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. Author manuscript; available in PMC: 2013 Jan 1.
Published in final edited form as: Mol Ecol. 2011 Nov 22;21(1):57–70. doi: 10.1111/j.1365-294X.2011.05364.x

Evidence for Genetic Differentiation and Variable Recombination Rates among Dutch Populations of the Opportunistic Human Pathogen Aspergillus fumigatus

Corné HW Klaassen *,1, John G Gibbons †,1, Natalie D Fedorova , Jacques F Meis *, Antonis Rokas †
PMCID: PMC3258581  NIHMSID: NIHMS333914  PMID: 22106836

Abstract

As the frequency of antifungal drug resistance continues to increase, understanding the genetic structure of fungal populations, where resistant isolates have emerged and spread, is of major importance. Aspergillus fumigatus is a ubiquitously distributed fungus and the primary causative agent of invasive aspergillosis (IA), a potentially lethal infection in immunocompromised individuals. In the last few years, an increasing number of A. fumigatus isolates has evolved resistance to triazoles, the primary drugs for treating IA infections. In most isolates, this multiple-triazole-resistance (MTR) phenotype is caused by mutations in the cyp51A gene, which encodes the protein targeted by the triazoles. We investigated the genetic differentiation and reproductive mode of A. fumigatus in the Netherlands, the country where the MTR phenotype likely originated, to determine their role in facilitating the emergence and distribution of resistance genotypes. Using 20 genome-wide neutral markers, we genotyped 255 Dutch isolates including 25 isolates with the MTR phenotype. In contrast to previous reports, our results show that Dutch A. fumigatus genotypes are genetically differentiated into five distinct populations. Four of the five populations show significant linkage disequilibrium, indicative of an asexual reproductive mode, whereas the fifth population is in linkage equilibrium, indicative of a sexual reproductive mode. Notably, the observed genetic differentiation among Dutch isolates does not correlate with geography, although all isolates with the MTR phenotype nest within a single, predominantly asexual, population. These results suggest that both reproductive mode and genetic differentiation contribute to the structure of Dutch A. fumigatus populations, and are likely shaping the evolutionary dynamics of drug resistance in this potentially deadly pathogen.

Introduction

Invasive fungal infections, which have become a major health issue over the past three decades, are difficult to diagnose and treat. Identifying the genetic structure (i.e., the genetic differentiation and mode of reproduction) of fungal pathogens is essential for the development of better therapeutics against fungal infections. The majority of medically important fungi are haploid organisms that reproduce mostly asexually while occasionally engaging in sexual or parasexual reproduction (Heitman 2006; Sun & Heitman 2011; Taylor et al. 1999b), resulting in populations that are predominantly non-recombining. This prevalence of “asexual” reproduction among human pathogenic fungi is surprising, not only because recombination can be very advantageous in stressful environments (Goddard et al. 2005; Zeyl et al. 2005), such as inside a human host, but also because several of these pathogens have closely related non-pathogenic sexual relatives (Butler 2010; Nielsen & Heitman 2007).

One of the most important fungal pathogens of humans is Aspergillus fumigatus, the leading cause of a rapidly progressing, and frequently deadly, systemic infection called invasive aspergillosis (IA) (Denning 1998). Although many fungal species, including several human pathogens, show genetic differentiation (Hittinger et al. 2010; Milgroom 1996; Taylor et al. 2006), several studies have reported that A. fumigatus lacks genetic differentiation (Debeaupuis et al. 1997; Pringle et al. 2005; Rydholm et al. 2006). For example, an early restriction fragment length polymorphism analysis of hundreds of clinical and environmental isolates (Debeaupuis et al. 1997) and a multilocus sequence-based phylogenetic analysis of clinical and environmental isolates from five continents (Rydholm et al. 2006) both found no evidence of genetic differentiation. This absence of genetic differentiation in A. fumigatus is consistent with the species’ cosmopolitan distribution, its high abundance, and ease of aerial dispersal. Alternatively, the observed absence of differentiation could be due to the use of markers that are less informative, or, to sampling design. For example, a different multilocus sequence-based phylogenetic analysis of isolates from around the world identified two globally distributed and genetically differentiated lineages within A. fumigatus (Pringle et al. 2005), arguing that additional studies are required prior to concluding that A. fumigatus is not genetically differentiated.

Early evolutionary analyses also suggested that A. fumigatus had lost the ability to reproduce sexually (Geiser et al. 1996). However, the presence of intact meiosis-related genes in the A. fumigatus genome (Galagan et al. 2005; Nierman et al. 2005; Rokas & Galagan 2008), the presence of mating type loci at near-equal frequencies (Paoletti et al. 2005), and the demonstration that certain isolates can undergo sexual reproduction in the laboratory (O’Gorman et al. 2009), suggest that natural A. fumigatus populations are likely to reproduce both asexually and sexually. This inference is consistent with the detection of historical recombination in several population genetic studies of A. fumigatus populations (Paoletti et al. 2005; Pringle et al. 2005; Varga & Toth 2003).

Understanding the genetic structure of A. fumigatus is important for human affairs because recent reports indicate an increase in the frequency of multi-triazole-resistant (MTR) A. fumigatus isolates worldwide. Triazole drugs are the primary and most effective therapy against IA infections (Herbrecht et al. 2002; Meis & Verweij 2001). In the majority of isolates, MTR resistance is due to two mutations in the cyp51A gene that encodes 14α-sterol demethylase, the triazole target (Mellado et al. 2007; Snelders et al. 2008; Verweij et al. 2007). The two mutations, known as the TR/L98H allele, involve (i) a duplication in the cyp51A promoter, and (ii) a non-synonymous point mutation in the cyp51A coding region that results in a L98H amino acid change in the protein product. A. fumigatus is not transmitted host-to-host, so the rapid spread of the TR/L98H allele raises the possibility that it might have originated outside the clinical environment (Snelders et al. 2008; Verweij et al. 2009). Notably, genetic analysis of a collection of MTR isolates shows that all isolates with the TR/L98H allele are confined within a single clade and are less variable than non-resistant isolates (Snelders et al. 2008), consistent with a single and recent origin.

