Background

Copy number variants (CNVs) are a class of structural variants (SVs) and are defined as fragments of DNA, typically larger than 1 Kilobase (Kb), that are present at variable copy number in comparison with a reference genome [1]. Mutation rates of CNVs are typically higher than those of single nucleotide polymorphisms (SNPs) [2, 3] and several mechanisms have been proposed to explain the formation of CNVs, including non-allelic homologous recombination, non-homologous end-joining, retrotransposition, and fork stalling and template switching [4–7]. Recent developments in sequencing technologies and bioinformatics tools have made it possible to more accurately identify and quantify CNVs, revealing their prevalence in the genome [8–12]. For instance, estimates suggest CNVs encompass up to 10 % of the human genome, account for more base pair differences between individuals than single nucleotide polymorphisms (SNPs), and overlap hundreds of genes [13].

Copy number variation is an important source of both genotypic and phenotypic variation, and commonly exerts its functional consequences through the modulation of gene expression [14]. Copy number variation can influence gene expression and gene function through a number of molecular mechanisms including gene dosage, regulatory element dosage, gene interruption, gene fusion, and position effect [15]. In humans, CNVs are associated with numerous genomic disorders including cancers, and autism-related disorders [16–19]. Conversely, CNVs can also lead to adaptive phenotypes such as the diet driven evolution of salivary amylase (AMY1) gene copy number (CN) in human populations, increased drug resistance of Plasmodium falciparum through elevated gch1 CN, and nematode resistance in soybean through increased CN of the multi-gene Rhg1 locus [20–22].

In recent years, a number of studies have utilized high-throughput sequencing technologies to characterize the prevalence and function of CNVs in vertebrate populations [11, 23–26]. However, few studies have examined population-level patterns of CNVs in fungi and oomycetes despite the observation that SVs can play a role in host specificity, fungicide resistance, and degree of pathogenicity [27–30]. For example, multiple copies and modulated expression of the effector gene Avr1a in the oomycete Phytophthora sojae increases virulence and enables evasion of soybean immunity [27]. CNVs have also been demonstrated as playing a role in Cryptococcus neoformans virulence. Chromosomal CN variation in C. neoformans can lead to reduced fungicide susceptibility by increasing copy number of ERG11, a target of fluconazole, and ARF1, an ABC transporter linked to azole susceptibility [29].

More than 1 million individuals die annually as a result of invasive mycoses and the vast majority of these life threatening infections occur in immuno-compromised individuals [31]. However, Cryptococcus gattii recently emerged as a primary human and animal pathogen, capable of infecting immuno-competent individuals [32]. A series of prominent and deadly C. gattii outbreaks occurred in Vancouver Island in 1999 and subsequently spread to mainland British Columbia, Oregon, and Washington [32, 33]. The epidemiology of these outbreaks was surprising considering C. gattii was believed to be endemic to subtropical and tropical regions [34]. The isolation of C. gattii from soil and trees revealed the global, but patchy, presence of C. gattii and the existence of four major phylogenetic lineages (denoted VGI, VGII, VGIII and VGIV) [33, 35, 36].

Within the Pacific Northwest (PNW), infections have been predominantly caused by isolates belonging to the VGIIa, VGIIb, and VGIIc subgroups [33]. Genomic and phylogenetic analyses suggest the PNW VGII subgroups likely originated from South America and Australia [33, 34, 37, 38]. The PNW VGII subgroups exhibit low levels of genetic variation indicative of a recent clonal expansion [30, 33, 37]. Despite the high degree of genetic similarity between PNW isolates, VGII subgroups display pathological diversity. For instance, VGIIa and VGIIc isolates exhibit heightened virulence and more commonly present as pulmonary infections, compared with VGIIb isolates which are generally less virulent and maintain the classical clinical pathology of neurological dominance [32, 33, 37]. Better understanding the genetic basis underlying VGII range expansion, niche adaptation, and pathological differences between subgroups could aid in the prevention, control, and treatment of future C. gattii outbreaks.

