Angeline Bertin
email: abertin@userena.cl

Nicolas Gouin

Alex Baumel

Ernesto Gianoli

Juan Serratosa

Rodomiro Osorio

Stephanie Manel

Angéline Bertin

Genetic variation of loci potentially under selection confounds species-genetic diversity correlations in a fragmented habitat

Keywords: community genetics, species-genetic diversity correlations, outlier loci, habitat connectivity, high Andean wetlands

Positive species-genetic diversity correlations (SGDCs) are often thought to result from the parallel influence of neutral processes on genetic and species diversity. Yet, confounding effects of non-neutral mechanisms have not been explored. Here, we investigate the impact of nonneutral genetic diversity on SGDCs in high Andean wetlands. We compare correlations between plant species diversity (SD) and genetic diversity (GD) calculated with and without loci potentially under selection (outlier loci). The study system includes 2188 specimens from five species (three common aquatic macroinvertebrate and two dominant plant species) that were genotyped for 396 amplified fragment length polymorphism loci. We also appraise the importance of neutral processes on SGDCs by investigating the influence of habitat fragmentation features. Significant positive SGDCs were detected for all five species (mean SGDC = 0.52 ± 0.05). While only a few outlier loci were detected in each species, they resulted in significant decreases in GD and in SGDCs. This supports the hypothesis that neutral processes drive species-genetic diversity relationships in high Andean wetlands. Unexpectedly, the effects on genetic diversity of the habitat fragmentation characteristics in this study increased with the presence of outlier loci in two species. Overall, our results reveal pitfalls in using habitat features to infer processes driving SGDCs and show that a few loci potentially under selection are enough to cause a significant downward bias in SGDC. Investigating confounding effects of outlier loci thus represents a useful approach to evidence the contribution of neutral processes on speciesgenetic diversity relationships.

Introduction

Investigating spatial or temporal patterns of covariation between species diversity within a community (SD) and genetic diversity within a species (GD) is one of the main objectives of community genetics [START_REF] Antonovics | Plant resistance to herbivores and pathogens: ecology, evolution, genetics[END_REF]. It is a key step towards a conceptual synthesis at the interface between ecology and evolutionary biology [START_REF] Vellend | Connections between species diversity and genetic diversity[END_REF]. It is also a core issue in conservation biology as it provides valuable information to understand and predict different facets of biodiversity [START_REF] Hu | Paleoecology meets genetics: deciphering past vegetational dynamics[END_REF][START_REF] Lavergne | Biodiversity and Climate Change: Integrating Evolutionary and Ecological Responses of Species and Communities[END_REF][START_REF] Mouquet | Ecophylogenetics: advances and perspectives[END_REF].

Studies addressing species genetic diversity correlations (SGDCs) in the wild have shown that the relationship between GD and SD can take different forms depending on the mechanisms at work [START_REF] Kahilainen | Conservation implications of speciesgenetic diversity correlations[END_REF], with reports of positive [START_REF] He | Covariation between intraspecific genetic diversity and species diversity within a plant functional group[END_REF][START_REF] Papadopoulou | Testing the Species-Genetic Diversity Correlation in the Aegean Archipelago: Toward a Haplotype-Based Macroecology[END_REF][START_REF] Lamy | Variation in habitat connectivity generates positive correlations between species and genetic diversity in a metacommunity[END_REF], negative [START_REF] Silvertown | Community genetics: resource addition has opposing effects on genetic and species diversity in a 150-year experiment[END_REF][START_REF] Xu | Soil properties drive a negative correlation between species diversity and genetic diversity in a tropical seasonal rainforest[END_REF]) and no correlation [START_REF] Taberlet | Genetic diversity in widespread species is not congruent with species richness in alpine plant communities[END_REF] between the two components. Concordant spatial patterns between species and genetic diversity can emerge from various processes.

They may result from direct causal relationships between the two biodiversity components [START_REF] Vellend | Connections between species diversity and genetic diversity[END_REF]. For instance, GD can influence SD when genetic variation of habitat-forming species, such as forest trees, structures dependent communities (i.e. microorganisms, fungi, arthropods and vertebrates, [START_REF] Whitham | A framework for community and ecosystem genetics: from genes to ecosystems[END_REF]. Alternatively, SD in the community can impose variable selective pressures on populations, and therefore alter the levels of population GD [START_REF] Strauss | Toward a more trait-centered approach to diffuse (co)evolution[END_REF][START_REF] Vellend | Connections between species diversity and genetic diversity[END_REF][START_REF] Johnson | An emerging synthesis between community ecology and evolutionary biology[END_REF][START_REF] Lankau | Mutual feedbacks maintain both genetic and species diversity in a plant community[END_REF]. SD and GD can become indirectly associated as well, by responding similarly to common mechanisms [START_REF] Vellend | Connections between species diversity and genetic diversity[END_REF]. Indeed, both ecological and genetic models posit similar effects of drift and migration on species and genetic diversity (e.g. depletion effects of drift and diversity increases from immigration, [START_REF] Hu | Paleoecology meets genetics: deciphering past vegetational dynamics[END_REF]. Thus, concordant spatial patterns between species and genetic diversity can be common under conditions of demographic stochasticity and dispersal limitation [START_REF] Rosenzweig | Species diversity in space and time[END_REF][START_REF] Frankham | Relationship of genetic variation to population size in wildlife[END_REF][START_REF] Frankham | Do island populations have less genetic variation than mainland populations?[END_REF]. To date, little research has been conducted into the drivers of negative SGDCs [START_REF] Xu | Soil properties drive a negative correlation between species diversity and genetic diversity in a tropical seasonal rainforest[END_REF].

According to a modeling study, negative SGDCs can occur under particular conditions of high genetic mutation rates, depending on the relative importance of mutation, immigration and local competition [START_REF] Laroche | A Neutral Theory for Interpreting Correlations between Species and Genetic Diversity in Communities[END_REF]. They may also occur in situations where increases in SD, promoted by environmental heterogeneity, are associated with a reduction in niche breadth and/or population size [START_REF] Taberlet | Genetic diversity in widespread species is not congruent with species richness in alpine plant communities[END_REF][START_REF] Vellend | Drawing ecological inferences from coincident patterns of population-and community-level biodiversity[END_REF][START_REF] Xu | Soil properties drive a negative correlation between species diversity and genetic diversity in a tropical seasonal rainforest[END_REF].

While both neutral and selective mechanisms can generate positive covariation between genetic and species diversity [START_REF] Vellend | Connections between species diversity and genetic diversity[END_REF], evidence suggests that neutral processes play a dominant role of in this respect [START_REF] Odat | Genetic diversity of Ranunculus acris L. (Ranunculaceae) populations in relation to species diversity and habitat type in grassland communities[END_REF][START_REF] Vellend | Parallel effects of land-use history on species diversity and genetic diversity of forest herbs[END_REF][START_REF] Struebig | Parallel declines in species and genetic diversity in tropical forest fragments[END_REF][START_REF] Papadopoulou | Testing the Species-Genetic Diversity Correlation in the Aegean Archipelago: Toward a Haplotype-Based Macroecology[END_REF][START_REF] Lamy | Variation in habitat connectivity generates positive correlations between species and genetic diversity in a metacommunity[END_REF][START_REF] Vellend | Drawing ecological inferences from coincident patterns of population-and community-level biodiversity[END_REF].

Positive SGDCs are common in discrete habitat units like oceanic islands, lakes, or forest fragments, and less frequent in arbitrarily defined spatial units such as grid cells or vegetation plots [START_REF] Vellend | Connections between species diversity and genetic diversity[END_REF][START_REF] Whitlock | Relationships between adaptive and neutral genetic diversity and ecological structure and functioning: a meta-analysis[END_REF][START_REF] Vellend | Drawing ecological inferences from coincident patterns of population-and community-level biodiversity[END_REF]. In fragmented landscapes, migration and drift are likely major contributors to within-patch diversity. Thus, positive SGDCs among islands and habitat patches have been postulated to arise from the parallel influence of neutral processes on both species and genetic diversity [START_REF] Vellend | Connections between species diversity and genetic diversity[END_REF][START_REF] Whitlock | Relationships between adaptive and neutral genetic diversity and ecological structure and functioning: a meta-analysis[END_REF][START_REF] Vellend | Drawing ecological inferences from coincident patterns of population-and community-level biodiversity[END_REF]. This is supported by a number of empirical studies, which have demonstrated that local habitat characteristics influencing rates of stochastic immigration and extinction contribute markedly to species-genetic diversity relationships in fragmented habitats [START_REF] Vellend | Species diversity and genetic diversity: Parallel processes and correlated patterns[END_REF][START_REF] He | Covariation between intraspecific genetic diversity and species diversity within a plant functional group[END_REF][START_REF] Papadopoulou | Testing the Species-Genetic Diversity Correlation in the Aegean Archipelago: Toward a Haplotype-Based Macroecology[END_REF][START_REF] Lamy | Variation in habitat connectivity generates positive correlations between species and genetic diversity in a metacommunity[END_REF]. These habitat features include habitat size, connectivity and perturbation. Indeed, both habitat carrying capacity and heterogeneity are expected to increase with habitat size. Larger patches can thus support larger populations, which are both more genetically diverse and more resilient to genetic drift [START_REF] Frankham | Do island populations have less genetic variation than mainland populations?[END_REF]). In addition, by offering more diverse ecological niches, larger habitats are also expected to hold more diverse communities, and are consequently less susceptible to random species loss through ecological drift [START_REF] Macarthur | The theory of island biogeography[END_REF]Wilson 1967, Hubbell 2001).

Because habitat connectivity influences rates of migration, it is expected to result in increases in both genetic and species diversity, by facilitating the introduction of new alleles in a population or new species in the community [START_REF] Hu | Paleoecology meets genetics: deciphering past vegetational dynamics[END_REF]. Regarding habitat perturbation, it can cause parallel effects on species and genetic diversity by provoking extinction of species and alleles irrespectively of their identities (Vellend 2003).