The recent spread of the TR/L98H allele in A. fumigatus presents a unique opportunity to investigate the role of genetic differentiation and reproductive mode in shaping the evolution of drug resistance in this potentially deadly pathogen. Our hypothesis was that sexual reproduction is facilitating the emergence and spread of the TR/L98H allele across the entire A. fumigatus population. To test this hypothesis, we studied the genetic structure of A. fumigatus in the Netherlands, the country where MTR first emerged. Using 20 neutral markers dispersed across the A. fumigatus genome, we genotyped and analyzed 255 clinical and environmental Dutch isolates, including 25 MTR isolates with the TR/L98H allele. Contrary to our expectation, we found that Dutch A. fumigatus genotypes group into five distinct populations that differed in levels of genetic diversity and recombination patterns, which allowed us to infer that four of the five populations were predominantly reproducing asexually. Importantly, the analysis showed that all isolates with the TR/L98H allele nested within one of the four asexually reproducing populations. These results suggest that genetic differentiation and reproductive mode are influencing the dynamics of drug resistance patterns in natural A. fumigatus populations.

Materials and methods

Isolate Collection

We analysed 255 clinical and environmental isolates from the Netherlands (Fig. 1A). We included 201 isolates from a nation-wide survey conducted in 2005 (Klaassen et al. 2009; Klaassen et al. 2010), 12 isolates from the clinical samples collection of the Canisius Wilhelmina Hospital in the city of Nijmegen, and 42 isolates from samples of cultivated garden soils from the greater Nijmegen area from a survey conducted in 2008. In all isolates, we determined the presence/absence of the TR/L98H allele using a recently developed real-time PCR screening method (Klaassen et al. 2010). Sixteen of the 213 clinical isolates and nine of the 42 environmental isolates contained the TR/L98H allele.

Fig. 1.

Fig. 1

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(A) Sampling location of the 156 genotypes from the Netherlands and (B) the chromosomal location of the TR/L98H (MTR) locus and the 20 markers used in the present study. In panel A, city and number of isolates and genotypes collected per city, respectively (in parentheses), are as follows: A: Leeuwarden (8, 3), B: Groningen (1, 1), C: Alkmaar (10, 9), D: Haarlem (7, 5), E: Amsterdam (19, 10), F: Enschede (15, 9), G: Utrecht (21, 7), H: Arnhem (6, 3), I: Rotterdam (45, 29), J: Nijmegen (102, 68), K: Goes (7, 5), L: Veldhoven (9, 4), and M: Heerlen (5, 3). The chromosome length scale (in million base pairs or Mbp) is shown on top of panel B.

Molecular Typing

We genotyped all 255 isolates using 20 markers (9 microsatellite, 1 indel, and 10 sequence/PCR-typing markers), distributed across the eight A. fumigatus chromosomes (Fig. 1B, Table S1). Typing of the nine microsatellite markers, which are STRAf2A, -2B, -2C, -3A, -3B, -3C, -4A, -4B and -4C, was performed as described previously (de Valk et al. 2005). We also scored the presence/absence of a one base-pair deletion in the flanking region of microsatellite marker 4A as a separate indel marker; this is the n.3 marker. We sequenced the BGT1 and ANXC4 loci and identified marker alleles using the multilocus sequence-typing scheme described by Bain and co-workers (Bain et al. 2007). We sequence-typed the CSP (cell surface protein) locus as previously described (Balajee et al. 2007; Klaassen et al. 2009). We PCR-typed the MAT (mating type) locus using the “one common and one specific primer” strategy, which yields amplicons of different length for each of the two MAT alleles (Paoletti et al. 2005). Using a similar approach, we PCR-typed the alleles of six putative HET (for heterokaryon or vegetative incompatibility) loci, which are thought to regulate self/non-self-recognition during filamentous growth (Fedorova et al. 2009; Fedorova et al. 2008). For the MAT and HET loci, which together will be further referred to as recombination markers RM1 – RM7 (Fig. 1B, Table S1), the A-allele corresponds to the allele present in strain Af293 (Nierman et al. 2005), the B-allele corresponds to the allele present in strain A1163 (Fedorova et al. 2008), whereas the C-allele corresponds to a recently identified third allelic variant (C.H.W. Klaassen and J.F. Meis, unpublished observations). We scored alleles that failed to yield a PCR product as nulls and confirmed negative results using a different set of PCR amplification primers.

Neutral Evolution Analysis

Markers undergoing positive selection are poorly suited for the study of population structure (Avise 2000). Therefore, we tested whether the coding sequences containing our markers are likely undergoing positive selection by estimating the ω ratio of the non-synonymous substitution rate (dN) to the synonymous substitution rate (dS) for each gene using the CODEML module from the PAML software, version 4.4 (Yang 2007). We first identified and aligned orthologs between the transcriptomes of A. fumigatus, Neosartorya fischeri, and A. clavatus as described previously (Fedorova et al. 2008; Rokas 2009; Rokas et al. 2007). To test for positive selection in each coding gene in our marker set, we rst evaluated the log likelihood of the null M7 model. Under M7, ω values at different codon positions in a gene follow a beta distribution, where ω is constrained to fall between zero and one. We then measured the difference (ΔL) between the log likelihood of the M7 model and that of the alternative M8 model, which, in addition to the zero to one beta distribution for ω values, also allows for a subset of codon sites to have ω values above one (Scannell et al. 2011; Yang 2006). We excluded all genes showing rates of synonymous substitutions larger than 2, because in these cases substitution saturation is likely to reduce the power and reliability of the performed comparisons. Finally, for the BGT1 and ANXC4 markers, for which typing was done by sequencing, we also evaluated whether the A. fumigatus population departed from neutrality by calculating Tajima’s D (Tajima 1989), as implemented in the DNASP software, version 5.10.01 (Librado & Rozas 2009). All tests were performed at p = 0.01 significance.

Clonal Correction

Inclusion of clonally related genotypes can blur analyses of genetic differentiation, haploid diversity and linkage disequilibrium, because it violates the assumptions of the evolutionary models used in these analyses (Jombart et al. 2010; Pritchard et al. 2000). Because microsatellite markers have very high mutation rates (Lynch et al. 2008), when a large number of microsatellite markers is used, clonally related genotypes might not have identical alleles at all microsatellite loci. Furthermore, for organisms, such as A. fumigatus, that are capable of reproducing both sexually and asexually, it is difficult to determine a priori what the best clonal correction threshold might be. To avoid these problems, we generated a series of clonally corrected data sets by eliminating the genotypes of all but one (randomly chosen) isolates with identical alleles for the n.3, ANXC4, BGT1, RM1–7, and CSP markers, and with 0 – 9 identical microsatellite markers.

