ABSTRACT

Recent studies suggest that immune-modulating single-nucleotide polymorphisms (SNPs) influence the risk of developing cancer-related infections. Here, we evaluated whether 36 SNPs within 14 immune-related genes are associated with the risk of invasive aspergillosis (IA) and whether genotyping of these variants might improve disease risk prediction. We conducted a case-control association study of 781 immunocompromised patients, 149 of whom were diagnosed with IA. Association analysis showed that the IL4R rs2107356 and IL8 rs2227307 SNPs (using dbSNP numbering) were associated with an increased risk of IA (IL4R rs2107356 odds ratio [OR], 1.92; 95% confidence interval [CI], 1.20 to 3.09; IL8 rs2227307 OR, 1.73; 95% CI, 1.06 to 2.81), whereas the IL12B rs3212227 and IFNγrs2069705 variants were significantly associated with a decreased risk of developing the infection (IL12B rs3212227 OR, 0.60; 95% CI, 0.38 to 0.96; IFNγrs2069705 OR, 0.63; 95% CI, 0.41 to 0.97). An allogeneic hematopoietic stem cell transplantation (allo-HSCT)-stratified analysis revealed that the effect observed for the IL4R rs2107356 and IFNγrs2069705 SNPs was stronger in allo-HSCT (IL4R rs2107356 OR, 5.63; 95% CI, 1.20 to 3.09; IFNγrs2069705 OR, 0.24; 95% CI, 0.10 to 0.59) than in non-HSCT patients, suggesting that the presence of these SNPs renders patients more vulnerable to infection, especially under severe and prolonged immunosuppressive conditions. Importantly, in vitro studies revealed that carriers of the IFNγrs2069705C allele showed a significantly increased macrophage-mediated neutralization of fungal conidia (P = 0.0003) and, under stimulation conditions, produced higher levels of gamma interferon (IFNγ) mRNA (P = 0.049) and IFNγ and tumor necrosis factor alpha (TNF-α) cytokines (P value for 96 h of treatment with lipopolysaccharide [P LPS-96 h], 0.057; P value for 96 h of treatment with phytohemagglutinin [P PHA-96 h], 0.036; P LPS+PHA-96 h = 0.030; P PHA-72 h = 0.045; P LPS+PHA-72 h = 0.018; P LPS-96 h = 0.058; P LPS+PHA-96 h = 0.0058). Finally, we also observed that the addition of SNPs significantly associated with IA to a model including clinical variables led to a substantial improvement in the discriminatory ability to predict disease (area under the concentration-time curve [AUC] of 0.659 versus AUC of 0.564; P −2 log likehood ratio test = 5.2 · 10−4 and P 50.000 permutation test = 9.34 · 10−5). These findings suggest that the IFNγrs2069705 SNP influences the risk of IA and that predictive models built with IFNγ, IL8, IL12p70, and VEGFA variants can used to predict disease risk and to implement risk-adapted prophylaxis or diagnostic strategies.

INTRODUCTION

Invasive aspergillosis (IA) is a life-threatening infection caused by Aspergillus spp. that affects acute myelogenous leukemia (AML) and allogeneic hematopoietic stem cell transplantation (allo-HSCT) patients (13). Despite recent improvements in the prophylaxis and treatment of IA, its incidence and attributable mortality rates remain unacceptably high even among those individuals who lack established risk factors (4, 5).
The initial immune response against fungal pathogens such as Aspergillus fumigatus, the principal pathogenic species, relies mainly on phagocytes and on endothelial and epithelial cells that recognize this fungal pathogen through pattern recognition receptors (PRRs), leading to phagocytosis, antigen presentation, and the production of specific cytokines and chemokines (6, 7). There are different families of PRRs, including C-type lectin receptors (CLRs), Toll-like receptors (TLRs), RIG-I-like receptors (RLRs), and NOD-like receptors (NLRs), that, in response to Aspergillus pathogen-associated molecular patterns (PAMPs), activate Th1-, Th2-, and Th17-related signaling cascades on phagocytes and nonprofessional immune cells (812). These intracellular molecular pathways culminate in the production of both proinflammatory (1318) and anti-inflammatory cytokines (19, 20) and certain chemokines and their receptors (2123), as well as in the release of certain proangiogenic factors, such as VEGFA and bFGF (17, 24), which are also key determinants in the immune response against Aspergillus spp.
Although both innate and adaptive immune responses against A. fumigatus have been extensively characterized (25, 26), it remains unclear why some immunocompromised subjects develop invasive or disseminated fungal infections while others under similar clinical conditions do not. The remarkable genetic variation of immune genes suggests that the presence of specific genetic variants in these genes influences their biological functions and, consequently, affect the risk of developing invasive fungal infections, such as IA. In support of this hypothesis, recent studies on genetic susceptibility have successfully identified several genetic variants on PRR genes (DC-SIGN, Dectin-1, TLRs, PTX3, and MBL) (2740), cytokines (IL1 gene cluster, IL10, IL12, and IFNγ) (32, 4144), chemokines (CXCL10) (45), and immune receptors (TNFR1 and TNFR2) (46, 47) as factors influencing the risk of developing IA. With this background, the purpose of this study was to comprehensively assess whether the presence of single-nucleotide polymorphisms (SNPs) within 14 immune-modulating genes (IL4, IL4R, IL8, IL8RA, IL8RB, IL10, IL12A, IL12B, IL13, IFNγ, IFNγR2, CCR5, MIF, and VEGF) influence the risk of developing IA. We also decided to evaluate the functional role of key variants in modulating immune responses and whether selected polymorphisms could be used to predict the disease risk.