Neutral explanations for positive SGDCs have also come from the wide use of presumed neutral molecular markers to estimate genetic diversity in SGDC investigations [START_REF] Vellend | Connections between species diversity and genetic diversity[END_REF][START_REF] Vellend | Drawing ecological inferences from coincident patterns of population-and community-level biodiversity[END_REF]. Genetic variation of molecular markers with supposedly no effects on fitness, such as microsatellite or amplified fragment length polymorphism (AFLP) loci, is considered to mirror neutral processes, including mutation, genetic drift and gene flow [START_REF] Holderegger | Adaptive vs. neutral genetic diversity: implications for landscape genetics[END_REF][START_REF] Kirk | Applications and Implications of Neutral versus Non-neutral Markers in Molecular Ecology[END_REF]. SGDCs estimated with these markers have therefore been assumed to emerge from concordant changes in species and genetic diversity induced by neutral processes [START_REF] Vellend | Connections between species diversity and genetic diversity[END_REF][START_REF] Lamy | Variation in habitat connectivity generates positive correlations between species and genetic diversity in a metacommunity[END_REF][START_REF] Vellend | Drawing ecological inferences from coincident patterns of population-and community-level biodiversity[END_REF]. However, no attention has been paid to confounding effects of non-neutral mechanisms, which may lead to erroneous conclusions regarding the mechanisms causing SD-GD covariation (Fig. 1). Indeed, selection generates changes in allele frequencies at target genes, but also in adjacent, linked regions (genetic hitchhiking; [START_REF] Cemex | Genetic hitchhiking[END_REF]. Such linked DNA regions, even non-coding DNA, can thus bear signatures of selection (confounding effect 1 in Fig. 1; [START_REF] Cemex | Genetic hitchhiking[END_REF][START_REF] Oleksyk | Genome-wide scans for footprints of natural selection[END_REF].

When used to infer neutral processes, such loci may bias population parameter estimates [START_REF] Luikart | The power and promise of population genomics: From genotyping to genome typing 13[END_REF]. Their expected effects on genetic diversity depend on the type of selection operating, as each selection mode leads to specific patterns of allele frequency changes [START_REF] Oleksyk | Genome-wide scans for footprints of natural selection[END_REF]. For instance, divergent directional selection decreases genetic variation within populations, while balancing selection maintains variation within populations. A few loci potentially under divergent selection are enough to cause a significant downward bias in the assessment of GD estimated from markers assumed to be neutral [START_REF] García-Verdugo | Do island plant populations really have lower genetic variation than mainland populations? Effects of selection and distribution range on genetic diversity estimates[END_REF]. The presence of such markers can thus be misleading for SGDC studies. When SGDCs are driven by neutral mechanisms, loci under selection are likely to decrease the correlation between species and genetic diversity, since only neutral GD is expected to covary with SD in such circumstances. In contrast, no such effects are expected when SGDCs arise from non-neutral processes.

Separating the neutral and non-neutral components of GD not only opens new perspectives for the study of SGDCs, but can also be critical to interpreting apparent effects of local habitat features in a neutral framework. Several studies have investigated how disturbance regime, habitat size and/or connectivity relate to SGDCs in order to infer underlying mechanisms [START_REF] Vellend | Connections between species diversity and genetic diversity[END_REF][START_REF] Papadopoulou | Testing the Species-Genetic Diversity Correlation in the Aegean Archipelago: Toward a Haplotype-Based Macroecology[END_REF][START_REF] Lamy | Variation in habitat connectivity generates positive correlations between species and genetic diversity in a metacommunity[END_REF][START_REF] Vellend | Drawing ecological inferences from coincident patterns of population-and community-level biodiversity[END_REF]. These studies postulate that these local features become associated with neutral components of SD/GD due to their modulating role on rates of migration and stochastic loss of species/alleles. However, these relationships can be mistakenly attributed to neutral processes (confounding effect 2 in Fig. 1) if disturbance regime, habitat size and/or connectivity and selective environmental factors are spatially correlated, as they would lead to indirect associations between non-neutral components of GD and the local habitat features. These confounding effects have been documented for species richness-island area associations [START_REF] Ricklefs | The roles of island area per se and habitat diversity in the species-area relationships of four Lesser Antillean faunal groups[END_REF], and are expected at the genetic level since populations at the margins of a species distribution range are not only more fragmented and isolated than central populations, but also subject to different ecological constraints (central-marginal hypothesis, [START_REF] Eckert | Genetic variation across species' geographical ranges: the central-marginal hypothesis and beyond[END_REF]). In such situations, factoring out the effects of disturbance regime, habitat size and/or connectivity bears the risk of overestimating the contribution of neutral processes on SD and GD, as it is likely to control for some influence of selective factors as well.

In this study, we aimed to disentangle the confounding effects of non-neutral genetic diversity on species-genetic diversity relationships in a highly fragmented ecosystem, the high Andean wetlands of Chile's Norte Chico. These wetlands, formed by groundwater upwelling, resemble oases in an arid matrix (Fig. 2, [START_REF] Squeo | Bofedales: high altitude peatlands of the central Andes[END_REF]. They occupy a latitudinal gradient of both aridity (increasing northwards) and wetland density (Fig. 2). This suggests that wetlands differing in connectivity also experience different selective pressures. Given the high degree of fragmentation of these ecosystems, we hypothesized that neutral processes should play a critical role in species-genetic diversity relationships. Thus, we expected loci influenced by selection to decrease SGDCs. We also postulated that such loci can complicate the evaluation of the importance of neutral processes on SGDC by factoring out the effects of habitat connectivity on SD and GD. Indeed, due to the spatial covariation between habitat fragmentation and the latitudinal environment gradient, GD of loci under environmental gradient pressure is also expected to correlate with fragmentation characteristics. To test these conjectures, we investigated SGDCs between species diversity of high Andean wetland vegetation and GDs of five species (two plants and three macroinvertebrates) with contrasting life-histories. We used a genome scan approach to screen for AFLP markers potentially under selection (i.e. outlier loci; [START_REF] Storz | Genome scans of DNA variability in humans reveal evidence for selective sweeps outside of Africa[END_REF]) and analyzed SGDCs and the effects of habitat characteristics (size, connectivity and stability) on SGDCs with and without these loci. Our results showed that, as expected, confounding effects of outlier loci on GD decrease SGDCs. This trend was particularly evident with loci potentially under divergent selection. We also found that, in some species, GD at outlier loci correlate with habitat features supposedly linked to neutral processes, revealing pitfalls in using such characteristics to infer processes driving SGDCs.

Material and methods

Study system

We analyzed species and genetic diversity for 21 high Andean wetlands located along a ca. 600 km latitudinal range (26°S-32°S) in north-central Chile (Fig. 2, and see [START_REF] Bertin | Effects of wind-driven spatial structure and environmental heterogeneity on high-altitude wetland macroinvertebrate assemblages with contrasting dispersal modes[END_REF]. The region, known as Norte Chico, is a biodiversity hotspot, and is characterized by remarkably high levels of endemism [START_REF] Arroyo | Chilean winter rainfall-Valdivian forests[END_REF]. It includes five hydrologically unconnected river basins. The climate of the region varies from hyperarid in the north to mediterranean in the south, with mean annual precipitation ranging between 35 mm and 200 mm for the northernmost and southernmost valleys, respectively.

Data collection

Community and population sampling

Sampling of plant and benthic macroinvertebrate specimens was carried out between March and April 2011. For plant diversity assessment, the length of each wetland was divided into five sectors and a 30 x 30 cm quadrat was randomly placed within each sector. Plant species were separated and identified in the laboratory.

For genetic diversity evaluation, we selected five common species: two dominant plants, Carex gayana (Cyperaceae) and Patosia clandestina (Juncaceae), and three abundant aquatic macroinvertebrates, Andesiops peruvianus (Insecta, Ephemeroptera), Austrelmis sp. (Insecta, Coleoptera) and Hyallela fossamancinii (Crustacea, Amphipoda).

Samples were collected by hand or using a small fishing net in the case of aquatic organisms. The entire wetland surface area was sampled in each case, with individual wetlands ranging in size from 3.7 to 38 ha. Leaf samples were conserved individually in silica gel until DNA extraction, whereas macroinvertebrates were stored in 95% alcohol.

The plant C. gayana was the only species found to occur within all 21 wetlands sampled.

Details regarding sampling sites and size for each species are given in Table S1.

Genetic data

For all five species, DNA extraction was performed using a CTAB protocol. AFLP amplifications and genotyping were carried out using standard procedures and following the protocol of [START_REF] Meudt | Almost forgotten or latest practice? AFLP applications, analyses and advances[END_REF], available at http://clarkeresearch.org/aflp_2012-01-26/aflp.html, with very few modifications. In each species, four combinations of specific primers were selected based on their reliability and number of fragments amplified. AFLP fragments were separated by capillary electrophoresis and genotyping was performed with the software GeneMarker v2.4 (Softgenetics). Following [START_REF] Bonin | Statistical analysis of amplified fragment length polymorphism data: a toolbox for molecular ecologists and evolutionists 6[END_REF], we retained only AFLP loci with genotyping error rate < 10%, with the exception of the crustacean H. fossamancinii, for which we included loci with up to 12% genotyping error in order to maintain a comparable number of loci for all species. Loci with redundant information were filtered using the software AFLPOP [START_REF] Duchesne | AFLPOP: a computer program for simulated and real population allocation, based on AFLP data[END_REF].

Detailed procedures and AFLP primer pairs are described in Supplementary Information.

Only polymorphic markers (those with band frequencies between 5% and 95%) were kept in the analysis [START_REF] Bonin | Statistical analysis of amplified fragment length polymorphism data: a toolbox for molecular ecologists and evolutionists 6[END_REF].

Habitat parameters

Habitat attributes associated with connectivity, size and stability were assessed for each of the 21 sampled wetlands. Attributes were derived from maps of wetland distribution generated based on spectral analysis of Landsat 8 OLI satellite imagery (http://glovis.usgs.gov/, see Fig. 2) and elevation data. Normalized Difference Vegetation Index, a measure of actively photosynthesizing vegetation (NDVI, [START_REF] Rouse | Monitoring vegetation systems in the Great Plains with ERTS[END_REF], was first calculated using Landsat data (January 2011) for a training dataset of 87 high Andean wetlands mapped on-site in the Norte Chico region by Chile's Ministry of Environment and Agricultural and Livestock Service. Based on the mean NDVI value of all pixels comprising the 87 field-mapped wetlands, and applying the minimum observed elevation value as a cut-off, we defined wetlands here as all vegetated pixels with NDVI values > 0.2, located above 1800 m a.s.l. To exclude sparse vegetation from the mapping, only vegetated areas ≥ 0.36 ha were considered.