Genetic Differentiation Analysis

We examined the genetic differentiation of Dutch A. fumigatus isolates using both model-based and non-model based approaches for the microsatellite (9 markers), non-microsatellite (11 markers) and full (20 markers) data sets, as well as for the series of clonally corrected full (20 marker) data sets.

We examined genetic differentiation using the Bayesian model-based approach implemented in the software STRUCTURE, version 2.3.3 (Pritchard et al. 2000). We used “admixture” and “allele frequencies are correlated among populations” as our ancestry and frequency models, respectively. We ran 100 replicates of 200,000 Markov Chain Monte Carlo (MCMC) generations for K = 1–10, where K = number of populations. In each run, we discarded the first 100,000 generations as burn-in. To identify the optimal K value, we used two different approaches. The first approach makes use of calculating the average log probability (LnP(D)) of each K value (Pritchard et al. 2000). Under this approach, the optimal K value is the one showing the highest LnP(D) score. The second approach is based on the ad hoc statistic ΔK, which calculates the rate of change in the log probability of data between successive runs with different K values (Evanno et al. 2005). Under this approach, the optimal K value is the one that maximizes ΔK. To evaluate the robustness of population assignment across the 100 STRUCTURE replicates we compared population assignments for each replicate to the replicate used in this study and calculated average individual membership coefficients using the CLUMPP software, version 1.1.2 (Jakobsson & Rosenberg 2007).

Because natural populations often violate Hardy-Weinberg equilibrium and linkage equilibrium assumptions, inferences drawn solely from model-based methods can be problematic. Therefore, we also analyzed our data set using the non-model-based multivariate approach DAPC, as implemented in the ADEGENET software, version 1.3-0 (Jombart 2008; Jombart et al. 2010). We predicted the optimal number of clusters (populations) using the k-means clustering algorithm, “find.clusters”, retaining all principal components. We calculated the Bayesian Information Criterion (BIC) for K = 1–10, where K = number of populations. The optimal number of populations was identified as the one for which BIC showed the lowest value and after which BIC increased or decreased by the least amount. We then used DAPC to assign individuals into populations, retaining the number of principal components encompassing 80% of the cumulative variance.

Genetic Differentiation by Geography Analysis

We performed two analyses to test the hypothesis that genotype geography was associated with genetic differentiation. In the first analysis, we grouped genotypes into populations based on their city of origin and based on their STRUCTURE population assignment. We then estimated global and pairwise population differentiation (ϕPT) values, a suitable measure of population differentiation analogous to FST for haploids, and performed AMOVA analysis in each data set using the GENALEX software, version 6.41 (Peakall & Smouse 2006). If genotype geography is significantly associated with genetic differentiation, we expect to see greater among–population variation, smaller within–population variation, and significant population differentiation when genotypes are grouped by city of origin compared to when genotypes are grouped to the populations they are assigned to by the STRUCTURE software.

In the second analysis, we performed a χ2 goodness-of-fit test between the observed number of genotypes from each location and the expected number of genotypes from each location due to chance. For each population, we calculated the expected number of genotypes from each location using the equation NLOC/(NTOT ⊗ NPOP), where NLOC is the number of genotypes from location X, NPOP is the number of genotypes from population X, and NTOT is the total number of genotypes.

Haploid Diversity Analysis

We calculated Nei’s unbiased haploid diversity (uh), a measure that calculates haploid diversity corrected for sample size, independently for the total marker set as well as the microsatellite marker set, for each one of the populations delineated using the STRUCTURE software, using the GENALEX software, version 6.4.1 (Peakall & Smouse 2006). For comparison, we also calculated haploid diversity from two populations of A. nidulans using a set of seven microsatellite markers (Hosid et al. 2008), as well as from a population of A. flavus and a population of A. parasiticus using a different set of seven microsatellite markers (Tran-Dinh & Carter 2000). Note that the microsatellite markers used in these studies are different from the markers used in this study.

Linkage Disequilibrium Analysis

To calculate the degree of association between alleles in our set of 20 markers and to examine whether patterns of recombination are similar across the five populations delineated using the STRUCTURE software, we calculated linkage disequilibrium (LD) using the MULTILOCUS software, version 1.3b (Agapow & Burt 2001). Specifically, we evaluated the index of association (Ia) (Maynard Smith et al. 1993), which calculates the distance between all possible locus pairs and compares the variance of distances against results expected if there is no association between loci. Ia was calculated both globally for each population as well as between all locus pairs within each population. We assessed statistical significance by comparing the observed Ia value against the Ia values obtained from 1,000 randomized multilocus genotype data matrices, using p = 0.05 as the significance value cutoff. The randomization step shuffles the alleles among isolates independently for each locus and assumes that the population is in linkage equilibrium. Because non-recombining populations are expected to contain higher numbers of clonally related genotypes, we also calculated the number of identical genotypes in a population, or clonal richness, for each population as another indicator of reproductive mode.

Estimation of Divergence Times of A. fumigatus Populations

We estimated pairwise divergence times between A. fumigatus populations by adapting the methods developed by Zhivotovsky (2001), which have been previously applied in A. flavus (Grubisha & Cotty 2010), for our set of microsatellite markers. We excluded markers 3A and 3C from this analysis because they exhibited unusually high levels of variation that may deviate from the generalized stepwise mutation model. We calculated divergence time, in generation units, using the equation TD = (D1/2w) − (V0/w). D1 is the average over loci of the average squared difference of repeat unit copy number between pairs of alleles sampled (one each from the populations) over 5 replicates (Goldstein et al. 1995), w is the effective mutation rate (Thuillet et al. 2005), and V0 is the average over all loci of the within-population variance in the repeat unit in the ancestral population. We estimated early and late boundaries of divergence by setting V0 = 0 and V0 = variance in the extant populations, respectively (Munkacsi et al. 2008; Zhivotovsky 2001). w was estimated by averaging the average mutation rate for each locus where mutation rate (μ) = 0.00003R − 0.0001, where R is the repeat unit copy number (Thuillet et al. 2005).