MATERIALS AND METHODS

Study design and study population.

We analyzed whether 36 SNPs within 14 immune-modulating genes were associated with IA. SNP selection was based on three criteria: (i) SNPs within immunoregulatory genes that may affect immune responses, (ii) SNPs having laboratory evidence of a biological function, and/or (iii) SNPs previously reported as being associated with infectious diseases (Table 1). SNPs were genotyped using KASPar assays (LGC Genomics KBioscience, London, United Kingdom) as previously described in detail (28). Patients were included either if they were undergoing allo-HSCT or if they had been diagnosed with acute myeloid leukemia (AML) or acute lymphoid leukemia (ALL) and were receiving intensive remission-induction chemotherapy. A total of 593 patients were recruited between February 2010 and March 2014 through the aspBIOmics Consortium (www.aspbiomics.eu) and through two Spanish medical institutions (University Hospital of Salamanca and Clinic University Hospital of Valencia) and a Spanish multicenter clinical trial (PCRAGA; EU clinical trial number 2010-019406-17) (48). Based on microbiological and clinical data, 113 patients were diagnosed with proven or probable IA according to the revised EORTC/MSG criteria (49). In order to further confirm significant associations identified in our population, we extended the analysis to a second patient group consisting of 188 high-risk patients (36 IA patients and 152 without IA) (Table 2) recruited from two Italian medical institutions (Università Cattolica del S. Cuore, Rome, and University of Modena and Reggio Emilia, AOU Policlinico, Modena) and from the Virgen de las Nieves University Hospital (Granada, Spain) between January 2013 and January 2015. This ambitious study design provided a population of 781 high-risk patients, 149 of whom were diagnosed with proven and probable IA (19 proven and 130 probable IA). To our knowledge, this is one of the largest populations recruited so far for exploring genetic susceptibility to IA. The study was approved by the ethical review boards of each participating institution.
TABLE 1
TABLE 1 Selected SNPs within immune-modulating genes
Gene nameGene designation (_SNP formatting)dbSNP no.Nucleotide substitutionAmino acid change/locationReported association(s) with infectious diseases/reported or potential functionalityReference(s)
Interleukin 4 (IL4)IL4_-1098rs2243248G/TPromoterAssociated with chronic disseminated candidiasis71
 IL4_Ex1-168rs2070874C/TIntronicUnknown 
 IL4_IVS2-1443rs2243268A/CIntronicUnknown 
 IL4_ IVS3-9rs2243290A/CIntronicUnknown 
Interleukin 4 receptor (IL4R)IL4R_-29429(−3223)rs2057768A/GPromoterAssociated with soluble IL4R protein levels72
IL4R_-28120(−1914)rs2107356A/GPromoterUnknown 
 IL4R_Ex11+828rs1801275A/GQ576RAssociated with enhanced responsiveness to IL473, 74
Interleukin 8 (IL8)IL8_-251rs4073A/TPromoterAssociated with increased levels of IL8 and susceptibility to bacterial urinary tract infection, recurrent Clostridium difficile infection, AIDS, Helicobacter pylori-related gastric diseases, and mycetoma7578
 IL8_ IVS1+230(+396)rs2227307G/TIntronicAssociated with susceptibility to periodontitis79
CXC-chemokine receptor 1 (IL8RA)CXCR1_Ex2+860rs2234671G/CS276TAssociated with chronic hepatitis B virus infection; predicted to affect IL8 signaling (classified as benign by Polyphena)80
CXC-chemokine receptor 2 (IL8RB)CXCR2_Ex3-1010rs1126580A/GIntronicUnknown 
Interleukin 10 (IL10)IL10_IVS1-286rs3024491G/TIntronicUnknown 
 IL10_Ex5+210rs3024496C/TIntronicIL10_Ex5+210G allele is associated with decreased production of IL10 by peripheral blood leukocytes in response to helminth infection81
Interleukin 12 alpha (IL12A)IL12A_IVS2-798rs582054A/TIntronicUnknown 
Interleukin 12 beta (IL12B)IL12B_Ex8+159(+1188)rs3212227A/CIntronicIL12B_+1188C allele is associated with an increased risk of lepromatous leprosy82
Interleukin 13 (IL13)IL13_-1069rs1800925C/TPromoterAlters expression of IL13 and binding of nuclear factors to the IL13 promoter; associated with increased risk of severe respiratory syncytial virus infection83, 84
 IL13_Ex4+98rs20541C/TR144QModifies IL13-mediated Th2 effector functions and correlates with IL13 activity and levels (114Q carriers have higher levels of IL13 than 144R carriers)85
 IL13_IVS3-24rs1295686A/GIntronicUnknown 
Interferon gamma (IFNγ)IFNG_-1615rs2069705C/TPromoterAssociated with a reduced risk of IA45
 IFNG_IVS3+284(+2109)rs1861494C/TIntronicUnknown 
Interferon gamma receptor 2 (IFNγR2)IFNGR2_Ex7-128rs1059293C/TIntronicUnknown 
IFNGR2_Ex2-16rs9808753A/GQ64RPredicted to affect IFNγ signaling (possibly damaging according to polymorphism phenotyping) 
C-C chemokine receptor type 5 (CCR5)CCR5_IVS1+246rs1799987A/GIntronicAssociated with CCR5 protein levels and HIV-186, 87
CCR5_IVS1+151rs2734648G/TIntronicPart of haplotype associated with HIV-188
Macrophage migration inhibitory factor (MIF)MIF_-173rs755622C/GPromoterMIF_-173CC is associated with pulmonary tuberculosis89, 90