We calculated various local structural connectivity metrics for each wetland using Graphab 1.0 [START_REF] Foltete | A software tool dedicated to the modelling of landscape networks[END_REF], including node degree (Dg), clustering coefficient (CC), closeness centrality (CCe), eccentricity (Ec), betweenness centrality (BC) and wetland density. These metrics were quantified by considering all the wetlands mapped within a 20 km radius from the focal site. In addition, we estimated the surface of each focal wetland from the GIS-based map (Fig. 2). As a proxy for wetland stability, we used the temporal range of the mean NDVI of each site calculated over 20 years (from 1991 to 2002 and from 2004 to 2011). Before evaluating the impact of the habitat metrics on diversity, we removed strongly correlated predictors by excluding one variable when a Pearson's correlation between a pair of habitat metrics was > 0.7. The final set of predictors included wetland size, Dg, CCe, Ec, BC and the temporal range of NDVI wetland values.

Statistical analysis

Identification of outlier loci

We used the Bayesian likelihood FST-based method implemented in the software BayeScan [START_REF] Foll | A Genome-Scan Method to Identify Selected Loci Appropriate for Both Dominant and Codominant Markers: A Bayesian Perspective[END_REF] to identify outlier loci. This approach estimates for each locus the posterior probabilities of two alternative models: a neutral one and a model including selection. We performed the analyses using the default parameters and considered a conservative value of 10 for the prior odds (10:1 odds in favor of the neutral model). Loci with FST values substantially higher than the observed norm are considered potentially under diversifying selection, while those with substantially lower FST values are considered potentially under balancing or purifying selection [START_REF] Foll | A Genome-Scan Method to Identify Selected Loci Appropriate for Both Dominant and Codominant Markers: A Bayesian Perspective[END_REF]. These outlier loci were identified with the R function plot_bayescan as described in the user's manual, using a false discovery rate (FDR, the q-value threshold) of 5%. Accordingly, we examined the effects of outlier loci by creating three marker datasets and considering their FST values: a dataset free of outliers (DS1), one with all the genotyped loci, thus including both the non-outlier and all the outlier loci (i.e. with high and low FST, DS2) and one dataset including the non-outlier loci and the outlier loci with high FST only (DS3).

Species richness, genetic diversity and the influence of outlier loci on genetic diversity estimates

We used the statistical software R (R Core Team 2015) to calculate diversity indices and perform SGDC analyses. We estimated species diversity within each wetland as species richness of all the collected quadrats, and within-population genetic diversity as Nei's gene diversity [START_REF] Nei | Molecular Evolutionary Genetics[END_REF]. We calculated the bootstrapped 95% confidence interval of species richness and Nei's indices by considering 10,000 bootstrap replications using the R package rich [START_REF] Rossi | Rich: an R package to analyse species richness[END_REF]) and R functions of AFLPdat [START_REF] Ehrich | AFLPDAT: a collection of R functions for convenient handling of AFLP data[END_REF], respectively. Nei's gene indices were computed for the three marker datasets separately (DS1, DS2 and DS3).

We tested the effects of outlier loci on GD by carrying out linear mixed models (LMM) of Nei's gene diversity (dependent variable) in relation to the presence of outlier loci (fixed effect), and including species and site as crossed random factors. We performed two such analyses, testing the effects of all the outlier loci (with datasets DS1 and DS2), and of those with high FST only (with datasets DS1 and DS3). These analyses were also performed for each species separately.

Effects of habitat characteristics on species and genetic diversity

We used partial least-square (PLS) regressions to analyze the influence of habitat characteristics on species and genetic diversity. PLS regressions are recommended in analyses involving a large number of potentially correlated predictor variables and few observations [START_REF] Mevik | The pls package: Principal component and partial least squares regression in R[END_REF], as in the case of the present study. Such analyses first produce a set of uncorrelated linear combinations of the original predictors, the PLS components, calculated so as to maximize the covariance between the predictor and response variables [START_REF] Mevik | The pls package: Principal component and partial least squares regression in R[END_REF]. Then, the PLS components are used to derive the linear regression of the dependent variable (i.e. species or genetic diversity in this case). To establish the optimal number of PLS components, we used a leave-one-out cross-validation strategy [START_REF] Mevik | Mean squared error of prediction (MSEP) estimates for principal component regression (PCR) and partial least squares regression (PLSR)[END_REF]. Significance of the PLS model was assessed by testing the significance of the Pearson correlation between the observed and predicted values. To determine which habitat characteristics contributed most to the model, we examined the loading weights and performed approximate t-tests based on jackknife variance estimates of regression coefficients, calculated based on the optimal number of PLS components.

Species and genetic diversity correlations

For each marker dataset, we computed Pearson correlations between plant species richness and Nei's gene diversity index. One-tail tests with Bonferroni-Holm corrections were applied to correct for multiple comparisons. In order to assess the overall trend in SGDCs, we calculated Student's t-tests to determine whether the average correlation calculated from the five species was greater than zero. To investigate whether the relationships between species and genetic diversity were mediated by habitat isolation, stability, and size, we performed partial Pearson correlations after controlling for significant effects of habitat characteristics on species and genetic diversity. Significance of the residual correlations was tested as described above for the raw SGDC coefficients. LMMs were performed to test for differences in SGDCs between DS1 and DS2 (dataset as fixed effect), using species as a random factor.

Results

Identification of the outlier loci

Depending on the species, between 66 and 90 polymorphic AFLP loci were retained in our study (Table S2). The BayeScan analysis identified between one and ten outlier loci per species, representing between 1.2% and 15.1% of the AFLP loci, respectively, with an average of 6.8% over all species (Table S2). Most of these outlier loci had high FST values, but in P. clandestina and Austrelmis sp. two loci showed low FST values (Table S2).

Species richness and genetic diversity

Plant species richness ranged from 6 to 21 species per wetland (Table S3) and decreased with latitude (Fig. S1). The two plant species showed low genetic diversity overall (range of average Nei´s gene diversity indices: 0.11-0.13; Table S4), varying moderately to substantially between wetlands (more than six-fold in C. gayana; Table S4). Genetic diversity estimates of macroinvertebrates were higher (range of average Nei´s gene diversity indices: 0.11-0.28; Table S4) but less variable (Table S4). The presence of outlier loci significantly influenced Nei's gene diversity. These effects were detected in four of the five species (Fig. 3), and as an overall trend across species (LMM, dataset effect: LR = 12.52, df = 1, P < 0.001 for DS1 vs DS2; LR = 24.77, df = 1, P < 0.001 for DS1 vs DS3).

The only species for which we failed to detect outlier loci effects on genetic diversity was C. gayana, but only one outlier was identified for this species. Overall, GD estimates decreased with the presence of outlier loci (Fig. 3, Fig. S1), and this trend was more pronounced when only outliers with high FST were included in the genetic datasets than when both outliers with high and low FST were included.

Species-genetic diversity correlations and the influence of outlier loci

Our analyses revealed positive correlations between species richness and genetic diversity (Table S5). The average SGDCs of the five species was high (ranging from 0.51 ± 0.07 for DS3 to 0.54 ± 0.05 for DS1, Table S5) and significantly positive overall (t-tests: t = 17.4-25.5, df = 4, P < 0.001 in all cases). All SGDCs were positive and significant with respect to each species (P < 0.05 in all cases after correcting for multiple comparisons). They ranged from 0.47 to 0.60 for DS1 (Table S5). The presence of outlier loci significantly influenced the SGDCs (Fig. 4A, LMM, dataset effect: LR = 6.05, df = 2, P = 0.05), causing a decrease in SGDCs overall (Fig 4A, Table S5). This trend was particularly marked in P. clandestina (Fig. 4A), for which the SGDC estimate dropped by 13.5% when outlier loci with high FST were included (Fig. 4A).

Influence of habitat fragmentation on species richness, genetic diversity and SGDCs

The PLS regressions explained 49% of the variation in wetland plant richness (Table 1).

Significant influence of wetland size and connectivity on genetic diversity was found for the plants and the mayfly species (A. peruvianus), but no such effects were detected for the long-lived aquatic species (H. fossamancinii and Austrelmis sp). Wetland isolation was an important determinant of plant species richness and genetic diversity. We found a negative influence of the average distance to neighboring patches (i.e. closeness centrality, Table 1) and positive effects of the number of neighboring patches (i.e. node degree, Table 1) on genetic and species diversity. A positive effect of the distance to the farthest wetland (i.e. eccentricity) on species richness and genetic diversity was observed for the plants and the mayfly species (Table 1).

The effects of habitat fragmentation on GD were not lowered by the presence of outlier loci (i.e. DS2 and DS3, Table 1). In fact, the opposite trend was observed in P. clandestina and A. peruvianus, with habitat connectivity explaining a greater proportion of the variance in GD for DS2/DS3 than for DS1 (Table 1).

Factoring out the effects of habitat fragmentation on species and genetic diversity significantly influenced SGDC estimates (LMM, fragmentation habitat control effect: LR = 8.42, df = 1, P < 0.01), decreasing average partial SDGCs relative to average original values (Fig. 4B, Table S5). The decreases in partial SGDCs were more consistent and pronounced with DS1 than with DS2 (Fig. 4B). In A. peruvianus, controlling for habitat fragmentation had strikingly contrasting effects depending on whether or not the outlier loci were included in the analysis (Fig. 4B). As in other species, the partial SGDC decreased with DS1, but increased considerably with DS2, reaching a maximum of 0.7 (Table S5). To confirm that this discrepancy was specifically linked to the three outlier loci detected, we applied a bootstrap procedure to simulate the null distribution of the difference in partial SGDCs between DS1 and DS2 when randomly eliminating three non-outlier loci from DS2. None of the 1,000 bootstrap replications resulted in SGDC differences greater than or equal to the one observed between DS1 and DS2.