Results

A. fumigatus Markers are Evolving Neutrally

Six of our 20 markers reside in non-coding regions and are unlikely to be under selective pressure (Table S1). For the remaining 14 markers that reside within coding genes, we were able to reliably identify orthologs for 10 genes and reliably estimate ω values for 7 of them. The remaining three genes had dS values larger than 2, making neutrality testing unreliable. The genes evaluated were associated with markers 2A, 2B, 3A, ANXC4, BGT1, CSP, and RM7. None of the 7 genes could reject the null M7 model in favour of the M8 model (2A: ΔL = 0; 2B: ΔL = 0.098; 3A: ΔL = 0; ANXC4: ΔL = 0; BGT1: ΔL = 0; CSP: ΔL = 3.512; and RM7: ΔL = 0; p values for all tests are > 0.01), suggesting that positive selection is unlikely to be acting on them. Consistent with these results, examination of Tajima’s D statistic for the ANXC4 and BGT1 loci within A. fumigatus showed no evidence of positive or balancing selection (ANXC4: D = −0.194; and BGT1: D = −0.522; p values for all tests are > 0.01).

A. fumigatus Genotypes and Clonal Correction

Prior to any clonal correction, the entire collection of 255 isolates yielded 225 different genotypes. Microsatellite markers made the biggest contribution to the observed genotypic diversity. Specifically, 224/225 genotypes were recognized by the microsatellite markers alone, 106/225 by the RM markers, 20/225 by the CSP marker, and 10/225 by the ANXC4 and BGT markers combined. In both ANXC4 and BGT1, we identified two new alleles. Since up to now in both genes only four different alleles were recognized, we have provisionally numbered the new alleles as the fifth and sixth allele of each marker.

To remove any additional clonally related genotypes, we generated series of clonally corrected data sets by eliminating the genotypes of all but one (randomly chosen) isolates with identical alleles for the n.3, ANXC4, BGT1, RM1–7, and CSP markers, as well as for 9 – 0 microsatellite markers (irrespectively of which microsatellite marker(s) were involved). This filter resulted in the elimination of 29 – 106 genotypes (Fig. S1), with the remaining number of non-clonal genotypes plateauing to ~150 genotypes when clonal correction of 5 – 0 identical (or 4 – 9 different) microsatellites was applied. Given these results, we decided to use the “5 identical microsatellites” clonal correction threshold, because it coincides with the reaching of the clonal correction plateau. Using this threshold, we removed 99 genotypes (94 with triazole susceptible alleles and 5 with the MTR allele), resulting in a final data set of 156 non-clonally related genotypes, including 20 with the MTR allele. Unless otherwise indicated, all subsequent analyses were performed on this data set.

A. fumigatus Genotypes Belong to Five Distinct Populations

We inferred genetic differentiation for the 156 non-clonally related genotypes using the STRUCTURE model-based approach (Pritchard et al. 2000) and the DAPC non model-based approach (Jombart et al. 2010), independently for the microsatellite, the non-microsatellite, and the full marker data sets. Although analyses with both approaches on the microsatellite and non-microsatellite data sets give different answers as to the optimal number of populations, DAPC analysis of the microsatellite data set and STRUCTURE analysis of the non-microsatellite data set both estimate that the optimal number of populations is five (Fig. S2). Analyses of the full marker data set with both the STRUCTURE and the DAPC approach support this inference. Specifically, both the LnP(D) (−4035.01) and ΔK (54.91) approaches in STRUCTURE analysis indicate that K = 5 (Fig. 2A), and so does the BIC in the DAPC analysis, which reaches its minimum value (393.37) for K = 5 as well as displays its smallest increase from K = 5 (393.37) to K = 6 (393.66) (Fig. 2C).

Fig. 2.

Fig. 2

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Both STRUCTURE (panels A and B) and DAPC (panels C and D) analyses of 156 non-clonally related clinical and environmental genotypes identify the existence of five A. fumigatus populations in the Netherlands. (A) STRUCTURE analysis estimates that the optimal predicted number of populations K for our set of genotypes is five. This inference is supported by both the average log probability (LnP(D)) of each K value (black line) and by the ad hoc statistic ΔK (grey line). (B) The STRUCTURE based assignment of 156 genotypes into the five A. fumigatus populations. Each column on the X-axis corresponds to a different genotype. The Y-axis represents an individual’s membership coefficient to each population. White stars indicate multi-triazole resistant (MTR) individuals. STRUCTURE populations 1 – 5 are indicated by red, green, blue, yellow and pink color, respectively. (C) DAPC analysis estimates that the optimal predicted number of populations K for our set of genotypes is five. The Y-axis corresponds to the Bayesian Information Criterion (BIC), a goodness of fit measurement calculated for each K. The lowest BIC value (K = 5) indicates the optimal number of populations. (D) DAPC clustering of the five populations using the first two principal components (Y-axis and X-axis, respectively). The first four eigenvalue components are show in the lower left panel. DAPC populations 1 – 5 are indicated by red, green, blue, yellow and pink color, respectively and are highly similar to STRUCTURE delineated populations.

We also examined whether our genetic differentiation structure inferences differed as we imposed different clonal correction thresholds on our marker data set (Fig. S3). In the 225 genotype data set (generated by elimination of all but one genotype with identical alleles in all 9 microsatellite markers) the optimal number of populations predicted using the LnP(D) approach was 2. In contrast, the optimal number of populations predicted for all other clonally-corrected data sets was either 4 (for the genotype data sets generated by elimination of all but one genotype with identical alleles in 8 and 7 microsatellite markers, respectively), or 5 (for the genotype data sets generated by elimination of all but one genotype with identical alleles in 6 or fewer microsatellite markers). The very similar numbers of populations inferred by requiring elimination of all but one genotype with identical alleles in 8 or fewer microsatellite markers justifies our use of the “5 identical microsatellites” clonal correction threshold. Unless otherwise indicated, all subsequent analyses use the 156 non-clonally related genotype data set generated by the “5 identical microsatellites” clonal correction threshold, and its 5 inferred populations (as assigned by STRUCTURE).

Seventeen of the 20 markers used in this study contribute significantly to the observed genetic differentiation (with the CSP marker showing the strongest association with the different populations; Cramer’s V statistic = 82.5%), whereas the remaining three markers (RM-1, RM-3 and RM-7) show a random distribution over the five populations (Figs. S4 – S6).