We hypothesize that CN variation could rapidly generate genotypic diversity in C. gattii subgroups and this variation might contribute to phenotypic and pathologic differences between VGII subgroups. To address this hypothesis, we identified 90 VGII isolates from more than 200 publically available C. gattii whole-genome Illumina sequencing datasets and generated high-resolution genome-wide CNV profiles for each isolate (Additional files 1 and 2) [30, 33, 35, 39]. Within predominantly PNW isolates (one isolate from Caribbean, and one isolate from Australia), we (i) broadly characterized genomic patterns of CNVs, (ii) identified divergent CNVs between the VGII subgroups, and (iii) examined the functional associations of genes overlapping with CNVs.

Methods

Data mining and sequence read processing

Whole-genome Illumina data for C. gattii isolates were retrieved from the NCBI Sequence Read Archive using the SRA Toolkit (Additional file 1) [30, 33, 35, 39]. For all samples, trim_galore (http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/) was used to remove residual adapter sequences and trim reads at bases with quality scores below 30. Trimmed reads shorter than 100 nucleotides (nt) were discarded. Quality and adapter trimmed read sets were then mapped against the C. gattii R265 reference genome (https://www.broadinstitute.org/annotation/genome/cryptococcus_neoformans_b/MultiHome.html) using the ‘sensitive’ preset parameters in Bowtie2 [40]. Mapping outputs were converted to sorted bam format using samtools ‘view’ and ‘sort’ parameters [41]. We first calculated average genome-wide coverage for each sample by dividing the sum of the per-base nucleotide depth values, using the samtools depth function, by the C. gattii R265 genome size [41]. Because differences in coverage between samples can bias CNV estimates, we used seqtk (https://github.com/lh3/seqtk) to randomly subsample reads such that each sample was normalized to 10× coverage. We chose a 10× coverage in order to retain the majority of publicly available data, and to ensure reliable CN estimation [42]. Samples with average pre-normalized coverage values less than 10× were removed from analysis, resulting in a total of 212 isolates. These 10× coverage subsampled datasets were mapped against the reference genome, and sorted bam files were generated as described above. The 10× coverage datasets were used for SNP calling and CNV estimation.

Inferring population structure

We performed population structure analysis to identify isolates belonging to the VGIIa, VGIIb, and VGIIc, subgroups. SNPs were conservatively called for each sample using VarScan.v2.3.9, requiring a minimum coverage of 8X and a SNP frequency of 100 % [43]. SNP sites were removed from a consensus whole population matrix when >10 % of the population harbored an ambiguously called base, or when minor allele frequency was <10 %. We subsampled the resulting matrix to ensure SNP sites were separated by >10 Kb (average distance markers ~13 Kb). The final matrix used for evolutionary analysis consisted of 1223 SNPs.

We employed several methods to infer the population structure of the 212 isolates. First, we used the Bayesian model-based software Structure 2.3.4, using the “admixture” ancestry model and the “allele frequencies are correlated among populations” frequency model [44]. We ran a burn-in period of 100,000 and a Markov Chain Monte Carlo (MCMC) of 200,000 generations for K = 1–13, where K represents the number of genetic clusters or populations. The optimal number of clusters was predicted by calculating the average log probability (LnP(D)) of each K value and by calculating the rate of change in the LnP(D) between successive Structure runs with different K values (ΔK) [44, 45]. Both statistics were calculated using Structure Harvester [46]. Because Structure assumes Hardy-Weinberg equilibrium and linkage equilibrium and natural populations often violate these assumptions, we also employed Discriminant Analysis of Principal Components (DAPC) in the adegenet v1.3-1 package. DAPC is a non-model-based multivariate approach, to infer population structure [47]. The number of distinct populations was predicted using the “find.clusters” k-means clustering algorithm and by calculating the Bayesian Information Criterion (BIC) value for each K = 1–16. Predicting the optimal population number is often unclear and complex in panmictic natural populations. An alternative approach is to define the number of populations that are useful in describing the genetic data [47]. Using this approach, BIC values smaller than 1000 represent useful summaries of population structure [47].