Vascular endothelial growth factor alpha (VEGFA)VEGFA_-2578rs699947A/CPromoterVEGF_-2578CC was associated with higher or lower VEGF expression; associated with urinary tract infection9193
 VEGFA_-7rs25648C/TPromoterAssociated with higher levels of VEGFA mRNA94
 VEGFA_IVS2+1378rs3024994C/TIntronicUnknown 
 VEGFA_IVS7-919rs3025035C/TIntronicUnknown 
 VEGFA_6112rs2146323A/C Unknown 
 VEGFA_IVS-99rs3024997A/GIntronicUnknown 
 VEGFA_IVS7+763rs3025030C/GIntronicUnknown 
 VEGFA_5530rs998584G/T Unknown 
 VEGFA_5958bp 3of STPrs6899540A/C Unknown 
 VEGFA_6119bp 3of STPrs6900017C/T Unknown 
 VEGFA_Near geners6905288A/G   
a
Polyphen is a tool that predicts the possible impact of an amino acid substitution on the structure and function of a human protein using straightforward physical and comparative considerations (publicly available at http://genetics.bwh.harvard.edu/pph/data/).
TABLE 2
TABLE 2 Baseline and clinical characteristics of patients with or without IAa
Phase and characteristicValue(s) according to population
aspBIOmics consortium and UHS-GHV-PCRAGACSC-MO and VNHaspBIOmics consortium, UHS-GHV-PCRAGA, and CSC-MO
Overall (n = 593)IA patients (n = 113)Non-IA patients (n = 480)P valueOverall (n = 188)IA patients (n = 36)Non-IA patients (n = 152)P valueOverall (n = 781)IA patients (n = 149)Non-IA patients (n = 632)P value
Phase 1            
    Demographic variables            
        Age (yr [avg ± SD])51.46 ± 15.0851.70 ± 13.2051.40 ± 15.510.834        
        Sex ratio (no. male/no. female)1.27 (332/261)1.90 (74/39)1.16 (258/222)0.031        
    Hematological disease (no. [%])            
        AML412 (69.48)66 (58.41)346 (72.08)0.006        
        ALL73 (12.31)23 (20.35)50 (10.42)0.006        
        Other108 (18.21)24 (21.24)84 (17.50)0.429        
    Allo-HSCT (no. [%])299 (50.42)65 (57.52)234 (48.75)0.116        
    Prophylaxis receivedb (no. [%])            
        Posaconazole73 (12.31)10 (8.85)63 (13.13)0.277        
        Itraconazole49 (8.26)13 (11.50)36 (7.50)0.230        
        Echinocandins30 (5.06)2 (1.77)28 (5.83)0.125        
        Voriconazole21 (3.54)6 (5.31)15 (3.13)0.397        
        Amphotericin B4 (0.67)0 (0.00)4 (0.83)0.738        
    Never received prophylaxis (no. [%])450 (75.89)87 (76.99)363 (75.63)0.855        
Phase 2            
    Demographic variables            
        Age (yr [avg ± SD])    56.93 ± 18.1255.75 ± 19.5057.21 ± 17.840.665    
        Sex ratio (no. male/no. female)    1.32 (107/81)1.57 (22/14)1.27 (85/67)0.705    
    Hematological disease (no. [%])            
        AML    173 (92.02)34 (94.44)139 (91.45)0.799    
        ALL    3 (1.60)1 (2.78)2 (1.32)0.912    
        Other    12 (6.38)1 (2.78)11 (7.24)0.545    
    Allo-HSCT (no. [%])    39 (20.74)3 (8.33)36 (23.68)0.069    
    Prophylaxisc received (no. [%])            
        Posaconazole    40 (40.40)3 (11.11)37 (51.39)0.0007    
        Itraconazole    7 (7.07)3 (11.11)4 (5.56)0.603    
        Echinocandins    1 (1.01)0 (0.00)1 (1.39)0.608    
        Voriconazole    2 (2.02)0 (0.00)2 (2.78)0.942    
        Amphotericin B    16 (16.16)2 (7.41)14 (19.44)0.253    
    Never received prophylaxis (no. [%])    69 (69.70)24 (88.89)45 (62.50)0.022    
Phases 1 and 2            
    Demographic variables            
        Age (yr [avg ± SD])        52.77 ± 16.0352.67 ± 15.0052.79 ± 16.270.935
        Sex ratio (no. male/no. female)        1.28 (439/342)1.81 (96/53)1.19 (343/289)0.031
    Hematological disease (no. [%])            
        AML        585 (74.90)100 (67.11)485 (76.74)0.020
        ALL        76 (09.73)24 (16.11)52 (08.23)0.006
        Other        120 (15.36)25 (16.78)95 (15.03)0.685
    Allo-HSCT (no. [%])        338 (43.28)68 (45.64)270 (42.72)0.579
    Prophylaxis received (no. [%])            
        Posaconazole        113 (16.33)13 (9.29)100 (18.12)0.017
        Itraconazole        56 (8.09)16 (11.43)40 (7.25)0.148
        Echinocandins        31 (4.48)2 (1.43)29 (5.25)0.085
        Voriconazole        23 (3.32)6 (4.29)17 (3.08)0.655
        Amphotericin B        20 (2.89)2 (1.43)18 (3.26)0.383
    Never received prophylaxisd (no. [%])        519 (75.00)111 (79.29)408 (73.91)0.229
a
Abbreviations: HSCT, hematopoietic stem cell transplantation; AML, acute myeloid leukemia; ALL, acute lymphoid leukemia; UHS, University Hospital of Salamanca (Spain); GHV, General Hospital of Valencia (Spain); PCRAGA, clinical trial (EU clinical trial number 2010-019406-17); CSC, Università Cattolica del S. Cuore, Rome (Italy); MO, University of Modena and Reggio Emilia, Modena (Italy); VNH, Virgen de las Nieves University Hospital. P values of ≤0.05 were considered significant and are shown in boldface.
b
Some patients received several prophylactic drugs.
c
Prophylaxis status was available for only 99 subjects (15 IA and 72 non-IA patients).
d
Percentage calculated according to the number of patients with prophylaxis data available.

