat Candida infections through genomics and microbiome science.

Goal 1

Establish genomic infrastructure

Building the foundation

Create accessible genomic databases and analysis tools so the community can track Candida evolution, transmission, and resistance in real time.

Deliverables
  • Reference genome datasets for key Candida species on pathogen.watch
  • Standardized pipelines for genome assembly and quality control
  • Resistance profiling tools linking genotypes to phenotypes
  • Phylogenetic analysis revealing emergent virulent or resistant strains
Goal 2

Map hospital transmission dynamics

Tracking the spread

Determine how Candida species move within healthcare settings and identify hospital-adapted strains that drive outbreaks.

Deliverables
  • Transmission networks for hospitals in Italy, Netherlands, and Australia
  • Signals that distinguish endemic hospital strains from new introductions
  • Evidence-based infection control recommendations
  • Plate-sweep metagenomics methodology for co-colonization tracking
Goal 3

Monitor environmental prevalence

Beyond hospital walls

Characterize the distribution of Candida species in wastewater, waterways, and environmental reservoirs to understand community-level prevalence.

Deliverables
  • High-resolution markers for rapid detection of species, strains, and resistance genes
  • Wastewater surveillance data from UK and Netherlands communities
  • Environmental sampling from wetlands, waterways, and bird populations
  • Evidence of Candida flow between clinical, community, and environmental settings
Goal 4

Identify protective commensal bacteria

Novel therapeutics

Discover and characterize gut bacteria that inhibit high-risk Candida strains, paving the way for microbiome-based interventions.

Deliverables
  • Co-occurrence analysis highlighting bacteria negatively associated with Candida
  • Cultured, genome-sequenced inhibitory bacteria collection
  • Experimental characterization of inhibitory mechanisms
  • Candidate bacterial strains for therapeutic development

Approach

WP1

Genomic databases and infrastructure

Building the reference framework

What we'll do

Sequence and assemble 650+ clinical Candida genomes plus 50+ environmental isolates. Establish pathogen.watch collections and high-resolution qPCR markers for species, strains, and resistance alleles.

Why it matters

Reference datasets and shared tools are the foundation for downstream analyses and enable reproducible, community contributions with standardised quality controls.

Key activities

  • Compile existing and novel genomes from clinical collections
  • Sequence environmental samples from wetlands, waterways, and bird populations
  • Publish interactive phylogenies and collections on pathogen.watch
  • Design and validate genetic markers for rapid diagnostics
WP2

Hospital transmission dynamics

Tracking pathogen spread

What we'll do

Run prospective sampling at hospitals in Italy, Netherlands, and Australia. Combine single-colony genomes with plate-sweep metagenomics to infer transmission networks and hospital-adapted strains.

Why it matters

Early signals show many infections come from persistent hospital strains while others are environmental introductions. Knowing which is which directs infection control.

Key activities

  • Collect ICU patient and environment samples
  • Sequence 2,000+ plate sweeps and 300+ metagenomes
  • Infer transmission networks and co-colonisation patterns
  • Identify protective and risk-associated bacterial-fungal interactions
WP3

One Health environmental surveillance

Monitoring beyond clinical settings

What we'll do

Screen retrospective wastewater from UK sites and sample wetlands, waterways, and bird populations. Mine global metagenomic datasets to map Candida presence alongside antifungal residues.

Why it matters

Wastewater surveillance provides community-level early warning, and environmental reservoirs may seed hospital introductions, especially for species like C. auris.

Key activities

  • Validate species and resistance markers on known samples
  • Screen wastewater for Candida species and resistance genes
  • Culture positive environmental samples for sequencing
  • Mine 700+ public wastewater and avian microbiomes
WP4

Protective bacteria characterisation

Discovering novel therapeutics

What we'll do

Use co-occurrence signals to find bacteria that inhibit high-risk Candida strains. Isolate, sequence, and test candidates in high-throughput inhibition assays and mechanistic studies.

Why it matters

Microbiome-based interventions can restore colonisation resistance and offer alternatives to antifungals, targeting the exact strains driving hospital outbreaks.

Key activities

  • Isolate diverse gut bacteria including anaerobes
  • Screen supernatants and co-cultures for Candida inhibition
  • Characterise mechanisms (nutrient competition, metabolites, pH)
  • Select top candidates for deeper study

Integration across work packages

Integration across work packages

WP1 — Genomes & markers

WP1 feeds WP2 and WP3 with reference genomes and validated markers.

Research Team

Prof. Ed Feil
Prof. Ed Feil
University of Bath
Dr. Anita Schürch
Dr. Anita Schürch
UMC Utrecht
Dr. Jo Rhodes
Dr. Jo Rhodes
University of Birmingham
Prof. Jukka Corander
Prof. Jukka Corander
University of Oslo
Dr. Hilary Browne
Dr. Hilary Browne
University College Cork
Dr. Davide Sassera
Dr. Davide Sassera
University of Pavia
Dr. Gerry Tonkin-Hill
Dr. Gerry Tonkin-Hill
University of Melbourne

Funding & Collaborations

Funding Organisation

JPIAMR

Participating Institutions

University of BathUniversity of BirminghamUniversity College CorkUniversity of MelbourneUniversity of OsloUniversity of PaviaUtrecht University

Collaborating Centre

CWBE collaborating centre