When assigning individuals to subpopulations, we assigned isolates to clusters when cluster assignment between Structure and DAPC were in agreement. Isolates with membership coefficients <90 % were removed. Clusters were assigned a VGIIa, VGIIb, or VGIIc classification according to predominant clustering of subgroups previously identified by other studies [30, 33, 35, 39]. This classification scheme resulted in 35 VGIIa, 22 VGIIb, and 33 VGIIc isolates.

Genome-wide copy number quantification

Whole supercontig CN variation can be detected through differences in the relative number of mapped reads. To estimate supercontig CN, a normalized mapped read value (NMRV) for each sample was determined by calculating the number of reads mapped per supercontig using the samtools ‘idxstats’ function [41]. The NMRV was calculated using only ten largest supercontigs which have an average length of 1,181,220 base pairs (bp). To estimate supercontig CN, the number of reads that mapped to each supercontig was calculated using the samtools ‘idxstats’ function and then divided by NMRV. Thus, relative read mapping serves as a proxy for CN events of larger sizes.

We used the Control-FREEC software for high-resolution CNV quantification [48]. Using the read depth approach, Control-FREEC additionally implements a sliding window and Least Absolute Shrinkage and Selection Operator (LASSO) approach for CNV detection [48–50]. Integer CN was estimated for each 100 bp non-overlapping window of the genome using the following parameters: window = 100, telocentromeric = 0, minExpectedGC = 0.33, and maxExpectedGC = 0.63. CN of high VST genes was calculated as the average copy number value of 100 bp windows that overlap with the gene of interest.

Identifying divergent CNVs between VGII subpopulations

The VST measurement, derived from FST, is used to identify loci that differentiate by CN between populations [9]. VST and FST consider how genetic variation is partitioned at the individual, population, and global levels and range from 0 (no population differentiation) to 1 (complete population differentiation). VST has proven useful in identifying CNVs under positive selection in genetically distinct populations [8, 25, 51]. We calculated VST between VGIIa, VGIIb, and VGIIc groups for each non-overlapping 100 bp bin in the genome using the following formula:

$$ {V}_{ST}=\frac{V_{total}-\raisebox{1ex}{$\left({\mathrm{V}}_{\mathrm{V}\mathrm{GIIa}}\times {\mathrm{N}}_{\mathrm{V}\mathrm{GIIa}}+{\mathrm{V}}_{\mathrm{V}\mathrm{GIIb}}\times {\mathrm{N}}_{\mathrm{V}\mathrm{GIIb}}+{\mathrm{V}}_{\mathrm{V}\mathrm{GIIc}}\times {\mathrm{N}}_{\mathrm{V}\mathrm{GIIc}}\right)$}\!\left/ \!\raisebox{-1ex}{${\mathrm{N}}_{\mathrm{total}}$}\right.}{V_{total}} $$

Vtotal is total variance, VVGIIx is the CN variance for each respective subpopulation, NVGIIx is the sample size for each respective subpopulation, and Ntotal is the total sample size. We considered VST values in the upper 99th percentile, corresponding to values greater than 0.3397, as significant.

Genomic statistics

To assess differences in gene gain or loss between subgroups, we used a non-parametric Fisher-Pitman permutation test implemented in the R package coin, using 1,000,000 permutations [52]. The R package boot was utilized for bootstrapping statistics to provide confidence intervals with 1,000,000 bootstrap replicates [53, 54]. If statistical significance was observed between subgroups, permutation-based pairwise T-tests (100,000 permutations) with Bonferroni-corrected p-values, as implemented in the R package RVAideMemoire, were used to assess statistical differences between subgroups [55].

Enrichment analysis

We used Gene Ontology (GO) annotation from the C. neoformans H99 genome to assign functional classification to C. gattii orthologs [56]. Orthologs were predicted using the reciprocal best-BLAST hit approach with an e-value cut-off of 1e−6 [57, 58]. PFam domains in high VST genes were predicted using interproscan-5.8-49.0 [59]. Enrichment analysis was conducted using default settings in GOEAST (GO terms) and dcGO (PFam domains) [60, 61]. Results from both web-based programs were visualized in REVIGO [62].