Statistical analysis.

The Hardy-Weinberg equilibrium (HWE) tests were performed on the uninfected control group by a standard observed-expected chi-square (χ2) test. Logistic regression analyses adjusted for age, gender, country of origin, allo-HSCT status, and receipt or nonreceipt of antifungal prophylaxis were performed to determine significant associations with IA risk. SNPtool (50) and Haploview were used for linkage disequilibrium (LD) block reconstruction and haplotype association statistics. Block structures were determined according to the method of Gabriel et al. (51). In order to account for multiple testing, we calculated an adjusted significance level using the Meff method (52), which considers the number of independent marker loci (MeffLi = 31) and the number of models of inheritance tested (codominant, dominant, recessive, and log additive). Detailed information about this method of multiple testing correction is freely available online at http://neurogenetics.qimrberghofer.edu.au/SNPSpDlite. Thus, the resulting threshold for the main-effect analysis was 0.0004 (the quotient of [0.05/31]/4).

Cell isolation and differentiation.

Peripheral blood mononuclear cells (PBMCs) and monocytes were isolated from whole blood collected from healthy donors after written informed consent was obtained (PI12/02688 and SECVS 014/2015 protocols). PBMCs were isolated by gradient centrifugation using Ficoll-Paque plus (GE Healthcare Bio-Sciences), and monocytes were isolated by immunomagnetic selection of CD14+ cells (Miltenyi Biotec). The purity of the obtained CD14+ population was assessed by fluorescence-activated cell sorting analysis. Monocytes then were plated at a density of 5 × 105 cells/ml in 24-well plates and cultivated for 7 days in complete RPMI 1640 medium supplemented with human serum and 20 ng/ml of granulocyte-macrophage colony-stimulating factor (GM-CSF) to allow differentiation into macrophages. The culture medium was replaced every 3 days. Genotyping of significant SNPs was performed, and either PBMCs or monocytes were grouped according to the genotype of interest.

Assessment of fungicidal activity.

Human monocyte-derived macrophages were infected with conidia from Aspergillus fumigatus at an effector-to-target ratio of 1:10. To measure the fungicidal ability, macrophages were allowed to kill the ingested conidia for 2 h. Serial dilutions of macrophage lysates were plated on solid growth media, and following a 2-day incubation, the number of CFU was enumerated and the percentage of CFU inhibition calculated. In order to avoid a bias due to differences in internalization rates, the supernatants collected after the coculture were plated and compared among different donors.

IL12p70 and IFNγ stimulation assays.