Discussion

Confounding effects of outlier loci in SGDC studies

There is increased awareness that neutral molecular markers are not always free of selection pressures; disregarding this fact can bias population genetic inferences [START_REF] Luikart | The power and promise of population genomics: From genotyping to genome typing 13[END_REF], Landguth & Balkenhol 2012). To avoid this problem, [START_REF] Luikart | The power and promise of population genomics: From genotyping to genome typing 13[END_REF] advocated excluding FST outlier loci from genetic analyses when the focus is on neutral processes. In this study, we screened for AFLP outlier loci and investigated their impact on SGDC estimates. We also examined how these loci influence the apparent contribution of habitat characteristics linked to neutral processes (migration rates and rates of stochastic loss of alleles/species) on SGDCs. Our results show that outlier loci led to a marked decrease in GD, which in turn downplayed the strength of SGDCs.

Outlier loci have been suggested to be involved in adaptive processes [START_REF] Hancock | Adaptation to Climate Across the Arabidopsis thaliana Genome[END_REF][START_REF] Manel | Broad-scale adaptive genetic variation in alpine plants is driven by temperature and precipitation[END_REF]). Yet, alternative mechanisms, which may or may not be indirectly associated with selective forces, have also been proposed to explain outlier loci, such as correlations in co-ancestry between subpopulations in hierarchical structures, gene surfing, background selection, and even endogenous genetic barriers [START_REF] Bierne | The coupling hypothesis: why genome scans may fail to map local adaptation genes[END_REF][START_REF] Bierne | Pervasive selection or is it …? Why are Fst outliers sometimes so frequent?[END_REF]. Regardless of the actual mechanisms involved in the atypical of the actual mechanisms involved in the atypical differentiation patterns of the outlier loci, those with markedly high FST demonstrated the most pronounced effects in terms of reductions in GD. This result concurs with previous empirical observations based on microsatellite data (Garcia-Verdugo et al. 2015), and thus suggests that GD might often be underestimated when such loci are included in population genetic analyses.

We found a significant downward effect of outlier loci on SGDCs, indicating that SD correlates more strongly with GD of non-outlier loci than with GD of outlier loci. This pattern is consistent with the expectation that SD is more closely related to neutral GD than to non-neutral GD, due to the parallel influence of neutral processes on both diversity levels. Investigating the effects of filtering out outlier loci from SGDC studies is therefore a potentially useful approach to evidence the contribution of neutral processes. In addition, it may contribute to revealing SGDCs, and explain some of the variation in species-genetic diversity relationships observed in the field. Indeed, the effects of outlier loci are likely to depend on the strength of neutral mechanisms on both diversity components. In high Andean wetlands, and highly fragmented ecosystems in general, neutral evolutionary processes are likely key determinants of biodiversity. As a result, they are expected to generate strong species-genetic diversity covariations [START_REF] Vellend | Connections between species diversity and genetic diversity[END_REF][START_REF] Whitlock | Relationships between adaptive and neutral genetic diversity and ecological structure and functioning: a meta-analysis[END_REF][START_REF] Vellend | Drawing ecological inferences from coincident patterns of population-and community-level biodiversity[END_REF], as were found here, with these SGDCs ranking among the highest values reported to date [START_REF] Vellend | Connections between species diversity and genetic diversity[END_REF][START_REF] Whitlock | Relationships between adaptive and neutral genetic diversity and ecological structure and functioning: a meta-analysis[END_REF][START_REF] Vellend | Drawing ecological inferences from coincident patterns of population-and community-level biodiversity[END_REF]). In such conditions, non-neutral processes, and thus outlier loci, should only have minor confounding effects. The influence of outlier loci on SGDCs is likely to become increasingly problematic as the relative importance of neutral processes in shaping diversity patterns decreases. Extending the present framework to less fragmented ecosystems may potentially uncover further SGDC patterns, thereby advancing current understanding of species-genetic diversity relationships.

The presence of outlier loci also complicated the evaluation of the contribution to SGDCs of habitat characteristics supposedly linked to neutral processes. Without such loci, the partial SGDCs, calculated after filtering out the effects of habitat fragmentation on SD and GD, decreased compared to the corresponding non-partial estimates. This indicates concordant responses of SD and GD at non-outlier loci to these habitat characteristics.

Outlier loci somehow blurred these trends however, as reduction of partial SGDCs was less consistent and less pronounced among species with outlier loci present. In the mayfly A. peruvianus, the SGDC with outlier loci actually increased when habitat effects were factored out (Fig. 4B). This indicates that contrasting effects of habitat characteristics on SD and GD at outlier loci were partly masking the species-genetic diversity relationship in this species. It further shows that GD at outlier loci and neutral GD can correlate differently with habitat features, thus confounding the apparent importance of neutral mechanisms on GD. In fact, for both P. clandestina and A. peruvianus, the habitat features showed stronger correlations with GD at outlier than at non-outlier loci (Table 1). Habitat stability, size, and connectivity, being intrinsic determinants of population demography, are expected to influence all neutral loci equally [START_REF] Luikart | The power and promise of population genomics: From genotyping to genome typing 13[END_REF]. Their stronger association with outlier loci in two species thus suggests that they can also be indirectly associated with selective processes and local adaptation. In our case, high altitude wetland densities decline in parallel with latitude, and both wetland size and connectivity show spatial structure along this gradient (Fig. S2, Supplementary Material). In such circumstances, the standard approach to disentangling the influence of neutral processes on SGDCsi.e., factoring out the effects of habitat features-may be ineffective, since it can simultaneously omit the effects of selective environmental factors. Our results thus illustrate the difficulties in inferring evolutionary processes from partial SGDCs when habitat density is paired with environmental gradient, a situation likely to be common in ecosystems subject to climatic and/or topographic influence. To overcome pitfalls in interpreting the contribution of disturbance regime, habitat size and/or connectivity to SGDCs, it would be advisable to analyze the spatial distribution of these variables and evaluate their correlation with other environmental factors as potential actors of selection.

Species-genetic diversity correlations in high altitude wetlands: patterns and processes

We found strong spatial associations between species richness and AFLP genetic diversity estimates in high altitude wetlands. Consistent with simulation models suggesting stronger species-genetic diversity relationships when GD is measured in more common species [START_REF] Vellend | Species diversity and genetic diversity: Parallel processes and correlated patterns[END_REF], highest SGDC was obtained for C. gayana, the most abundant plant species of high altitude wetlands of Chile's Norte Chico. Overall, our results add to previous meta-analysis evidence that SGDCs are widespread in discrete habitats functioning as islands [START_REF] Vellend | Connections between species diversity and genetic diversity[END_REF][START_REF] Whitlock | Relationships between adaptive and neutral genetic diversity and ecological structure and functioning: a meta-analysis[END_REF][START_REF] Vellend | Drawing ecological inferences from coincident patterns of population-and community-level biodiversity[END_REF]. Furthermore, our results show that such correlations are not limited to component species of the community. For instance, we detected strong SGDCs between plant species richness and GD of the benthic macro-invertebrate taxa, despite the fact that the latter possess very different life-history traits, particularly regarding their dispersal modes.

SGDCs in patchy ecosystems are thought to result from parallel influences of neutral forces governing variation at the genetic and species diversity levels [START_REF] Vellend | Connections between species diversity and genetic diversity[END_REF][START_REF] Whitlock | Relationships between adaptive and neutral genetic diversity and ecological structure and functioning: a meta-analysis[END_REF][START_REF] Vellend | Drawing ecological inferences from coincident patterns of population-and community-level biodiversity[END_REF]. Here, the contribution of such mechanisms is substantiated both by the lowering effect of outlier loci on SGDCs, as well as the apparent common responses of SD and GD at non-outlier loci to habitat features (size, connectivity and stability). The persistence of positive, and overall significant, partial SGDCs after removing all confounding effects (outlier loci and habitat features) suggests, however, that other causal mechanisms might also be involved. These can include both parallel responses to processes unrelated to habitat features, and direct relationships between SD and GD. While our results indicate that potentially selected loci tend to decrease SGDCs overall, they do not rule out the possibility that some selective mechanisms may contribute to species-genetic diversity relationships. For instance, a strong selective agent, e.g. a voracious herbivore or an aggressive exotic plant, acting with increasing intensity upon plant communities located along a spatial gradient might cause a gradual reduction in SD (due to differences in species' abilities to cope with increased predation/competition) together with a reduction of GD in the "surviving" species, where only some tolerant genotypes could remain; this would generate a positive correlation between SD and GD. In our study system, plant SD may affect soil and water characteristics and, as such, may influence local GD by determining the abundance of plant and macroinvertebrate populations. Alternatively, genetic variation within foundation species such as P. clandestina, which hosts other plant species, can affect the composition and diversity of associated communities [START_REF] Whitham | A framework for community and ecosystem genetics: from genes to ecosystems[END_REF]). Nevertheless, this conjecture would imply a significant correlation between GD at non-outlier loci and GD of specific genes encoding ecologically relevant traits influencing plant assemblages.

Further investigation is required to fully unravel the mechanisms driving SGDCs in high Andean wetlands. Future studies will benefit from next-generation sequencing applied to genome scans, which will very soon allow for more reliable estimates of genes under selection [START_REF] Manel | Genomic resources and their influence on the detection of the signal of positive selection in genome scans[END_REF], essential to distinguishing neutral from adaptive markers.

Ideally, field studies could complement these efforts by identifying strong interactions between species in the community, putative selective pressures, and patterns of selection and adaptive evolution. Table 1. Effects of habitat characteristics on genetic diversity and taxa diversity estimated using partial least-square regressions (PLSR). The direction of the effect (positive/negative) of each habitat characteristic on the diversity parameters is reported in parentheses. DS1 refers to the AFLP dataset excluding outlier loci, DS2 to the dataset including both non-outlier and outlier loci andSGDCDS3). SGDCDS2 -SGDCDS1 refers to the difference between SGDCs calculated with the full AFLP dataset including both non-outlier and outlier loci (DS2) and the dataset excluding all the outlier loci (DS1), and SGDCDS3 -SGDCDS1 to the difference between SGDCs calculated with the dataset including nonoutlier and outlier loci with high FST only (DS3) and the dataset excluding all the outlier loci (DS1). B) Difference between raw SGDCs and partial SGDCs, calculated after accounting for habitat characteristics, for each focal species and for datasets without (DS1) and with all the outlier loci (DS2).