Our results also suggest that the ancestry of several genotypes in the five populations traces to more than one population. For example, the results from the STRUCTURE approach indicate that several genotypes from population 1 share contributions from population 4 and vice versa (Fig. 2B and Table S2), indicating either that these two populations share a more recent common ancestor and/or provide evidence of recent admixture between them. Furthermore, the results from the DAPC approach point to a clear separation of population 2, and to a lesser extent of population 3, from all other populations; in contrast, population 1 genotypes show some overlap with genotypes from population 4 and population 5 genotypes (Fig. 2D). Interestingly, we found that all 20 genotypes containing the MTR allele nested within population 3 (Fig. 2B, Table S2), a result that was supported by both approaches.

Finally, we note that the assignment of individual genotypes into the five populations is highly concordant across STRUCTURE replicates as well as between the DAPC and STRUCTURE analyses. Using CLUMPP (Jakobsson & Rosenberg 2007), we calculated average individual membership coefficients for the 100 STRUCTURE replicates and found that population assignments are identical across runs and membership coefficients nearly identical to the replicate used in this study (Fig. S7; Table S3). Between the DAPC and STRUCTURE analyses, only 8/156 genotypes, all of which have considerable STRUCTURE membership coefficients to more than one population, are assigned to different populations when analyzed using the two different approaches (Table S2). For all subsequent analyses, we used the assignment of individual genotypes into the five populations from the STRUCTURE approach.

A. fumigatus Genotype Geography is not Associated with Genetic Differentiation

Two different analyses failed to provide any evidence in support of the hypothesis that the geographical origin of genotypes was associated with genetic differentiation (as inferred by STRUCTURE). Specifically, we do not identify any global population differentiation when genotypes are grouped into populations by their city of origin (ϕPT = 0.004, p = 0.39). Similarly, we find only 4/66 cases of significant population differentiation when we calculate ϕPT values pairwise between cities (these were: Alkmaar vs. Haarlem: ϕPT = 0.056, p = 0.036; Alkmaar vs. Rotterdam: ϕPT = 0.030, p = 0.038; Arnhem vs. Goes: ϕPT = 0.153, p = 0.020 and Arnhem vs. Veldhoven: ϕPT = 0.126, p = 0.026) (Fig. S3). Conversely, when genotypes are grouped by STRUCTURE, we find significant global (ϕPT = 0.223, p = 0.001) and pairwise differentiation for each population (p = 0.001 for all comparisons) (Fig. S8). Finally, the AMOVA analysis shows that almost none of the genetic variation among populations is explained when genotypes are grouped by city of origin; in contrast, 23% of the genetic variation among populations is explained when genotypes are grouped by the STRUCTURE-assigned populations (Fig. S8).

Using a second analysis, we also tested whether genotypes from particular cities are represented disproportionately in specific populations. For each population, our statistical analyses reject an association between geography and population assignment (ppopulation1 = 0.96, ppopulation2 = 0.83, ppopulation3 = 0.78, ppopulation4 = 0.45 and ppopulation5 = 0.90).

Haploid Diversity in the Five A. fumigatus Populations

Levels of haploid diversity are variable across the five A. fumigatus populations. Populations 1, 3, 4, and 5 show relatively high levels of haploid diversity (uhpopulation1 = 0.599, uhpopulation3 = 0.540, uhpopulation4 = 0.586 and uhpopulation5 = 0.531), whereas population 2 exhibits relatively lower levels of diversity (uhpopulation2 = 0.388).

To make estimates of haploid diversity comparable to previously published microsatellite analyses from other Aspergillus species (Hosid et al. 2008; Tran-Dinh & Carter 2000), we also calculated uh using only our microsatellite markers (Fig. 3). Again, we found that populations 1, 3, 4, and 5 show higher relative levels of haploid diversity (uhpopulation1 = 0.788, uhpopulation3 = 0.702, uhpopulation4 = 0.787 and uhpopulation5 = 0.711), compared to population 2 (uhpopulation2 = 0.408). Interestingly, uh values obtained for populations 1, 3, 4 and 5 are comparable to those found in two populations of A. nidulans (uhpopulation1 = 0.675 and uhpopulation2 = 0.598) (Hosid et al. 2008), a population of A. flavus (uh = 0.733) (Tran-Dinh & Carter 2000) and, to a lesser extent, a population of A. parasiticus (uh = 0.505) (Tran-Dinh & Carter 2000), even though different sets of markers were used in different studies. We observed few fixed loci in all populations (population 1 = 0, population 3 = 1, population 4 = 0 and population 5 = 0) with the exception of population 2, which contained seven fixed loci.

Fig. 3.

Fig. 3

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Unbiased haploid diversity (uh) measures of the five A. fumigatus populations and other representative Aspergillus species. Microsatellite-based uh values from populations of other representative Aspergillus species are from the following studies: A. flavus (Tran-Dinh & Carter 2000), A. parasiticus (Tran-Dinh & Carter 2000), and two A. nidulans populations (Hosid et al. 2008).

Recombination Levels Vary between the Five A. fumigatus Populations

Examination of LD suggests that A. fumigatus populations show varying levels of recombination (Fig. 4). Globally, populations 2 – 5 showed significant Ia values indicative of population-level LD (population 2: Ia = 0.588, p = 0.003; population 3: Ia = 0.429, p < 0.001; population 4: Ia = 0.374, p < 0.001; population 5: Ia = 0.471, p < 0.001), whereas values for population 1 (Ia = 0.106, p = 0.114) are indicative of a recombining population. Furthermore, of the 190 possible locus pairs tested, 12, 135, 47, 21 and 13 are fixed or in LD in populations 1–5, respectively (Fig. 3). Of the 76 observed locus pairs that were in LD, only 16 pairs involved markers located on the same chromosome, of which only nine involved neighboring markers not separated by another marker.

Fig. 4.

Fig. 4

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Linkage disequilibrium (LD) patterns of the five A. fumigatus populations. The LD patterns of the five A. fumigatus populations. LD was determined by calculating the Index of Association (Ia) for all locus pairs independently for all populations. White, grey and black boxes represent loci in equilibrium, loci in significant LD, and fixed loci, respectively.

Non-recombining populations are expected to contain higher numbers of clonally related genotypes. Thus, clonal richness, i.e., the number of identical genotypes in a population, might be used as another indicator of reproductive mode. Levels of clonal richness across the five A. fumigatus populations were 50% (29 of 58 isolates belonging to population 1 were inferred to have identical genotypes) for population 1, 35% (6 of 17) for population 2, 30% (22 of 73) for population 3, 45% (30 of 66) for population 4, and 29% (12 of 41) for population 5.