IL12p70 and IFNγ stimulation assays were performed in PBMCs from healthy donors according to a previously reported protocol (53). PBMCs were selected according to the genotypes of IL12B allele rs3212227 (IL12Brs3212227) and IFNγrs2069705 (using dbSNP numbering) and were cultured in 2 ml of RPMI 1640 culture medium supplemented with 10% sterile heat-inactivated fetal bovine serum (FBS) and an antibiotic mixture containing penicillin, streptomycin, and neomycin (Gibco/Life Technologies) at 37°C in 5% CO2. PBMCs from healthy subjects harboring the IFNγrs2069705T/T (n = 8), IFNγrs2069705C/T (n = 8), and IFNγrs2069705C/C (n = 3) genotypes were incubated for 72 h and 96 h with phytohemagglutinin (PHA; 2 μg/ml) alone or in combination with lipopolysaccharide (LPS; 100 ng/ml), and gamma interferon (IFNγ), interleukin-12p70 (IL12p70), tumor necrosis factor (TNF), and IL8 levels were determined in triplicate using the Procartaplex multiplex immunoassay (Affymetrix/eBioscience) according to the manufacturer's recommendations. In parallel, PBMCs bearing the IL12Brs3212227A/A (n = 13), IL12Brs3212227A/C (n = 3), and IL12Brs3212227C/C (n = 1) genotypes were treated for 24 h and 48 h with zymosan (5 μg/ml) alone or in combination with LPS (100 ng/ml), and the correlation of cytokine levels with the IL12Brs3212227 or IL8rs321227 SNP also was analyzed. After the incubation period, supernatants were collected and stored at −80°C until cytokine measurement.

Analysis of IL4R and IFNγ mRNA expression.

We measured IL4R and IFNγ mRNA gene expression in blood samples collected from healthy blood donors but also from monocyte-derived macrophages at baseline and after stimulation with conidia from A. fumigatus at an effector-to-target ratio of 1:2 for 8 h. Total RNA from blood or monocyte-derived macrophages was extracted using an RNeasy minikit (Qiagen) and reverse transcribed with the iScript cDNA synthesis kit (Bio-Rad) according to the manufacturer's instructions. Real-time RT-PCR was performed in an Applied Biosystems 7500HT fast system using TaqMan probe-based gene expression technology (Life Technologies) according to the manufacturer's instructions. Statistical significance in gene expression changes was determined by unpaired t test with Welch's correction (assuming unequal variances between groups).

Analysis of IL4R protein expression on T and B lymphocytes and monocytes by flow cytometry.

IL4R protein levels were determined in PBMCs carrying wild-type IL4Rrs2107356G/G (n = 13), heterozygous IL4Rrs2107356A/G (n = 24), or mutant IL4Rrs2107356A/A genotypes (n = 6) by flow cytometry by following a slightly modified version of a previously reported protocol (54). Briefly, PBMCs (1 × 106) were preincubated with phosphate-buffered saline (PBS) supplemented with 5% fetal bovine serum (FBS) plus 2 mM EDTA for 10 min to block Fc receptors. Subsequently, cells were stained for 45 min at ambient temperature with mouse anti-human antibodies to determine the levels of IL4R protein expression on CD3+ T cells, CD19+ B cells, and CD14+ monocytes. The negative control consisted of cells incubated with mouse phycoerythrin (PE)-IgG1 kappa (BD Pharmingen). The analysis was performed according to the flow-cytometric cell surface staining method, and the following antibodies were used: PE-conjugated CD124+, peridinin chlorophyll protein (PerCP)-conjugated CD14+, allophycocyanin (APC)-eFluor780-conjugated CD3+, and fluorescein isothiocyanate (FITC)-conjugated CD19+ antibodies (BD Pharmingen). Cells were acquired on a BD FACSVerse flow cytometer (BD Biosciences), and the data were analyzed using FlowJo software (TreeStar Inc.). The median fluorescence intensity (MFI) of the positive population was recorded for each cell type, and statistical differences were evaluated using an unpaired t test with Welch's correction (two-tailed P value).

Predictive models and discriminative accuracy.

The value of immune-modulating polymorphisms for the prediction of IA was examined using stepwise logistic regression analysis. A prediction model was built that included age, gender, allo-HSCT status, and antifungal prophylactic status, as well as those genetic variants that showed significant associations with IA in the single-SNP analysis (P < 0.05 for phases 1 and 2). Using P values as a selection criterion, variables with the highest P value then were dropped and analyses were finalized when all variables reached statistical significance (P < 0.05). A predictive model with a similar number of nonsignificant SNPs (P > 0.10) also was built. The area under the curve (AUC) of a receiver operating characteristic (ROC) curve analysis was used to assess the discriminative accuracy of each model compared with a reference model including only demographic and clinical variables as covariates (age, gender, and allo-HSCT and antifungal prophylaxis status). A −2-log likelihood ratio (LR) test was used to determine whether predictive models including genetic information were statistically different from the reference model. Finally, we ran a randomization test to confirm whether the improved predictive ability of the model including genetic variants significantly associated with IA was consistent after 50,000 iterations. Further details are included in the supplemental material. All analyses were performed using R (http://www.r-project.org/).