Results

Population structure of C. gattii isolates

We were primarily interested in identifying loci with distinct CN profiles between the VGII subpopulations. Thus, we first performed population structure analysis in order to identify VGIIa, VGIIb, and VGIIc isolates using Structure and DAPC [44, 47]. We generated a SNP matrix of 1223 informative loci for 212 individuals (see Methods), and determined that a K of eight was the optimal number of populations by the ΔK method (Fig. 1a) [45]. With the same SNP matrix, DAPC did not determine a single optimal K but rather an informative range of K from 4 to 16 (Fig. 1b) [47]. We chose a K value of 8 as it was the optimal value in Structure and fell within DAPC’s suggested range. Membership coefficients were independently calculated using Structure and DAPC (Fig. 1c and d, respectively) [44, 47]. When assessing membership coefficients across K 5–13, VGIIa, VGIIb and VGIIc individuals clustered together consistently (Fig. 1e). Using this approach, a total of 35, 22, and 33 VGIIa, VGIIb, and VGIIc isolates were identified, respectively (Fig. 1b, d, and e). Subsequent CN variation analysis was performed on this set of 90 VGII isolates.

Fig. 1
figure 1

Structure (a, b) and DAPC (c, d) analyses of 212 Cryptococcus gattii isolates identify the presence of identical VGIIa, VGIIb, and VGIIc genetic clusters. a In the Structure analysis, the ∆K statistic predicts that the optimal number of populations (K) is eight. b Structure-based individual assignment of the 212 C. gattii isolates into eight populations. The Y-axis represents individual membership coefficient, while the X-axis corresponds to the 212 individuals. Thirty-five VGIIa, 22 VGIIb, and 33 VGIIc isolates were identified. c DAPC analysis indicates that values of K between 4 and 16 represent an informative number of populations. K values below the Bayesian information criterion (BIC) cutoff value (dotted line) represent useful summaries of population structure. d When K = 8 individual population assignment into the VGIIa, VGIIb, and VGIIc populations was identical compared with Structure. e Stability and agreement of individual population assignment into the VGIIa, VGIIb, and VGIIc subpopulations for K between 5 and 9 between Structure and DAPC. VGIIa, VGIIb, and VGIIc individuals are colored coded as blue, orange, and grey, respectively, through different values of K

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Copy Number Variant (CNV) analysis

We generated high-resolution CNV profiles for the 90 VGIIa, VGIIb, and VGIIc isolates to better understand the contribution of CNVs to genome variation. On average, CNV regions encompassed 1.63 % (284,860 bp) of the VGIIa genomes, 2.32 % (404,935 bp) of the VGIIb genomes, and 1.96 % (341,803 bp) of the VGIIc genomes, in comparison to the reference R265 genome. The average number of CNV regions per sample for VGIIa, VGIIb, and VGIIc was 156 ± 20.97, 171 ± 20.34, and 194 ± 21.29, respectively. We performed several analyses to (i) identify large-scale chromosomal and segmental duplication and deletion events, (ii) broadly characterize genomic patterns of CNVs, and (iii) identify CNVs that are differentiated between VGII subgroups.

Aneuploidy has been previously linked to variance in fungal pathogenicity, thus, we estimated CN for each supercontig (Fig. 2) [29, 30, 63, 64]. The vast majority of supercontigs were present at a CN of ~1; however, some supercontigs were present at higher or lower CN between subgroups. We calculated VST between CN estimates of entire supercontigs to identify those that differed between VGII subgroups. The average VST value for supercontigs was 0.097 suggesting most supercontigs were equally represented between subgroups and that variation is similarly partitioned between VGII subpopulations rather than within individual subpopulations. However, supercontig 26 was found at greater CN in VGIIa (1.14 ± 0.111 CN) compared to VGIIb (0.75 ± 0.068 CN) and VGIIc (0.67 ± 0.066 CN) (VST = 0.861) (Fig. 2). Supercontig 26 is one of the smallest supercontigs (15,500 bp) in the R265 reference genome and contains only one short uncharacterized gene (CNBG_9678) with no annotated protein domains. We also identified three individuals with atypical supercontig CNs. Two VGIIb isolates had an estimated CN of 1.87 and 1.84 for supercontig 13, and another VGIIb isolate had an estimated copy number of 1.75 for supercontig 9. A large scale duplication on supercontig 13 has been previously reported in a clinically isolated VGII sample from France [37].