Divergence Times between A. fumigatus Populations

We estimated the average mutation rate, w, at 2.97 × 10−4, a value comparable to that obtained for A. flavus (Grubisha & Cotty 2010). The pairwise upper and lower divergence time estimates between population 3, which contains the MTR alleles, and all others were (in 1,000 generation multiples): Population 3 vs. 1: 102 – 19; vs. 2: 80 – 23; vs. 4: 111 – 33; and vs. 5: 109 – 51 (Table 1). We also estimated of upper and lower boundaries of divergence times (again in 1,000 generation multiples) between the MTR allele containing genotypes within population 3 and populations 1 (113 – 20), 2 (49 – 6), 4 (94 – 16), and 5 (58 – 7) (Table 1).

Table 1.

Estimated times of divergence between A. fumigatus populations with upper and lower divergence time estimates given in units of 1,000 generations

Population #1 #2 #3 #4 #5
#2 167 – 67
#3 102 – 19 80 – 23
#4 76 – 0 102 – 32 111 – 33
#5 54 – 0 28 – 0 109 – 51 68 – 6
MTR-containing isolates within population #3 113 – 20 49 – 6 N/A 94 – 16 58 – 7
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Discussion

To investigate the role of reproductive mode and population structure in evolution of triazole drug resistance, we studied the A. fumigatus populations in the Netherlands, where the multi-triazole-resistant TR/L98H allele likely originated and was first reported (Verweij et al. 2007). Given the evidence that recombination, commonly associated with sexual reproduction, can be very advantageous in stressful environments (Goddard et al. 2005; Zeyl et al. 2005), such as inside a human host or upon exposure to a fungicide, we hypothesized that sexual reproduction was facilitating the emergence and/or spread of the TR/L98H allele. However, analysis of the data obtained in this study allowed us to reject our original hypothesis, suggesting instead that all TR/L98H alleles identified in our study nest within a single, predominantly asexual, population and have not spread across populations. These results emphasize the role of asexual reproduction and genetic differentiation in shaping the evolution of azole resistance in A. fumigatus.

High-resolution Markers Show Genetic Differentiation in Dutch A. fumigatus

To our knowledge, this is the first study that reports the existence of genetic differentiation in any part of the distribution of A. fumigatus, one of the most important opportunistic fungal pathogens of humans, and suggests that Dutch A. fumigatus genotypes group into five distinct populations. Consistent with our findings, another recent study reported the existence of two genetically differentiated lineages within A. fumigatus (Pringle et al. 2005), although in that case the authors argued, based on phylogenetic analysis, that these lineages were separate species. The detection of genetic differentiation in A. fumigatus argues against the “everything is everywhere” hypothesis, which states that highly abundant microbial eukaryote species with cosmopolitan distributions lack genetic differentiation (Finlay 2002).

One potential explanation for this discrepancy between past studies and ours might be the difference in the density and scale of sampling between studies. For example, in the two most comprehensive multilocus studies to date, 63 and 70 isolates from five different continents were analysed, respectively (Pringle et al. 2005; Rydholm et al. 2006), whereas our study examined a much larger number of isolates from a relatively small geographical area.

Another potential explanation for the discordance of these results with those from earlier studies might be our use of a much larger and more highly informative panel of markers (Figs. S4 and S5). All previous studies have employed either sequence-based or RFLP-based typing techniques (Debeaupuis et al. 1997; Pringle et al. 2005; Rydholm et al. 2006). Although these techniques are very reliable (Taylor et al. 1999b), they are typically less informative when compared to microsatellite markers (Bain et al. 2007). If the absence of differentiation in past studies of A. fumigatus population biology is to be explained by the use of less informative markers, then we expect that future studies on isolates from other geographic regions of comparable size using similar or superior markers (e.g., Harris et al. 2010) to ours are highly likely to identify genetically differentiated A. fumigatus populations. Interestingly, a previous study of A. fumigatus isolates performed using the most informative non-microsatellite marker from our panel did not find any evidence of genetic differentiation in North America (Balajee et al. 2007), suggesting that the pattern of differentiation of this important human pathogen might vary across its range of distribution.

Our results also show that the five identified A. fumigatus populations do not correlate with geography. In many eukaryotes, the presence of genetically differentiated populations is often the result of geographical isolation or ecological niche preference. Surprisingly, the populations in our study do not show any correlation with geography or environment of origin (clinical versus soil). A similar lack of correlation with geography was observed in A. flavus populations (Grubisha & Cotty 2010). In this study, the genetic diversity of 243 A. flavus isolates was analysed using 24 microsatellite loci and the mating type locus. Notably, all A. flavus populations were clonal, with no evidence of gene flow between populations.

Varying Levels of Recombination among Dutch A. fumigatus Populations

Four of the five Dutch populations show significant levels of LD, suggesting that recombination in these populations has been rare or absent. Significant LD can be due to several different reasons (Maynard Smith et al. 1993). For example, significant LD is expected in populations of organisms that do not possess any molecular mechanism for recombination such that clonal propagation is their sole means of reproduction. This explanation is unlikely to hold true for A. fumigatus. The sexual reproduction machinery in the A. fumigatus genome appears intact (Galagan et al. 2005; Rokas & Galagan 2008), the MAT loci are typically at near-equal frequencies in A. fumigatus populations (Paoletti et al. 2005) – this is also the case in our populations, and certain isolates can reproduce sexually in the laboratory (O’Gorman et al. 2009).

Another reason for significant LD values involves failure to account for the genetic differentiation of the species studied. In such cases, the presence of LD will likely reflect the lack of recombination between populations of the species, potentially masking recombination within populations. For example, analysis of the entire 156-genotype data set reveals significant levels of LD (Ia = 0.718, p < 0.001), masking the finding that population 1 is not in LD. Given that we first identified the pattern of genetic differentiation in A. fumigatus and then calculated LD separately for each genetically differentiated population, it is highly unlikely that our results are affected by this reason.