RESULTS

A total of 781 patients were enrolled in this case-control study, and among them, 149 were diagnosed with proven or probable IA according to the revised EORTC/MSG definitions. The remaining 632 patients showed no evidence for proven or probable IA. Baseline and clinical characteristics of IA and non-IA patient groups are summarized in Table 2. Overall, IA and non-IA patients had a similar mean age (52.67 versus 52.79 years; P = 0.935), but IA patients showed a significantly higher male-to-female gender ratio than patients with no evidence of IA (1.81 versus 1.19; P = 0.031). In addition, the percentage of patients diagnosed with ALL was significantly higher in IA than in non-IA patients (16.11% versus 8.23%; P = 0.006), whereas the percentage of patients with AML was significantly lower in IA than non-IA cases (67.11% versus 76.74%; P = 0.020). Interestingly, we also observed a significantly smaller proportion of IA cases among those receiving posaconazole prophylaxis (P = 0.017).
Thirty-six genetic variants within 14 immune-modulating genes initially were genotyped in 593 high-risk patients (113 IA and 480 non-IA patients). Logistic regression analysis revealed that patients carrying the IL4Rrs2107356A/A, VEGFArs2146323A, and VEGFArs6900017T alleles had a significantly increased risk of IA (odds ratio [OR] of 2.05 and 95% confidence interval [CI] of 1.24 to 3.40, OR of 1.63 and 95% CI of 1.02 to 2.61, and OR of 1.76 and 95% CI of 1.02 to 3.03, respectively), whereas patients carrying the IL12Brs3212227C and IFNγrs2069705C alleles showed a significantly decreased risk of developing the disease (OR of 0.57 [95% CI of 0.35 to 0.93] and OR of 0.56 [95% CI of 0.36 to 0.88], respectively) (Table 3). When a log-additive model was assumed, we also found a significant association between the VEGFArs2146323 and VEGFArs6900017 SNPs and an increased risk of IA (per-allele OR of 1.45 [95% CI of 1.04 to 2.03] and per-allele OR of 1.73 [95% CI of 1.08 to 2.77], respectively) and a statistically significant association of the IFNγrs2069705 SNP with a decreased risk of developing IA (per-allele OR of 0.69 and 95% CI of 0.49 to 0.97).
TABLE 3
TABLE 3 Associations found between immunoregulatory polymorphisms and invasive aspergillosish
Variant dbSNPGeneResults for:
Phase 1aOverallb
OR (95% CI)P valueOR (95% CI)P value
rs2243248IL41.19 (0.63–2.26)0.59  
rs2070874IL40.91 (0.53–1.55)0.72  
rs2243268IL40.85 (0.52–1.38)0.50  
rs2243290IL40.67 (0.39–1.16)0.14  
rs2057768IL4R1.20 (0.75–1.92)0.44  
rs2107356IL4R2.05 (1.24–3.40)c0.00631.92 (1.20–3.09)c0.008
rs1801275IL4R1.00 (0.63–1.59)0.99  
rs4073IL81.02 (0.64–1.61)0.95  
rs2227307IL81.72 (1.00–2.94)c0.0491.73 (1.06–2.81)c0.031
rs2234671IL8RA1.57 (0.80–3.08)0.20  
rs1126580IL8RB1.50 (0.88–2.54)0.13  
rs3024491IL101.09 (0.67–1.78)0.72  
rs3024496IL101.16 (0.71–1.90)0.55  
rs582054IL12A1.09 (0.64–1.84)0.76  
rs3212227IL12B0.57 (0.35–0.93)0.0210.60 (0.38–0.96)g0.029
rs20541IL130.76 (0.46–1.24)0.26  
rs1800925IL130.85 (0.54–1.36)0.51  
rs1295686IL130.73 (0.45–1.16)0.18  
rs2069705IFNγ0.56 (0.36–0.88)d0.0120.63 (0.41–0.97)0.035
rs1861494IFNγ0.74 (0.47–1.17)0.20  
rs1059293IFNγR20.98 (0.57–1.67)0.93  
rs9808753IFNγR21.10 (0.65–1.85)0.72  
rs1799987CCR51.40 (0.83–2.36)0.20  
rs2734648CCR51.07 (0.67–1.71)0.76  
rs755622MIF1.38 (0.84–2.25)0.20  
rs25648VEGFA1.11 (0.63–1.97)0.72  
rs699947VEGFA1.28 (0.75–2.18)0.35  
rs3024994VEGFA1.61 (0.86–3.03)0.15  
rs3025035VEGFA1.31 (0.78–2.22)0.31  
rs2146323VEGFA1.63 (1.02–2.61)e0.0401.46 (0.95–2.27)0.085
rs3024997VEGFA1.04 (0.67–1.61)0.87  
rs3025030VEGFA1.00 (0.58–1.70)0.99  
rs998584VEGFA0.66 (0.41–1.06)0.088  
rs6899540VEGFA0.84 (0.51–1.40)0.50  
rs6900017VEGFA1.76 (1.02–3.03)f0.0461.47 (0.87–2.47)0.16
rs6905288VEGFA0.83 (0.52–1.31)0.42  
a
Phase 1 included aspBIOmics, PCRAGA, Valencia, and Salamanca populations (n = 593 hematological patients).
b
Overall included patients (n = 781) after extension with 188 high-risk patients (39 HSCT and 149 non-HSCT patients), with prophylaxis data for 87 of them.
c