Fig. 2
figure 2

Supercontig copy number estimates between VGII subgroups. In the heatmap blue, black, and red represent supercontig copy numbers of 0, 1, and 2, respectively. Supercontigs and individuals and are represented on the X-axis and Y-axis, respectively. The VST value between VGIIa, VGIIb, and VGIIc subpopulations is reported for each supercontig (upper panel). Supercontig 26 present at a higher copy number in VGIIa individuals. Individual supercontig duplications can be seen for three isolates in VGIIb

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For high-resolution CNV prediction in each VGII isolate, we calculated integer CN for each 100 bp non-overlapping window in the genome using Control-FREEC [48]. To examine the potential functional effects of CNVs, we compared the frequency of genes for which CN variable regions partially or fully overlapped relative to the R265 reference genome (Fig. 3). VGIIa had the fewest partially deleted genes, VGIIb had the greatest partially deleted genes, and VGIIc had the fewest partially duplicated genes and the greatest partially deleted genes (Fig. 3a). The number of partially duplicated genes significantly differed between subgroups (Fisher-Pitman permutation test; p = 8.88e−3) and post-hoc pairwise comparisons revealed that VGIIc had fewer partially duplicated genes than VGIIa (p = 0.034) but not VGIIb (p >0.05) (Fig. 3c). Further, we found that the frequency of partially deleted genes was significantly different between VGII subgroups (Fisher-Pitman permutation test; p <2.2e−16) with post-hoc pairwise comparisons revealing that VGIIb had higher levels of partially deleted genes than VGIIa (p = 0.006) and VGIIb (p = 0.012) (Fig. 3d). We also found significant differences in the number of whole duplicated (p = 0.0051) and deleted (p <2e−6) genes between VGII subpopulations. VGIIb had significantly more duplicated genes than VGIIc (p = 0.039), while VGIIa had significantly fewer whole deleted genes compared with VGIIb (p <6e−5) and VGIIc (p <6e−5) (Fig. 3e and f). Reference genome bias might account for the lower frequency of CNVs in VGIIa since R265 is a member of the VGIIa subgroup.

Fig. 3
figure 3

Genes overlapping with copy number variants across VGIIa, VGIIb, and VGIIc isolates. Scatter plots represent the number of duplications (X-axis) and deletions (Y-axis) that partially (a) and (b) entirely overlap genes, with marginal density distribution plots for each axis. c–f 95 % confidence intervals and VGIIa, VGIIb, and VGIIc subpopulation means were calculated using 1,000,000 bootstrap replicates. VGIIc has fewer partially duplicated genes than VGIIa (p = 0.034) (c) and more partially deleted genes than VGIIa (p = 0.006) and VGIIb (p = 0.012) (e). VGIIc has fewer whole duplicated genes than VGIIb (p = 0.039) (d), and VGIIa has fewer whole deleted genes than VGIIb (p <6e−5) and VGIIc (p <6e−5) (f). Three individuals from VGIIb with high frequencies of duplication events were removed for scaling in (a) and (b) but were retained for statistical analysis (c–f)

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Identifying divergent CNVs between VGII subgroups