Tables

(Figure 2 .

 2 Figure 2. Picture of a high altitude wetland and geographical distribution of the sampling sites along Chile´s Norte Chico. The rectangle depicts the remote-sensing area used for identification and mapping of high Andean wetlands (shown in grey).

Figure 3 .Figure 4 .

 34 Figure3. Average population difference + or -SD between Nei's gene diversity indices estimated from the AFLP datasets including outlier loci (DS2 and DS3) and the dataset free of outliers (DS1). Asterisks indicate significant differences (P < 0.05) in average population genetic diversity estimated with and without outlier loci for each comparison in each species.

Acknowledgements

We thank F. Squeo, G. Arancio, D. Rodriguez, R. Inzulza Ayala, L. Cifuentes, E. Alvarez, C. Urqueta, R. Hereme and A. Troncoso for their help with field work and/or for their technical assistance, and Craig Weideman for revising the English of the manuscript. Our thanks go to P. Jarne and F. Laroche for insightful discussion during the preparation of this manuscript. We also thank four anonymous reviewers for their comments on previous versions of the manuscript. The authors are grateful to La Junta de Vigilancia del Elqui, la Comunidad Agrícola los Huascoaltinos, La Sociedad de Parceleros de Coiron, La Sociedad de Parceleros de San Agustín, La Sociedad de Parceleros Hacienda Illapel, la Hacienda Tulahuén Oriente, la Hacienda El Maiten de Pedregal and la Hacienda El Bosque for giving us access to private wetlands. This research was funded by a grant from the Chilean National Research Agency FONDECYT #1110514 and the exchange research grant ECOS-CONICYT C12B02.

Data accessibility

Raw files of the AFLP genotypes (i.e. band presence and absence) for each of the five species and R scripts will be made available from the Dryad Digital Repository upon acceptance of the manuscript.

Author contributions

ABertin, NG, ABaumel, EG and SM conceived and designed the study. ABertin, NG, JS and RO performed the experiments. ABertin, NG and ABaumel analyzed the data. ABertin, NG, ABaumel and SM wrote the manuscript. All the authors contributed substantially to revisions.


Hélène Guegan

Florence Robert-Gangneux

Christophe Camus

Sorya Belaz

Tony Marchand

Marion Baldeyrou

Jean-Pierre Gangneux
email: jean-pierre.gangneux@univ-rennes1.fr

Improving the diagnosis of invasive aspergillosis by the detection of Aspergillus in broncho-alveolar lavage fluid: comparison of non-culture-based assays

Keywords: aspergillosis, PCR, BAL, azole resistance

Objectives: this study aimed to evaluate new tools to diagnose invasive aspergillosis (IA) directly from bronchoalveolar lavage (BAL) samples.

Methods: all consecutive patients with suspected IA who underwent bronchoscopy with BAL were prospectively included. Mycological culture and ELISA detection of galactomannan (GM) were performed on BAL. Two in-house and two marketed PCR assays were used on BAL DNA extracts to detect Aspergillus species. Susceptibility testing was performed after culture; marketed PCR assays detected mutations in the CYP51A gene associated to resistance.

Results: within 3 years, 1 555 BAL samples were processed, including 413 samples from 387 immunosuppressed patients. IA diagnosis was no-IA, possible, probable or proven IA in 326, 23, 37 and 1 patients, respectively. PCR assays sensitivity for Aspergillus detection ranged from 61% to 74%, below GM (87%), but contrasting with 47% for cultures. Combining PCR to EORTC/MSG criteria increased the sensitivity to 100%. Interestingly, tests performance in non-hematological patients ranged from 60% to 75%, and were higher than in hematological patients, and those with prior exposure to antifungals. All 16 isolates of A. fumigatus were susceptible; PCR did not detect any resistance marker in the 37 A. fumigatus PCR-positive samples.

Conclusion: the molecular detection of Aspergillus directly in BAL samples greatly improved the diagnosis of IA, particularly in non-hematological patients.

INTRODUCTION

Immunocompromised patients are increasingly frequent, either because of hematological malignancies, or of non-hematological conditions such as solid-organ transplantation, chronic pulmonary diseases, long-term corticosteroid therapy, and stay in intensive care unit (ICU). Despite major progresses in their medical management, invasive aspergillosis (IA) remains a major cause of morbidity and mortality [START_REF] Gangneux | An estimation of burden of serious fungal infections in France[END_REF]. The timely and accurate diagnosis of active Aspergillus infection is eagerly required to initiate on time an adequate targeted antifungal therapy [START_REF] Walsh | Treatment of Aspergillosis: Clinical Practice Guidelines of the Infectious Diseases Society of America[END_REF]. However, IA definite diagnosis is still challenging, as clinical and radiological manifestations are most often unspecific. The European Organization for Research and Treatment of Cancer/Mycoses Study Group (EORTC/MSG) recommended using microbiological criteria [START_REF] Ascioglu | Defining opportunistic invasive fungal infections in immunocompromised patients with cancer and hematopoietic stem cell transplants: an international consensus[END_REF][START_REF] De Pauw | Revised Definitions of Invasive Fungal Disease from the European Organization for Research and Treatment of Cancer/Invasive Fungal Infections Cooperative Group and the National Institute of Allergy and Infectious Diseases Mycoses Study Group (EORTC/MSG) Consensus Group[END_REF], such as the histological evidence of tissue infiltration, although rarely obtained, and mycological culture, although positive in only 50% of patients [START_REF] Levy | The value of bronchoalveolar lavage and bronchial washings in the diagnosis of invasive pulmonary aspergillosis. -PubMed -NCBI[END_REF][START_REF] Lortholary | Epidemiological trends in invasive aspergillosis in France: the SAIF network (2005-2007)[END_REF]. This shortcoming has fuelled an interest in non-culture-based diagnostic techniques applied directly in clinical samples, particularly for non-hematological patients, such as antigen detection (galactomannan (GM), beta-1,3-D glucan) by enzyme-linked immuno-sorbent assay (ELISA), or PCRbased assays targeting the identification of Aspergillus spp. or of genes coding for triazole resistance. Numerous PCR-based assays able to detect Aspergillus spp. DNA with a high performance have recently been developed [START_REF] Hope | Laboratory diagnosis of invasive aspergillosis[END_REF][START_REF] Mengoli | Use of PCR for diagnosis of invasive aspergillosis: systematic review and meta-analysis[END_REF][START_REF] Khot | Development and optimization of quantitative PCR for the diagnosis of invasive aspergillosis with bronchoalveolar lavage fluid[END_REF][START_REF] White | Aspergillus PCR: One Step Closer to Standardization[END_REF]. Many centers include now PCR-based algorithms assays in their clinical management. However, data on the relevance of such molecular detection assays in bronchoalveolar (BAL) samples are still poor, preventing their inclusion in disease-defining criteria. Reported sensitivities of PCR assays in BAL have been highly variable, ranging from 36 to 95% [START_REF] Buess | Aspergillus-PCR in bronchoalveolar lavage for detection of invasive pulmonary aspergillosis in immunocompromised patients[END_REF][START_REF] Denning | High-frequency Triazole Resistance Found In Nonculturable Aspergillus fumigatus from Lungs of Patients with Chronic Fungal Disease[END_REF][START_REF] Zhang | Quantitative Real-Time PCR and Platelia Galactomannan Assay for the Diagnosis of Invasive Pulmonary Aspergillosis: Bronchoalveolar Lavage Fluid Performs Better Than Serum in Non-neutropaenic Patients[END_REF][START_REF] Boch | Diagnosis of invasive fungal infections in hematological patients by combined use of galactomannan, 1,3-beta-D-glucan, Aspergillus PCR, multifungal DNA-microarray and Aspergillus azole resistance PCRs in blood and bronchoalveolar lavage samplesresults of a prospective multicenter study[END_REF], mainly because of differences in assay characteristics, population evaluated (hematological versus non-hematological patients), and doubts in IA diagnosis.

In addition, the treatment efficacy might be hampered by resistance to antifungals, as it is spreading in Europe [START_REF] Van Der Linden | Prospective Multicenter International Surveillance of Azole Resistance in Aspergillus fumigatus[END_REF], as in South-America and Asia [START_REF] Lockhart | Azole Resistance in Aspergillus fumigatus Isolates from the ARTEMIS Global Surveillance Study Is Primarily Due to the TR/L98H Mutation in the cyp51A Gene[END_REF][START_REF] Pape | Multiple Fungicide-Driven Alterations in Azole-Resistant Aspergillus fumigatus, Colombia, 2015[END_REF]. In France, the incidence of resistant A. fumigatus isolates has been reported to be still low in the immunocompromised population (about 1% [START_REF] Alanio | Low prevalence of resistance to azoles in Aspergillus fumigatus in a French cohort of patients treated for haematological malignancies[END_REF]), whereas in chronically colonized patients the incidence of azole resistant strains can reach 8-12% as observed in our center and others [START_REF] Morio | High prevalence of triazole resistance in Aspergillus fumigatus, especially mediated by TR/L98H, in a French cohort of patients with cystic fibrosis[END_REF]. Identification of azole resistance in Aspergillus fumigatus is conventionally based on minimal inhibitory concentration (MIC) determination, which necessitates to recover the strain in culture. Triazole-resistant isolates can also be detected by the identification of mutations in the cyp51A gene.(20) Several cyp51A-based PCR assays have been developed for detecting triazole resistance directly from clinical specimens. Most assays are nested in-house PCR assays. [START_REF] Denning | High-frequency Triazole Resistance Found In Nonculturable Aspergillus fumigatus from Lungs of Patients with Chronic Fungal Disease[END_REF][START_REF] Van Der Linden | Rapid Diagnosis of Azole-Resistant Aspergillosis by Direct PCR Using Tissue Specimens[END_REF][START_REF] Spiess | Development of Novel PCR Assays To Detect Azole Resistance-Mediating Mutations of the Aspergillus fumigatus cyp51A Gene in Primary Clinical Samples from Neutropenic Patients[END_REF] Simultaneous detection of IA and identification of resistance mutations sparks an increasing interest in clinical settings for an earlier adaptation of antifungal therapy; of note A. fumigatus resistant strains have been related to a higher mortality rate, due to a delayed initiation of adequate treatment [START_REF] Howard | Frequency and Evolution of Azole Resistance in Aspergillus fumigatus Associated with Treatment Failure[END_REF] . In this context, commercial kits targeting both A. fumigatus and cyp51A genes have been recently marketed, but their evaluation on clinical samples IA population is still scarce [START_REF] Chong | Validation of a New Aspergillus Real-Time PCR Assay for Direct Detection of Aspergillus and Azole Resistance of Aspergillus fumigatus on Bronchoalveolar Lavage Fluid[END_REF][START_REF] Montesinos | Culture based methods and molecular tools for azole-resistant Aspergillus fumigatus detection in a Belgian University hospital[END_REF].