Estimated according to a recessive model of inheritance.
d
IFNγrs2069705 per-allele OR, 0.69; 95% CI, 0.49 to 0.97; Ptrend = 0.032 (log-additive model of inheritance).
e
VEGFArs2146323 per-allele OR, 1.45; 95% CI, 1.04 to 2.03; Ptrend = 0.029 (log-additive model of inheritance).
f
VEGFArs6900017 per-allele OR, 1.73; 95% CI, 1.08 to 2.77; Ptrend = 0.027 (log-additive model of inheritance).
g
IL12Brs3212227 per-allele OR, 0.67; 95% CI, 0.45 to 0.99; Ptrend = 0.040 (log-additive model of inheritance).
h
Estimates were adjusted for age, sex, country of origin, and allo-HSCT and prophylaxis status (i.e., prior use of prophylaxis). P < 0.05 for values in boldface. P < 0.0004 was defined as the multiple-testing significance threshold.
To further confirm these significant associations, the study cohort was extended by recruiting 188 additional patients, 36 of whom were diagnosed with proven or probable IA. Given the small number of proven and probable IA cases, we could not consider this second population an independent population for replication. An overall association analysis including 781 patients (149 IA and 632 non-IA patients) confirmed that carriers of the IL4Rrs2107356A/A and IL8rs2227307G/G genotypes had a significantly increased risk of IA compared to those carrying the wild-type allele (OR of 1.92 [95% CI of 1.20 to 3.09] and OR of 1.73 [95% CI of 1.06 to 2.81], respectively), whereas those subjects harboring the IL12Brs3212227C and IFNγrs2069705C alleles showed a decreased risk of developing the infection (OR of 0.60 [95% CI of 0.38 to 0.96] and OR of 0.63 [95% CI of 0.41 to 0.97], respectively). When we tested the allele dose effect of significant SNPs, we found that the IL12Brs3212227 polymorphism was significantly associated with a reduced risk of getting the infection (per-allele OR of 0.67 and 95% CI of 0.45 to 0.99) (Table 3). As part of these association analyses, we also performed haplotype analysis that confirmed that none of these polymorphisms were part of risk haplotypes. We observed a significant association with IA only for a relatively rare IFNγTC haplotype whose effect likely was due to the IFNγrs2069705 SNP (OR of 0.34 and 95% CI of 0.13 to 0.88; see Table S1 in the supplemental material).
Interestingly, a logistic regression analysis restricted to allo-HSCT patients and considering only donor genotypes and episodes of IA that occurred after transplantation (n = 171) also showed that the effect of the IL4R2107356 and IFNγrs2069705 SNPs on the risk of IA was considerably stronger in allo-HSCT patients than in patients who did not undergo transplantation (for IL4R2107356, OR of 5.63 [95% CI of 1.98 to 16.05] versus OR of 1.48 [95% CI of 0.81 to 2.71]; for IFNγrs2069705, OR of 0.24 [95% CI of 0.10 to 0.59] versus OR of 0.86 [95% CI of 0.52 to 1.45]) (Table 4). In this allo-HSCT-stratified analysis, we also found that allo-HSCT patients carrying the VEGFArs3024994T allele showed an increased risk of IA compared with those of allo-HSCT patients carrying the wild-type genotype/allele (OR of 4.48 and 95% CI of 1.25 to 16.08) (Table 4).
TABLE 4
TABLE 4 Associations found between immunoregulatory SNPs and IA in allo-HSCT patientsa (n = 171)
Variant dbSNP entryGeneOR (95% CI)P value
rs2243248IL40.93 (0.25–3.44)0.91
rs2070874IL41.40 (0.56–3.52)0.47
rs2243268IL41.15 (0.49–2.73)0.75
rs2243290IL40.93 (0.35–2.45)0.88
rs2057768IL4R1.30 (0.56–3.03)0.54
rs2107356IL4R5.63 (1.98–16.05)b,c0.0009
rs1801275IL4R0.50 (0.20–1.24)0.12
rs4073IL80.76 (0.33–1.73)0.51
rs2227307IL81.21 (0.51–2.86)0.67
rs2234671IL8RA1.48 (0.42–5.15)0.55
rs1126580IL8RB2.39 (0.92–6.20)b0.072
rs3024491IL101.08 (0.44–2.70)0.86
rs3024496IL100.74 (0.30–1.83)0.52
rs582054IL12A1.73 (0.61–4.91)0.29
rs3212227IL12B0.64 (0.26–1.57)0.32
rs20541IL130.80 (0.32–1.99)0.63
rs1800925IL131.96 (0.84–4.58)0.12
rs1295686IL130.53 (0.22–1.29)0.16
rs2069705IFNγ0.24 (0.10–0.59)e0.0011
rs1861494IFNγ0.63 (0.27–1.49)0.29
rs1059293IFNγR21.53 (0.59–3.97)0.37
rs9808753IFNγR20.78 (0.29–2.09)0.62
rs1799987CCR51.75 (0.65–4.69)0.26
rs2734648CCR51.04 (0.44–2.48)0.93