We hypothesized that CN profiles that segregated by VGII subgroups might contribute to the pathological differences between VGII subgroups. To identify these divergent CNV regions, we calculated VST for each 100 bp bin across the genome between VGIIa, VGIIb, and VGIIc subgroups [9]. Values near 0 represent no CN differentiation between subpopulations while values of 1 are indicative of fixed copy number differences between subpopulations. The average VST value for genomic bins where at least one isolate was copy number variable was 0.041, suggesting that the majority of CNV loci are not differentiated between VGII subpopulations. To identify highly differentiated CNVs, we examined loci in the upper 99 % percentile of VST values (VST ≥0.3397). Using this approach, we identified 67 loci harboring a total of 58 genes (Fig. 4a, Additional file 3). One of the highest VST regions (0.885) is present on supercontig 8, encompasses 10,900 bp, and contains four genes. Each gene in this locus is present near one copy in VGIIa, two copies in VGIIb, and zero copies in VGIIc. Two of the high VST loci are separated by 100 bp on supercontig 11 (Fig. 4b). The total length of these two neighboring high VST loci is 20.5 Kb and the region contains eight genes. All genes within this region are present as a single copy in VGIIa but absent in VGIIb and VGIIc. This region has been reported previously using a different computational approach and reinforces the efficacy of our methodology [33].

Fig. 4
figure 4

Loci with distinct subpopulation copy number profiles. a Manhattan plot of VST values (Y-axis) for each 100 bp bin (X-axis) in the Cryptococcus gattii genome. The horizontal red line represents the upper 99 % percentile cutoff. Points above this line are considered significantly differentiated CNVs. The five highest VST loci that contain genes are highlighted in yellow (supercontig 11 contains two of the five loci). b Gene copy number estimates for the five highest VST gene-containing loci. The gene ID and VST ID are reported for each locus. Blue, orange, and gray points represent VGIIa, VGIIb, and VGIIc isolates. Functional annotation (PFam domains or gene name) is provided below each plot

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Lastly, we compiled a list of candidate genes associated with Cryptococcus and fungal pathogenicity from the literature and examined their CNV profiles [65–89]. A comprehensive suite of genes empirically related to virulence in C. gattii or C. neoformans (53 total genes) and chitin synthase genes (10 total genes) revealed very few deletion or duplication events. However, several potential virulence associated genes were found in high VST regions and are discussed later. We did identify CNVs in several genes related to transport activity. The majority of high VST transporters show the same pattern in which VGIIa isolates retain a single copy, while VGIIb and VGIIc isolates have experienced deletions (CNBG_9639, CNBG_2482, CNBG_4491, CNBG_2944) (Additional file 3).

Functional analysis of CNV genes

We performed enrichment analysis of genes overlapping CNVs to broadly characterize their functional associations. First, we identified orthologs between C. neoformans and C. gattii using the Reciprocal Best BLAST Hit (RBBH) approach [57, 58]. C. neoformans GO annotation was assigned to C. gattii genes with one-to-one orthology [56]. We performed GO term enrichment analysis on all genes in the high VST CNV regions, on the genes overlapping CNV regions within each VGII subpopulation, and on the genes overlapping CNV regions across all VGII subpopulations. We independently analyzed genes that were (i) deleted partially, (ii) deleted entirely, (iii) duplicated partially, and (iv) duplicated entirely because each type of mutation cannot be categorized as “functional” or “nonfunctional” without experimental validation. For instance, partial gene deletions and duplications may result in loss of function, but may also be adaptive [90–92]. No GO terms were significantly enriched in the combined VGII subpopulations when entire deleted genes, partially deleted genes, or entire duplicated gene sets were analyzed. However, in the gene set composed of CNV regions that partially overlap genes across the combined VGII samples, we identified several enriched GO terms (for a full list of enriched GO terms see Additional file 4). The top three enriched GO terms in the biological processes category were transmembrane transport (p = 1.7e−4), methylation (p = 2.4e−3), transport (p = 5.4e−3), the top three enriched GO terms in the molecular function category were substrate-specific transmembrane transporter activity (p = 3.41e−5), transmembrane transporter activity (p = 3.98e−5), receptor activity (p = 1.15e−2), and the top three GO terms in the cellular component category were proton-transporting V-type ATPase, V1 domain (p = 1.69e−4), proton-transporting V-type ATPase complex (p = 1.4.3e−3), and proton-transporting two-sector ATPase complex, catalytic domain (p = 2.2e−3) (Additional file 4).