Our study aimed to assess the contribution of two in-house Aspergillus real-time PCR assays and of two commercial resistance multiplex assays (AsperGenius ® , PathoNostics, Maastricht, the Netherlands; and MycoGenie ® , AdemTech, Pessac, France) for IA diagnosis in BAL fluid samples. Additionally, triazole resistance screening by PCR on clinical samples was compared to phenotypic and genotypic characteristics of A. fumigatus isolates recovered with cultures.

MATERIALS AND METHODS

Patients and study design

For this prospective study, all consecutive patients with suspected IA who underwent standard bronchoscopy with BAL at Rennes University Hospital (France) were included. Redundant samples for a unique clinical episode were excluded. Patients were classified as having proven, probable, possible IA, or no IA, according to the revised EORTC/MSG case definitions [START_REF] Walsh | Treatment of Aspergillosis: Clinical Practice Guidelines of the Infectious Diseases Society of America[END_REF].

BAL fluid samples processing

BAL fluid specimens were divided into 3 aliquots to perform culture, GM detection assay and PCR assays, respectively. For culture, 5-10 mL were centrifuged and the resuspended pellet (about 200µL) was directly inoculated on 2 conventional fungal media incubated at 30°C and 37°C for 8 days. Mold isolates from cultures were identified by macroscopic and microscopic examinations. For PCR assays, 1 mL of plain BAL was centrifuged and DNA was extracted from the pellet, using the QIAamp ® DNA minikit (Qiagen) according to manufacturer's recommendations. For ELISA, the aliquot was 390 µL, according to manufacturer recommendations.

Susceptibility testing

Itraconazole (ITC) and voriconazole (VRC) susceptibility of A. fumigatus strains isolated from IA samples was determined using Etest ® strips according to the manufacturer's instructions (bioMérieux, France), after 48h of incubation at 30°C. Azole resistance was assessed for MIC >2mg/mL for ITC and VRC according to EUCAST breakpoints v.8.0.

In-house Aspergillus PCR assays

BAL DNA extracts were tested using two real-time "in-house" PCR assays. When discrepant results with other tests (culture or GM) were observed, DNA was retested with both PCR assays.

The first PCR assay ("Af-mito") amplified a 196-bp sequence of A. fumigatus mitochondrial gene, as previously described [START_REF] Bretagne | Detection of Aspergillus species DNA in bronchoalveolar lavage samples by competitive PCR[END_REF]. The second PCR assay ("28S") targeted a 67-bp sequence of the A. fumigatus 28S rRNA gene, using primers and probe as previously published [START_REF] Challier | Development of a Serum-Based Taqman Real-Time PCR Assay for Diagnosis of Invasive Aspergillosis[END_REF]. Each amplification was performed in a 25µL final volume containing 1X TaqMan ® Universal PCR MasterMix, 0.5µM of each primer, 0.2 µM of probe and 5µL of DNA sample. Amplification used the following thermal conditions : 2 min at 50°C, 10 min at 95°C and 45 cycles of 15 sec at 95°C and 1 min at 60°C, on a StepOne Plus ® instrument (Applied Biosystems).

Commercial Aspergillus PCR assays

BAL fluid specimens from IA cases were tested by two commercial multiplex real-time PCR assays, that simultaneously detect Aspergillus spp. and the most relevant mutations in the cyp51A gene.

MycoGenie ® (AdemTech, Pessac, France) is a quadruplex real-time PCR assay which targets A. fumigatus (28SrRNA gene), TR34, L98H, and an internal control.

AsperGenius ® assay (PathoNostics, Maastricht, the Netherlands) is composed of 2 different real-time quadriplex amplification mixtures, targeting Aspergillus species and resistance mutations. The species multiplex assay targets the 28S rRNA multicopy gene, allowing the specific detection of A. fumigatus complex (Af), Aspergillus terreus and Aspergillus spp. (Asp sp). The resistance multiplex assay targets the single-copy Cyp51A gene of A. fumigatus, and can detect the TR34, L98H, Y121F and T289A regions. The distinction between wild-type and mutant A. fumigatus strains was performed by melting curve analysis. Amplification was conducted for 45 cycles on a LightCycler ® 480 instrument (Roche), according to manufacturer's instructions. The horizontal threshold was fixed above the background noise and a positive result was defined by a signal detection with a Ct value <45 cycles.

Galactomannan (GM) Platelia Aspergillus assay

The sandwich ELISA for BAL GM detection (Platelia Aspergillus ® ; Bio-Rad, Marnes-la-Coquette, France) was performed according to the manufacturer's recommendations, on a 390µL aliquot of BAL sample. For the classification of patients, the BAL was considered as positive when optical density index (ODI) was≥ 0.8, if concomitant serum GM was positive (ODI ≥0.5), and ≥1.0 when serum GM was negative or unavailable.

Beta-tubulin sequencing

Identification of Aspergillus species other than fumigatus was confirmed by beta-tubulin gene typing, using primers bt2a and bt2b [START_REF] Glass | Development of primer sets designed for use with the PCR to amplify conserved genes from filamentous ascomycetes[END_REF]. Amplification reaction and subsequent sequencing were performed as previously described [START_REF] Comacle | Contribution of molecular tools for the diagnosis and epidemiology of fungal chronic rhinosinusitis[END_REF]. Bidirectional sequences were analyzed using Seqscape ® software and submitted to GenBank public database by using the BLAST Search program for species identification.

ITS1-ITS2 sequencing

Identification of Scedosporium from BAL sample was confirmed by ITS1-ITS2 typing, using the primers ITS1 and ITS4. The protocol was similar as described above [START_REF] Glass | Development of primer sets designed for use with the PCR to amplify conserved genes from filamentous ascomycetes[END_REF][START_REF] Comacle | Contribution of molecular tools for the diagnosis and epidemiology of fungal chronic rhinosinusitis[END_REF].

Statistical methods.

Data analysis was performed using GraphPad PRISM ® v. 5.02 software. Categorial variables were compared using the χ 2 test or Fisher's exact test. A p-value of 0.05 was considered to be statistically significant.

RESULTS

Patient characteristics

As depicted in Figure 1, within 3 years (from January 2012 to December 2014), a total of 1,555 BAL, fluid samples (1,336 patients) were processed, including 413 samples collected from immunocompromised patients with clinical symptoms or radiological signs compatible with invasive fungal infection. Non-redundant BAL fluid specimens of 387 patients at high risk for IA were analyzed in this study.

According to the EORTC-MSG criteria, 61 patients were diagnosed with IA (1 proven, 37 probable, 23 possible cases), whereas the diagnosis of IA was excluded for 326 patients. The distribution of the patients according to their infection status and underlying conditions is presented in Table 1. Twenty-seven out of 61 patients (44%) were hospitalized in intensive care unit and 22 (36%) were in hematology unit.

Diagnostic performance of Aspergillus fumigatus PCR assays

The sensitivity of the 28S PCR assay was higher than that of Af-mito PCR (71.1% versus 60.5%), whereas its specificity for IA was 92.8% (324/349), i.e., lower than that of Af-mito PCR (95.4%, 333/349)(Table 2). It was interesting to note that in the no-IA group, 10 out of the 16 28S PCR positive BAL were from patients with chronic pulmonary disease, as aspergilloma or CNPA in 4 cases. These positive assays are consistent with Aspergillus colonization and could not reasonably be considered as false positive results. The combination of both PCRs led to a slight increase of sensitivity (73.7%) with similar specificity (92.3%). By contrast, mycological cultures allowed to diagnose only 18/38 proven or probable IA (47.4%).

In BAL from patients with IA, commercial multiplex assays showed roughly similar results than in-house PCRs.

MycoGenie ® had the highest sensitivity (73.7%). The AsperGenius ® assay had a sensitivity of 60.5% and 68.4% for A. fumigatus and Aspergillus spp. detection, respectively.

Interestingly, combinations of any PCR assay to current EORTC/MSG criteria (GM detection and culture in BAL) led to an increase of sensitivity (from 92.1% to 97.4 or 100% depending the PCR assay), without significant decrease of specificity (Table 2).

Of the twenty-two samples grown positive for A. fumigatus, Af-mito PCR was positive in 19/22 (86%) and 28S PCR in 20/22 (91%) (Table 3). When considering only IA cases, Af-mito and 28S PCRs were positive in 15/16 BAL with positive culture.

Of four BAL from which an Aspergillus species other than A. fumigatus was isolated, two yielded a positive result with the 28S PCR assay. The cultures were identified as A. flavus and A. ochraceus, respectively, by microscopic examination and beta-tubulin gene sequencing. The sample cultured positive with A. flavus also yielded a positive result with the Mycogenie ® assay, which targets also the 28S gene. By contrast, the AsperGenius ® assay was negative for A. fumigatus, but correctly positive for Aspergillus spp. in one case. All PCR assays were positive for a sample grown with S. apiospermum (identification confirmed by ITS1-ITS2 typing from the clinical specimen), suggesting that none of them is 100% specific for A. fumigatus.

