Clonality and α-a recombination in the Australian Cryptococcus gattii VGII population--an emerging outbreak in Australia

Abstract

Background: Cryptococcus gattii is a basidiomycetous yeast that causes life-threatening disease in humans and animals. Within C. gattii, four molecular types are recognized (VGI to VGIV). The Australian VGII population has been in the spotlight since 2005, when it was suggested as the possible origin for the ongoing outbreak at Vancouver Island (British Columbia, Canada), with same-sex mating being suggested as the driving force behind the emergence of this outbreak, and is nowadays hypothesized as a widespread phenomenon in C. gattii. However, an in-depth characterization of the Australian VGII population is still lacking. The present work aimed to define the genetic variability within the Australian VGII population and determine processes shaping its population structure.

Methodology/principal findings: A total of 54 clinical, veterinary and environmental VGII isolates from different parts of the Australian continent were studied. To place the Australian population in a global context, 17 isolates from North America, Europe, Asia and South America were included. Genetic variability was assessed using the newly adopted international consensus multi-locus sequence typing (MLST) scheme, including seven genetic loci: CAP59, GPD1, LAC1, PLB1, SOD1, URA5 and IGS1. Despite the overall clonality observed, the presence of MATa VGII isolates in Australia was demonstrated for the first time in association with recombination in MATα-MATa populations. Our results also support the hypothesis of a "smouldering" outbreak throughout the Australian continent, involving a limited number of VGII genotypes, which is possibly caused by a founder effect followed by a clonal expansion.

Conclusions/significance: The detection of sexual recombination in MATα-MATa population in Australia is in accordance with the natural life cycle of C. gattii involving opposite mating types and presents an alternative to the same-sex mating strategy suggested elsewhere. The potential for an Australian wide outbreak highlights the crucial issue to develop active surveillance procedures.

Figure 1. (A) Unrooted Neighbor-Joining consensus tree of the 54 Crytococcus gattii VGII Australian isolates and the two reference strains CDC R265 (VGIIa  =  ST20) and CDC R272 (VGIIb  =  ST7) based on the 7 concatenated MLST loci.

The two ST38 isolates are MATa, all others are MATα. For each sequence type the isolate proportions regarding their (B) geographical origin (QLD: Queensland, NSW: New South Wales, NT: Northern Territory and WA: Western Australia) and their (C) source of isolation (CLIN: clinical, VET: veterinary and ENV: environmental) are presented. Pie charts are proportional to the sampling size.

Figure 2. Relationships between the number of loci and the genotypic diversity in the Australian C. gattii VGII population.

Each data point corresponds to the mean genotypic diversity and its standard error from 1000 permutations.

Figure 3. Unrooted Neighbor-Joining tree of the sequence types delineated for C. gattii VGII in this study and originated from different parts of the world.

Bootstrap values over 50% are given at the nodes.

Figure 4. Spatial distribution of the different sequence types delineated in the Australian C. gattii VGII population.

Pie charts are proportional to the number of samples. The symbols n and nST corresponds to the number of samples and the number of sequence types observed, respectively.

Figure 5. Repartition of the sequence types according to their source of isolation.

Figure 6. Observed (white bars) and expected (solid line and triangles) mismatch distributions under the sudden expansion model for the seven loci used in this study.

The abscissa corresponds to the number of nucleotide differences between pairwise of sequences and the ordinate to the frequency. Pairwise nucleotide differences were realized on the global C. gattii VGII Australian population. Dashed lines represent the 90% confident interval of the expected mismatch distribution. Goodness-of-fit between the observed and expected mismatch distributions were tested using the sum of square deviation index (SSD) and loci for which a good match has been detected are highlighted by dashed squares.