Furthermore, the observed varying levels of LD suggest that different A. fumigatus populations may exhibit different reproductive modes. Nearly two decades ago, Maynard Smith and colleagues distinguished microbial reproductive modes into three models: clonal (predominantly non-recombining), panmictic (predominantly recombining), and epidemic (predominantly recombining but shows significant associations between loci due to recent, explosive increases in particular genotypes) (Maynard Smith et al. 1993). On a first level of analysis, it appears that the observed pattern of reproductive modes across A. fumigatus populations, where four of the five populations are predominantly non-recombining, might be more similar to the epidemic model than to either the clonal or the panmictic one. One expectation of the epidemic model is that clonal populations are very recent and have undergone explosive growth, so they are expected to harbour little genetic diversity. However, neither the estimated times of divergence nor the levels of genetic diversity for most of the non-recombining A. fumigatus populations support the very recent origins. Similarly, levels of genotypic diversity are comparable across most populations, irrespective of the presence or absence of recombination. Rather, it appears that different A. fumigatus populations fit into different reproductive mode models.

How this variation in reproductive mode across A. fumigatus populations is controlled and whether these distinct populations use either sexual or asexual reproductive modes to regulate gene flow regardless of environmental signals remain open questions. It is known that both asexual and sexual reproductive modes can confer significant advantages as well as disadvantages to fungal populations (Sun & Heitman 2011), so the pattern of reproductive modes across populations might be determined by their balance. It is also theoretically possible that the only population of A. fumigatus identified in this study as recombining may in fact be reproducing asexually. This is so because, in fungi, genetic recombination can occur both during meiosis as part of the sexual cycle or during mitosis, as part of the parasexual cycle (Clutterbuck 1996; Taylor et al. 1999a). The effect of the parasexual cycle on long-term genetic exchange within fungal populations is thought to be limited because it typically takes place between genetically similar individuals (Clutterbuck 1996), but its true extent on recombination within A. fumigatus populations is unknown.

Population Structure, Recombination and their Implications for the Spread of the MTR Allele

Our finding that all genotypes containing the TR/L98H allele were confined to a single, predominantly asexually reproducing, population and has not yet spread across populations rejects our hypothesis that sexual reproduction facilitated the emergence and/or spread of the TR/L98H allele in A. fumigatus. Notably, very recent global surveillance studies have found isolates with the TR/L98H allele in both China and India (Lockhart et al. 2011; Chowdhary et al. 2011), but it is not yet known whether these isolates stem from the same population. This lack of recombination and lack of gene flow is puzzling; in environments lacking triazole drugs, retention of MTR-like alleles is likely to be costly (Cowen 2008; Cowen et al. 2001; Stergiopoulos et al. 2003), whereas in environments containing triazole drugs MTR-like alleles are likely to be strongly advantageous. In both cases, recombination (and associated gene flow) should be favored – to eliminate the costly resistant allele in the first case, or to fix the advantageous resistant allele in the second, and yet our evidence suggests that the population is predominantly asexual. One possible explanation is that the sexual reproduction mode in A. fumigatus populations cannot be “switched on” instantaneously once a population has become asexual because of the accumulation of deleterious mutations in mating and meiosis genes (Sun & Heitman 2011), despite the high costs associated with clonal reproduction during episodic selection, e.g., upon sporadic exposure to a fungicide.

When did the TR/L98H allele originate? Recently, Verweij and colleagues raised the hypothesis that the evolution of MTR resistance in A. fumigatus might have been a by-product of the use of azole compounds in agriculture (Verweij et al. 2009). Dating of the divergence of population 3 genotypes as well as of all TR/L98H allele containing genotypes within population 3 from the other populations (Table 1), implies that both groups diverged from the other populations ~6,000 generations ago. Thus, the genetic background of MTR allele-containing genotypes is likely more ancient than the first use of azole drugs in agriculture or medicine. Nevertheless, it is entirely plausible that the origin of the TR/L98H allele in a population 3 genetic background occurred much more recently (Verweij et al. 2009), a hypothesis consistent with the pattern of resistance spreading from the Netherlands to the rest of Europe. Future studies that examine the global genetic structure of A. fumigatus using a similarly informative set of markers are likely to be highly instructive on how the genetic differentiation and reproductive mode of A. fumigatus populations shape the evolutionary dynamics of drug resistance patterns of this deadly human pathogen.

From a practical standpoint, elucidating the role of genetic differentiation and reproductive mode in influencing the genetic structure of A. fumigatus can also facilitate the development of diagnostic tools to detect resistant infections and preventive measures aimed to curb the spread of azole resistance in fungal populations. As the frequency of azole resistant A. fumigatus isolates continues to increase in several European countries, a better understanding of the origin and spread of MTR alleles across A. fumigatus is becoming critical.

Supplementary Material

Supp Figure S1-S8

Fig. S1 The effect of different clonal correction thresholds on the number of non-clonal genotypes identified from 255 Dutch A. fumigatus isolates. The X-axis corresponds to a wide range of clonal correction thresholds, from requiring that genotypes are identical in all markers (microsatellites and non-microsatellites) before considered clonal to requiring that genotypes are only identical across the non-microsatellite markers. The Y-axis shows the number of unique genotypes identified for the different clonal correction thresholds.

Fig. S2 STRUCTURE and DAPC analysis of the microsatellite marker (panels A and B), non-microsatellite marker (panels C and D), and full marker (panels E and F) data sets. Both approaches predict K = 5 as the optimal number of populations for the full marker data set.

Fig. S3 The optimal number of populations K predicted by STRUCTURE analysis on data sets with varying levels of clonal correction. For each data set, the average log probability (LnP(D)) of each K value (black line) and the ad hoc statistic ΔK (grey line) were calculated. The data sets are as follows: exclusion of all but one randomly chosen genotype with identical alleles for the n.3, ANXC4, BGT1, RM1–7, and CSP markers and with identical alleles in 9 (panel A), 8 (panel B), 7 (panel C), 6 (panel D), 5 (panel E), or 4 – 0 (panel F) identical microsatellite markers.

Fig. S4 The distribution of alleles for each of the 9 microsatellite markers and the one indel marker used in this study across the five A. fumigatus populations, as delineated by the STRUCTURE analysis. Populations 1 – 5 are indicated by red, green, blue, yellow and pink color, respectively. The X-axis displays the different alleles for each marker, and the Y-axis the number of genotypes with that allele.

Fig. S5 The distribution of alleles for each of the 10 sequence/PCR-typing markers used in this study across the five A. fumigatus populations, as delineated by the STRUCTURE analysis. Populations 1 – 5 are indicated by red, green, blue, yellow and pink color, respectively. The X-axis displays the different alleles for each marker, and the Y-axis the number of genotypes with that allele.