We also examined the enrichment of PFam domains using dcGO to provide an additional level of resolution for functional analysis of high VST genes [61, 93]. Within the biological processes category, we observed enrichment of protein domains associated with primary metabolism (Fig. 5a). In particular, several of these categories are associated with fundamental processes such as carbohydrate metabolic process (p = 1.99e−5), telomere maintenance (p = 2.04e−3), and mitochondrial proton-transporting ATP synthase, catalytic core (p = 4.83e−7). Additionally, we identified several terms that are associated with environmental sensing, including regulation of response to biotic stimulus (p = 4.86e−4) and response to xenobiotic stimulus (p = 5.59e−4). Analysis of terms related to the cellular component category revealed several terms associated with the cell surface, including external encapsulating structure (p = 5.82e−4), microvillus (p = 4.3e−4) and proton-transporting two-sector ATPase complex (p = 6.72e−4) (Fig. 5b, Additional file 5). We also identified an enrichment of transport-related domains including active transmembrane transporter activity (p = 8.3e−4), cation-transporting ATPase activity (p = 9.56e−5), and proton-transporting ATPase activity, rotational mechanism (p = 3.31e−6) (Fig. 5c, Additional file 5).

Fig. 5
figure 5

Functional enrichment of high VST loci. Gene Ontology (GO) enrichment based on PFam domains present in the genes overlapping high VST loci. Enrichment analysis was conducted via dcGO and is independently reported for Biological Processes (a), Cellular Component (b), and Molecular Function (c) [66]. Terms with p-values <0.01 are depicted. The shading of red represents p-value, with dark red corresponding to the lowest p-value. The area of each circle is relative to the number of terms falling into the respective GO category. Grey lines connect related GO terms and the thickness of the line indicated the degree of relatedness

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Discussion

We have conducted the first genome-wide population genomic analysis of C. gattii CN variation. Our high-resolution analysis revealed SVs ranging from 100 to 365,300 bp (Figs. 2, 3 and 4) encompassing ~1–2 % of the genome. The proportion of the genome affected by CNVs are in agreement to levels found in Homo sapiens (~5 %), Danio rerio (~4.5 %), Sus scrofa domesticus (1.5 %), Arabidopsis thaliana (0.75 %), and the yeasts S. cerevisiae (1.2 %) and S. paradoxus (3.5 %) [13, 26, 31, 94, 95].

We were primarily interested in identifying differentiated CNVs between the VGIIa, VGIIb, and VGIIc subpopulations given their close phylogenetic relationship but distinct pathological phenotypes. We calculated the CN specific population statistic VST to identify CN profiles that differed between VGII subpopulations (Fig. 4). Selection can cause patterns of CNV divergence between populations, however, other population genetic process, such as genetic drift and gene flow can also contribute to CNV profiles that are stratified by population. Nonetheless, the VST approach has offered insights into phenotypic differences between populations [31, 51, 96–98]. For instance, high VST loci in South African Nguni cattle revealed CN variable genes involved in parasite resistance, body size, and fertility that may account for phenotypic differences between breeds [98].

In our analysis, one of the highest VST values was obtained for a ~20 Kb region of supercontig 11 (VST-SC_1.11_1 and VST-SC_1.11_2) that contained 8 genes associated with sugar transport, alcohol dehydrogenase activity, and collagenase activity (Fig. 4b). In VGIIa the locus is present at a single copy, while deleted from the VGIIb and VGIIc isolates. This locus and pattern had been previously discovered through de novo genome assembly, in silico gene prediction, and BLAT score ratio analysis between VGIIa, VGIIb, and VGIIc isolates [33]. Another high scoring VST region included a four gene, 10.9 Kb region on supercontig 8 (VST-SC_1.26_1) with a CN of 1, 2, and 0 in the VGIIa, VGIIb, and VGIIc sub-populations, respectively (Fig. 5b). This locus included a putative transcription factor with a Zinc cluster domain, a glycosyl hydrolase family gene, and a deoxyribose-phosphate aldolase. In Staphylococcus pneumoniae a glycosyl hydrolase encoding gene (GHIP) is involved in host-cell invasion and knockout mutants exhibit reduced capacity to colonize mouse tissue [99]. In Toxoplasma gondii, a deoxyribose-phosphate aldolase plays an essential role in pathogenicity by mediating host-cell invasion, and providing energy during the invasion process [100].