Resistance screening using commercial PCR assays

A. fumigatus susceptibility to azole was investigated in the 61 samples from IA patients. Mycogenie ® PCR assay did not detect any TR34 or L98H mutation in the 37 positive samples for A. fumigatus target.

No resistance marker was detected with Aspergenius ® assay either. However, we noticed that TR34, L98H, T289A and Y121F regions were successfully amplified in only 16 of the 29 samples with positive A. fumigatus detection, regardless of wild-type or mutated allele (Table 4). The mean Ct values for A. fumigatus detection in samples for which cyp51A amplification failed tended to be higher than that with successful cyp51A detection (34.1±4.6 versus 35.8±1.9, p=0.203).

Finally, in vitro susceptibility testing could be performed on 11 of the 16 A. fumigatus isolates. Only susceptible phenotypes were observed, with ITC and VRC MIC ranging from 0.5 to 1.5 mg/l and 0.032 to 0.5 mg/l, respectively. Susceptibility of non fumigatus species cultured from IA BAL samples was also evaluated, yielding to low MIC for A. flavus (ITC: 0.5 mg/L, and VRC: 0.38 mg/L). By contrast, A. calidoustus had a poor susceptibility to triazole antifungals, with ITC and VCZ MICs at 3 mg/L and 16 mg/L, respectively.

Performance of tests according to the clinical background

As displayed in Table 5, sensitivity of culture combined to BAL GM and Af-mito PCR was significantly lower in patients with hematological malignancy (n=41) compared to the other patients (n=20), with estimated values decreasing from 85% to 43.9%, and 70% to 36.6%, respectively (p<0.01, p<0.05, respectively). Indeed, the sensitivity of Af-mito PCR was significantly reduced in neutropenic group (30% versus 64.5%, p<0.05) compared to non-neutropenic patients. Similarly, a previous exposure to mold-active antifungal agents (as prophylactic or curative treatment) lowered the sensitivity of all tests for Aspergillus detection (Table 6), at a statistically significant level for Mycogenie ® PCR (40.0% versus 70.7%, p=0.03) and BAL GM assay combined to culture (30.0% versus 70.7%, p=0.005).

DISCUSSION

In this 3-year prospective study, the diagnosis of IA was considered as proven or probable for 38 of 387 immunocompromised patients (9.8%) based on conventional techniques. When PCR was tested directly in BAL samples, its sensitivity was higher than that of culture, but lower than that of GM detection. Mycogenie ® PCR assay had a good sensitivity for IA diagnosis (73.7%), followed by Aspergenius ® PCR (68.4%). Of the two inhouse PCRs, the 28S PCR had the highest sensitivity, compared to Af-mito PCR (71.1% versus 60.5%). This difference was expected, based on A. fumigatus genome structure. Indeed while the copy number of the mitochondrial genome relative to the nuclear genome was estimated to be 12 [START_REF] Nierman | Genomic sequence of the pathogenic and allergenic filamentous fungus Aspergillus fumigatus[END_REF], the ribosomal gene was shown to be repeated in 38 to 91 copies, varying according to the strain [START_REF] Herrera | Strain-Dependent Variation in 18S Ribosomal DNA Copy Numbers in Aspergillus fumigatus[END_REF][START_REF] Alanio | Variation in copy number of the 28S rDNA of Aspergillus fumigatus measured by droplet digital PCR and analog quantitative real-time PCR[END_REF]. A previous study on AsperGenius ® assay in BAL from hematological population, reported a higher sensitivity (84%) [START_REF] Chong | Validation of a New Aspergillus Real-Time PCR Assay for Direct Detection of Aspergillus and Azole Resistance of Aspergillus fumigatus on Bronchoalveolar Lavage Fluid[END_REF] and another assay (MycAssay ® Aspergillus) yielded sensitivity until 95% [START_REF] Denning | High-frequency Triazole Resistance Found In Nonculturable Aspergillus fumigatus from Lungs of Patients with Chronic Fungal Disease[END_REF][START_REF] Torelli | Diagnosis of Invasive Aspergillosis by a Commercial Real-Time PCR Assay for Aspergillus DNA in Bronchoalveolar Lavage Fluid Samples from High-Risk Patients Compared to a Galactomannan Enzyme Immunoassay[END_REF]; it could possibly be related to a slightly different use of antifungals at this time.

Currently, PCR in BAL is not included in the criteria for classification of IA. Although BAL is frequently sampled in ICU patients, it is rare in hematological patients. In oncological-hematological patients, the increasing use of anti-Aspergillus chemoprophylaxis decreases the incidence but also the sensitivity of all diagnostic tests, as shown here. In our study, patients with hematological malignancy and/or allogeneic hematopoietic transplantation were more frequent in the group with possible IA; of note, the rate of patients receiving anti-Aspergillus prophylaxis was higher in this group. Conversely, solid organ transplantation was more frequent among patients with proven/probable IA (p<0.05). This work clearly demonstrates a superior value of PCR in BAL for non-hematological immunosuppressed patients. The value of Aspergillus detection in endotracheal aspirates of ICU patients was already exemplified by Blot et al [START_REF] Blot | A clinical algorithm to diagnose invasive pulmonary aspergillosis in critically ill patients. -PubMed -NCBI[END_REF] who proposed a specific algorithm. Here, we observed a high sensitivity with a good specificity for BAL biomarkers such as GM and PCR in non-hematological patients, particularly if combined. Combining PCR to current EORTC criteria increased sensitivity from 92% to 100%. Such increase in sensitivity confirm previous results [START_REF] Eigl | Galactomannan testing and Aspergillus PCR in same-day bronchoalveolar lavage and blood samples for diagnosis of invasive aspergillosis[END_REF][START_REF] Hoenigl | Performance of Galactomannan, Beta-D-Glucan, Aspergillus Lateral-Flow Device, Conventional Culture, and PCR Tests with Bronchoalveolar Lavage Fluid for Diagnosis of Invasive Pulmonary Aspergillosis[END_REF].

In hematological patients, despite a moderate sensitivity of each test performed alone, we showed that combination of PCR with EORTC criteria was of particular interest increasing the weak sensitivity of EORTC criteria from 44% to 68%, and particularly an increase from 30% to 50% and from 27% to 54% in patients under antifungals treatment (curative, empirical or prophylaxis) or specifically antifungal prophylaxis, respectively. These findings could lead to the inclusion of PCR among EORTC-MSG IA criteria [START_REF] White | Aspergillus Polymerase Chain Reaction: Systematic Review of Evidence for Clinical Use in Comparison With Antigen Testing[END_REF]. In our cohort, taking the in-house 28S PCR for the definition of IA would have upgraded 3 episodes from "possible cases" to "probable cases". Indeed, including an earlier and more sensitive method to detect Aspergillus in tissues may facilitate the diagnosis of IA, and, in turn, improve its management and clinical outcome. In addition, the high predictive value of simultaneous detection of fungal DNA and of GM antigen could lead to a reevaluation of empirical antifungal therapy for a better adequacy and a limitation of their use [START_REF] Hasseine | High negative predictive value diagnostic strategies for the reevaluation of early antifungal treatment: A multicenter prospective trial in patients at risk for invasive fungal infections[END_REF].

The specificity was excellent with all PCR assays (>92%). It should be noted that among the group of excluded IA, several patients had a chronic pulmonary aspergillosis or impaired pulmonary functions, which are known to increase the frequency of Aspergillus colonization. Indeed, of the 16 BAL with positive 28S PCR, we recorded 10 patients with aspergilloma, semi-invasive aspergillosis, or other etiology of chronic respiratory failure. Apart from PCR results, 6 of these patients had a typical imaging (aspergilloma) or a strongly positive detection of Aspergillus antibodies and precipitins, leading to a triazole therapy.

Although 28S PCR was described as specific for A. fumigatus, we observed cross-reaction with A. flavus and A. ochraceus, two species already associated with IA in previous studies [START_REF] Lortholary | Epidemiological trends in invasive aspergillosis in France: the SAIF network (2005-2007)[END_REF][START_REF] Wierzbicka | Invasive pulmonary aspergillosis caused by Aspergillus ochraceus[END_REF][START_REF] Steinbach | Clinical epidemiology of 960 patients with invasive aspergillosis from the PATH Alliance registry[END_REF]. Considering this finding, the combination of both techniques appears valuable in case of strongly suspected aspergillosis with negative culture and GM detection. In this setting, as Aspergenius ® assay targets both Aspergillus sp and A. fumigatus, it could have been considered as a relevant tool for IA diagnosis. However, the poor sensitivity observed here is a serious limitation to its use.

Apart ensuring PCR standardization, MycoGenie ® and AsperGenius ® kits are able to simultaneously detect hotspot cyp51A mutations associated with A. fumigatus triazole resistance directly from clinical samples. We did not detect any cyp51A mutation in our positive samples, and it was correlated with in vitro susceptibility testing results, when available. This finding is consistent with the apparent low azole resistance prevalence in immunocompromised patients in France (around 1%) [START_REF] Alanio | Low prevalence of resistance to azoles in Aspergillus fumigatus in a French cohort of patients treated for haematological malignancies[END_REF]. However, we underlined a substantial poor sensitivity of cyp51A gene detection, mainly due to the presence of a single copy cyp51A gene in each fungal cell. This limiting factor has been previously raised in several reports using in-house molecular typing methods [START_REF] Spiess | Development of Novel PCR Assays To Detect Azole Resistance-Mediating Mutations of the Aspergillus fumigatus cyp51A Gene in Primary Clinical Samples from Neutropenic Patients[END_REF][START_REF] Verweij | Azole Resistance in Aspergillus fumigatus: Can we retain the Clinical Use of Mold-Active Antifungal Azoles ?[END_REF]. This is especially a concern in samples from immunocompromised patients with a low fungal burden, moreover when an effective antifungal therapy has been initiated before sampling.