Fig. S6 Strength of association between markers and populations according to Cramér’s V statistic (Cramér 1999). Asterisks (*) denote markers that do not show a statistically significant association with population structure.

Fig. S7 STRUCTURE analysis is highly consistent across replicates. Shown are the plots of the 100 STRUCTURE replicates after output data were processed using the CLUMPP software (Jakobsson & Rosenberg 2007) to correct for label switching across replicates. Samples were sorted first by population and then by numeric order (X axis). The Y axis represents an individual’s membership coefficient to each population. STRUCTURE populations 1 – 5 are represented by red, green, blue, yellow and pink color, respectively.

Fig. S8 Geographic origin is not associated with genetic differentiation. (A) Individuals were grouped into populations based on their city of origin or (B) STRUCTURE population assignment. Pie charts represent AMOVA results explaining the variance found within and among populations. Tables represent pairwise ϕPT values (lower diagonal)and probability values of population differentiation based on 999 permutations (upper diagonal). Grey boxes represent significant population differentiation at a p-value cutoff of 0.05.

Supp Table S1-S3

Table S1 The nomenclature, description, marker type, genomic location, and origin of the 20 markers used in this study.

Table S2 The population assignment of the 156 non-clonally related clinical and environmental A. fumigatus genotypes from the Netherlands into five populations using the STRUCTURE and DAPC approaches.

Table S3 The individual membership coefficients of the 156 non-clonally related clinical and environmental Dutch A. fumigatus genotypes from the STRUCTURE replicate used in this study against the average individual membership coefficients from the 100 STRUCTURE replicates calculated using the CLUMPP software.

Acknowledgments

We thank Zeev Frenkel, Abraham Korol, and Nai Tran-Dinh for access to raw Aspergillus genotype data, Dr. A. Chowdhary for access to unpublished data, and Thibaut Jombart for assistance with the DAPC approach. We are also grateful to David McCauley and the reviewers for their helpful suggestions and advice. This work was conducted in part using the resources of the Advanced Computing Center for Research and Education at Vanderbilt University. This project has been funded in part with federal funds from the National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services under contract numbers N01-AI30071 and/or HHSN272200900007C, the National Institute of Allergy and Infectious Diseases, National Institutes of Health (NIH, NIAID: F31AI091343-01 to JGG), the Searle Scholars Program (AR), and the National Science Foundation (DEB-0844968 to AR). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIAID or the NIH.

Footnotes

Data Accessibility:

Genotype data and sample information: DRYAD entry doi:10.5061/dryad.7m0797t0

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supp Figure S1-S8

Fig. S1 The effect of different clonal correction thresholds on the number of non-clonal genotypes identified from 255 Dutch A. fumigatus isolates. The X-axis corresponds to a wide range of clonal correction thresholds, from requiring that genotypes are identical in all markers (microsatellites and non-microsatellites) before considered clonal to requiring that genotypes are only identical across the non-microsatellite markers. The Y-axis shows the number of unique genotypes identified for the different clonal correction thresholds.

Fig. S2 STRUCTURE and DAPC analysis of the microsatellite marker (panels A and B), non-microsatellite marker (panels C and D), and full marker (panels E and F) data sets. Both approaches predict K = 5 as the optimal number of populations for the full marker data set.

Fig. S3 The optimal number of populations K predicted by STRUCTURE analysis on data sets with varying levels of clonal correction. For each data set, the average log probability (LnP(D)) of each K value (black line) and the ad hoc statistic ΔK (grey line) were calculated. The data sets are as follows: exclusion of all but one randomly chosen genotype with identical alleles for the n.3, ANXC4, BGT1, RM1–7, and CSP markers and with identical alleles in 9 (panel A), 8 (panel B), 7 (panel C), 6 (panel D), 5 (panel E), or 4 – 0 (panel F) identical microsatellite markers.

Fig. S4 The distribution of alleles for each of the 9 microsatellite markers and the one indel marker used in this study across the five A. fumigatus populations, as delineated by the STRUCTURE analysis. Populations 1 – 5 are indicated by red, green, blue, yellow and pink color, respectively. The X-axis displays the different alleles for each marker, and the Y-axis the number of genotypes with that allele.

Fig. S5 The distribution of alleles for each of the 10 sequence/PCR-typing markers used in this study across the five A. fumigatus populations, as delineated by the STRUCTURE analysis. Populations 1 – 5 are indicated by red, green, blue, yellow and pink color, respectively. The X-axis displays the different alleles for each marker, and the Y-axis the number of genotypes with that allele.

Fig. S6 Strength of association between markers and populations according to Cramér’s V statistic (Cramér 1999). Asterisks (*) denote markers that do not show a statistically significant association with population structure.

Fig. S7 STRUCTURE analysis is highly consistent across replicates. Shown are the plots of the 100 STRUCTURE replicates after output data were processed using the CLUMPP software (Jakobsson & Rosenberg 2007) to correct for label switching across replicates. Samples were sorted first by population and then by numeric order (X axis). The Y axis represents an individual’s membership coefficient to each population. STRUCTURE populations 1 – 5 are represented by red, green, blue, yellow and pink color, respectively.

Fig. S8 Geographic origin is not associated with genetic differentiation. (A) Individuals were grouped into populations based on their city of origin or (B) STRUCTURE population assignment. Pie charts represent AMOVA results explaining the variance found within and among populations. Tables represent pairwise ϕPT values (lower diagonal)and probability values of population differentiation based on 999 permutations (upper diagonal). Grey boxes represent significant population differentiation at a p-value cutoff of 0.05.

NIHMS333914-supplement-Supp_Figure_S1-S8.pdf (3MB, pdf)
Supp Table S1-S3

Table S1 The nomenclature, description, marker type, genomic location, and origin of the 20 markers used in this study.

Table S2 The population assignment of the 156 non-clonally related clinical and environmental A. fumigatus genotypes from the Netherlands into five populations using the STRUCTURE and DAPC approaches.

Table S3 The individual membership coefficients of the 156 non-clonally related clinical and environmental Dutch A. fumigatus genotypes from the STRUCTURE replicate used in this study against the average individual membership coefficients from the 100 STRUCTURE replicates calculated using the CLUMPP software.

NIHMS333914-supplement-Supp_Table_S1-S3.xls (115.5KB, xls)

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