Enrichment analysis of genes in high VST regions revealed cohesive networks of functional terms potentially linked to C. gattii pathogenicity, including many transport related GO terms (Fig. 5, Additional files 4 and 5). Transporter encoding genes are often associated with fungal virulence because of their ability to export fungicides from the cell [101]. Differences in fluconazole minimum inhibitory concentration within and between VGII subgroups have been observed, but have not been attributed to target gene (ERG11) expression or genetic variation [65, 67]. Rather, fluconazole resistance in C. neoformans and C. gattii appears to be driven by up-regulation of the ABC transporter encoding genes ARF1, ARF2, and MDR1 [65, 102, 103]. Genes encoding a diversity of other transporters also contribute to Cryptococcus pathogenicity through such mechanisms as micronutrient scavenging and sugar transport for capsule formation [104–106]. Several Major Facilitator Superfamily (MFS) domains were predicted in the high VST genes including CNBG_4491 (VGIIa: one copy, and VGIIb and VGIIc: zero copies), CNBG_9477 (VGIIa: one copy, and VGIIb and VGIIc: zero copies), and CNBG_9094 (VGIIa and VGIIb: two copies, and VGIIc: one copy).

The anti-phagocytic capsule is one of the defining virulence factors in C. neoformans and C. gattii and is primarily composed of a lattice of glucuronoxylomannan, glucuronoxylomannogalactan, and mannoproteins [34]. We observed the enrichment of several GO terms in the high VST genes that might be associated with the capsule, including polysaccharide catabolism and external encapsulating structure (Fig. 5). For instance, CNBG_1084 contains a fibronectin type III-like domain. This domain can interact with the ARF1 protein, which, along with its contribution to fluconazole resistance, is also involved in vesicle transport and influences capsule size [107, 108]. In VGIIc, the CNV found in CNBG_1084 results in a ~600 bp deletion in the 3′ end of the gene and overlaps with the fibronectin type III-like domain and the stop codon. We also identified a highly differentiated 100 bp CNV (VST = 0.94) located in the 3′ end of a histone deacetylase (HDAC) encoding gene (CNBG_5703) (Additional file 3). This CNV did not overlap with the HDAC domain but did overlap with the stop codon (VGIIa: one copy, and VGIIb and VGIIc: zero copies). Through phenotypic screening of >1000 targeted gene deletion mutants in C. neoformans, Liu et al. [109] demonstrated that HDAC encoding genes contribute to capsule formation and influence infectivity.

CNVs can affect phenotype through changes in gene dosage, which most commonly correspond to alterations in gene expression [20, 22, 110–112]. Using a high resolution in silico approach, we have identified 67 CN variable regions in the C. gattii genome that contain 58 genes and are highly differentiated by VGII subgroup (Fig. 4, Additional file 3). These loci may account for some of the observed pathological differences between VGII subgroups. Moving forward, it will be essential to experimentally evaluate the function roles of these genes and to assess the regulatory impact of CNVs. Recent developments in molecular tools, such as RNA interference and CRISPR-Cas9 genome editing, as well as a murine model of C. gattii meningoencephalitis, will aid in assessing the function of C. gattii CNV gene candidates [113, 114].

Conclusion

We identified 90 C. gattii isolates corresponding to the subpopulations responsible for the deadly outbreaks in the North American Pacific North West. Within these isolates, we bioinformatically predicted CNVs and uncovered loci that display subpopulation specific patterns of variation. Many of these CNV differentiated loci harbored genes that were enriched for metabolic, transport, and capsule composition associated functions. These genes represent novel candidates that may explain some of the pathological differences between VGII subgroups. Further functional investigation of candidate genes is needed to better understand the impact of copy number variation on C. gattii virulence.