Here, we could not strictly rule out any resistant fungi, since these molecular screening focus only on a few hotspot single polynucleotide polymorphisms, and because several cyp51A-independent mechanisms have been reported to contribute to azole resistance [START_REF] Camps | Discovery of a hapE Mutation That Causes Azole Resistance in Aspergillus fumigatus through Whole Genome Sequencing and Sexual Crossing[END_REF]. In addition, rare cryptic species in Aspergillus genus responsible for IA are increasingly reported. Indeed, attention should be paid to these fungi, with treatment failure likely due to their low intrinsic susceptibility to some antifungals, especially triazole therapy [START_REF] Howard | Multi-Resistant Aspergillosis due to Cryptic Species[END_REF][START_REF] Alastruey-Izquierdo | Population-Based Survey of Filamentous Fungi and Antifungal Resistance in Spain (FILPOP Study)[END_REF][START_REF] Egli | Emergence of Aspergillus calidoustus Infection in the Era of Posttransplantation Azole Prophylaxis[END_REF].

This study is the first study that broadly evaluated the value of several tools for diagnosing IA directly in BAL fluid of immunosuppressed patients, including non-hematological as hematological patients, and clearly demonstrated the value of combining tests, particularly PCR for the detection of Aspergillus DNA and ELISA for GM detection. However it has some limitations. First, this is a mono-centric study, which need to be conducted again in other centers. Second, we compared marketed versus in-house PCR assays; however, we provided all requested information for allowing to set them up elsewhere. Third, the sample dilution when working on BAL is an inevitable debate. However, while it is hard to circumvent it, this is not a debate for GM determination in BAL that is now include in EORTC/MSG criteria.

Allogeneic HSC transplantation a 3 (7.9) 11 (47.

  Consequently to efficient strategies of antifungal prophylaxis in hematology, ICUs now receive very high risk patients for aspergillosis and need specific recommendations for IA diagnosis. Overall, this study demonstrated the worth of molecular detection of Aspergillus in ICU patients BAL for the diagnosis of IA, particularly in nonhematological patients and those without prior exposure to antifungals. Such major information associated to other published data in non-hematological patients should be taken into account, independently to valuable hesitations and debates in the specific context of hematology. Checking the susceptibility profile by in vitro testing from cultures remains essential for triazole resistance monitoring.

  stem cell. a 14/41 patients with hematological malignancies b Liver n=5 ; kidney n=1 ; heart n=1 c Corticosteroid or rejection prophylaxis or cytotoxic chemotherapy d Mold-active antifungal agent administered since at least 7 days before BAL performing e 2 patients lost to follow-up Table 2. Performances of PCR assays in broncho-alveolar lavage samples compared to galactomannan detection and culture for the diagnosis of invasive aspergillosis (N=387)

a

  BAL GM + BAL culture + serum GM b Aspergilloma (n=2), chronic necrotising pulmonary aspergillosis (CNPA) (n=2), pulmonary tumor (n=2), bronchiectasis (n=1), chronic obstructive pulmonary disease (COPD) (n=1), normal pulmonary functions (n=2) c Aspergilloma (n=2), CNPA (n=2), pulmonary tumor (n=2), COPD (n=2), bronchiectasis (n=1), severe asthma (n=1), normal pulmonary functions (n=6) na : not applicable; Af-mito PCR: targeting a sequence of an A. fumigatus mitochondrial gene; 28S PCR: targeting a sequence of a 28S rRNA gene of A. fumigatus; BAL GM ELISA: sandwich enzyme-linked immunosorbent assay (ELISA) for the detection of galactomannan (GM) in broncho-alveolar fluid (BAL); AsperGenius ® Af PCR: sub-assay of the AsperGenius ® multiplex assay detecting 28S rRNA of the A. fumigatus complex; AsperGenius ® Asp sp: sub-assay of the AsperGenius ® multiplex assay detecting 28S rRNA of A. terreus and Aspergillus spp.; MycoGenie ® : quadruplex real-time PCR assay which targets A. fumigatus (28SrRNA gene), TR34, L98H, and an internal control to monitor sample inhibition. A result was considered as positive when a signal was detected at Ct<45.

aTable 6 .FIGURESFig. 1 :

 61 FIGURES Fig.1 : Flow-chart of the study population

TABLES Table 1 . Characteristics of patients with invasive aspergillosis (N=61)

 TABLES1

		EORTC/MSG classification
	Clinical features	Proven/probable (N=38)	Possible (N=23)
		n (%)	n (%)
	Age (years, [range])	62 [37-81]	52 [11-86]
	Gender; Sex ratio	30/8; 3.75	15/8 ; 1.8
	Hospitalization ward
	Intensive Care Unit	20 (52.6)	7 (30.4)
	Haematology unit	11 (28.9)	11 (47.8)
	Infectious disease unit	2 (5.3)	2 (8.7)
	Others	5 (13.2)	3 (13.0)
	Underlying condition
	Hematological malignancies	20 (52.6)	21 (91.3)
	Acute myeloid leukemia	6 (30.0)	10 (47.6)
	Acute lymphoid leukemia	0	1 (4.8)
	Lymphoma	6 (

Table 3 . PCR results according to broncho-alveolar (BAL) sample culture results

 3 proven or probable IA with positive culture and 4/6 excluded IA with positive culture h 15/16 proven or probable IA with positive culture and 5/6 excluded IA with positive culture Af-mito PCR: targeting a sequence of an A. fumigatus mitochondrial gene; 28S PCR: targeting a sequence of a 28S rRNA gene of A. fumigatus; MycoGenie ® : quadruplex real-time PCR assay which targets A. fumigatus (28SrRNA gene), TR34, L98H, and an internal control to monitor sample inhibition; AsperGenius ® Af PCR: subassay of the AsperGenius ® multiplex assay detecting 28S rRNA of the A. fumigatus complex; AsperGenius ® Asp sp: sub-assay of the AsperGenius ® multiplex assay detecting 28S rRNA of A. terreus and Aspergillus spp..

	Culture results		Positive Aspergillus PCR assay results
		Af mito PCR	28S PCR	Mycogenie ®	Aspergenius ®	Aspergenius ®
		n/N (%)	n/N (%)	PCR a n/N(%)	Af PCR a	Asp sp PCR a
					n/N(%)	n/N(%)
	A. fumigatus	19/22 (86.4) g	20/22 (90.9) h	15/16 (93.8)	15/16 (93.8)	15/16 (93.8)
	Non fumigatus	0/4 (0)	2 c /4 (50.0)	1 d /2 (50.0)	0/2 (0)	1 d /2 (50.0)
	Aspergillus species b
	Others moulds e	1 f /6 (16.7)	1 f /6 (16.7)	1 f /2 (50.0)	1 f /2 (50.0)	1 f /2 (50.0)
	Negative	19/355 (5.3)	29/355 (8.2)	20/41 (48.8)	13/41 (31.7)	15/41 (36.6)

a Tested only on BAL samples from IA cases (n=61) b A. flavus (n=1), A. glaucus (n=1), A. ochraceus (n=1), A. calidoustus (n=1). A. flavus and A. calidoustus were grown from IA BAL samples. c A.flavus, A. ochraceus d A. flavus e Penicillium sp. (n=4), Scedosporium sp. (n=1), S. apiospermum (n=1) f S. apiospermum g 15/16

Table 4 . Detection of cyp51A mutation using Mycogenie ® and AsperGenius ® assays

 4 Tm value of melting peaks in "mutant" range, according to manufacturer's instructions MycoGenie ® : quadruplex real-time PCR assay which targets A. fumigatus (28SrRNA gene), TR34, L98H, and an internal control to monitor sample inhibition; AsperGenius ® : multiplex assay detecting 28S rRNA of the A. fumigatus complex, A. terreus and Aspergillus spp..

	PCR assay		Number of positive samples (%)
			A.fumigatus PCR	TR34	L98H	Y121F	T289A
	AsperGenius ®	Target amplification	29/61 (47.5) 18/29 (62.1) 18/29 (62.1) 19/29 (65.5) 18/29 (62.1)
		Detection mutated allele a of	na	0/18	0/18	0/19	0/18
	Mycogenie ® Detection of mutated allele	37/61	0/37	0/37	na	na
	na : not applicable

a

Table 5 . Diagnostic performances of PCR assays, galactomannan (GM) detection and Culture of broncho- alveolar lavage (BAL) samples, according to the underlying disease in patients with IA

 5

		Hematological	No hematological		Neutropenia	No
	Method	disease	disease	p-value	<0.5G/L	neutropenia	p-value
		n=41	n=20		n=30	n=31
		Sensitivity	Sensitivity		Sensitivity	Sensitivity
		n(%)	n(%)		n(%)	n(%)
	Alone
	Af mito PCR	15 (36.6)	14 (70.0)	0.028*	9 (30.0)	20 (64.5)	0.010*
	28S PCR	21 (51.2)	15 (75.0)	0.100	15 (50.0)	21 (67.7)	0.198
	MycoGenie ® PCR	22 (53.7)	15 (75.0)	0.163	16 (53.3)	21 (67.7)	0.300
	AsperGenius ® Af PCR	17 (41.5)	12 (60.0)	0.275	12 (40.0)	17 (54.8)	0.309
	AsperGenius ® Asp sp PCR	18 (43.9)	14 (70.0)	0.063	14 (46.7)	18 (58.1)	0.446
	Combined
	Af mito PCR and EORTC/MSG criteria a	24 (58.5)	20 (100)	<0.001***	19 (63.3)	25 (80.6)	0.161
	28S PCR and EORTC/MSG criteria a	27 (65.9)	20 (100)	0.002**	21 (70.0)	26 (83.9)	0.235
	Mycogenie ® PCR and EORTC/MSG criteria a	27 (65.9)	20 (100)	0.002**	21 (70.0)	26 (83.9)	0.235
	AsperGenius ® Af PCR and EORTC/MSG criteria a	24 (58.5)	19 (95.0)	0.003**	20 (66.7)	23 (74.2)	0.582
	AsperGenius ® Asp PCR and EORTC/MSG criteria a 24 (58.5)	19 (95.0)	0.003**	20 (66.7)	23 (74.2)	0.582
	EORTC/MSG criteria a	18 (43.9)	17 (85.0)	0.003**	16 (53.3)	19 (61.3)	0.609

*p<0.05 ; **p<0.01 ; ***p<0.001

Acknowledgements: the authors thank Celine Feger, M.D., (EMIBiotech) for her editorial support.