rs755622MIF0.63 (0.25–1.61)d0.33
rs25648VEGFA1.39 (0.53–3.66)0.51
rs699947VEGFA0.51 (0.19–1.36)0.18
rs3024994VEGFA4.48 (1.25–16.08)f0.022
rs3025035VEGFA1.96 (0.77–4.99)0.16
rs2146323VEGFA0.86 (0.38–1.97)0.72
rs3024997VEGFA0.71 (0.31–1.62)0.41
rs3025030VEGFA1.11 (0.41–3.01)d0.84
rs998584VEGFA0.84 (0.32–2.22)0.72
rs6899540VEGFA0.62 (0.23–1.69)0.34
rs6900017VEGFA2.68 (0.97–7.42)0.061
rs6905288VEGFA1.04 (0.43–2.53)0.92
a
Estimates were adjusted for age, sex, country of origin, severe neutropenia, and prophylactic status (i.e., prior use of prophylaxis). P < 0.05 for values in boldface. P < 0.0004 was defined as the corrected significance threshold.
b
Estimated according to a recessive model of inheritance.
c
IL4Rrs2107356 per-allele OR, 2.17; 95% CI, 1.18 to 3.98; Ptrend = 0.0097 (log-additive model of inheritance).
d
Estimates calculated according to a codominant model (homozygotes for the rare allele were not found).
e
IFNγrs2069705 per-allele OR, 0.50; 95% CI, 0.26 to 0.95; Ptrend = 0.027 (log-additive model of inheritance).
f
VEGFArs3024994 per-allele OR, 3.19; 95% CI, 1.08 to 9.45; Ptrend = 0.033 (log-additive model of inheritance).
Although none of the reported overall, haplotype-stratified, and allo-HSCT-stratified associations remained significant after correction for multiple testing (PMeff_correction = 0.0004), the association of IL4Rrs2107356 and IFNγrs2069705 polymorphisms showed a marginal level of significance in allo-HSCT patients when recessive (PREC) and dominant (PDOM) models were assumed (PREC = 0.0009 and PDOM = 0.0011). Considering these results and those showing a suggestive association between IL12Brs3212227 SNP and risk of IA, we decided to evaluate whether the IL4Rrs2107356, IL8rs2227307, IL12Brs3212227, and IFNγrs2069705 variants had a functional effect in modulating the strength of immune responses against specific Aspergillus antigens and/or stimulatory molecules. For that purpose, we first investigated whether the presence of IFNγrs2069705 and IL4Rrs2107356, as well as IL12Brs3212227 and IL8rs2227307, variants correlated with the ability of monocyte-derived macrophages to efficiently kill fungal conidia. Interestingly, we found that macrophages from donors carrying the IFNγrs2069705C allele showed a significantly increased ability to kill fungal spores compared to that of subjects carrying the wild-type genotype (TT versus TC, P = 0.0043; TT versus CC, P = 0.0012; and TT versus TC+CC, P = 0.0003) (Fig. 1A). No differences in killing ability were observed in macrophages from donors carrying the IL4Rrs2107356A/A and IL8rs2227307G/G genotypes or the IL12Brs3212227C allele compared with their respective wild-type allele/genotype (Fig. 1B to D).
FIG 1
FIG 1 Fungicidal activity of monocyte-derived macrophage according to IFNγ (A), IL12B (B), IL4R (C), and IL8 (D) genotypes.
Motivated by these results, we decided to investigate whether the presence of the above-mentioned SNPs correlated with cytokine levels after stimulation of PBMCs from healthy donors with fungal antigens (zymosan) or stimulatory molecules (LPS and PHA). These in vitro stimulation experiments revealed that carriers of the IFNγrs2069705C allele showed an increased production of IFNγ after 4 days of incubation with LPS or PHA and when both stimulating reagents were used in combination (PLPS = 0.057, PPHA = 0.036, and PLPS+PHA = 0.030) (Fig. 2A; also see Table S2 in the supplemental material). We also observed that donors carrying the IFNγrs2069705C allele showed a drastic increase in the production of TNF at almost all time points compared to those bearing the wild-type genotype (PPHA-72 h = 0.045, PLPS+PHA-72 h = 0.018, PLPS-96 h = 0.058, and PLPS+PHA-96 h = 0.0058) (Fig. 2B; also see Table S2). In addition, we observed that subjects carrying the IFNγrs2069705C allele tended to have an increased production of IL12p70 compared with that of subjects carrying the wild-type genotype (Fig. 2C; also see Table S2). No correlation between IL12p70 and IL8 levels and IL12Brs3212227 and IL8rs2227307 genotypes was found. These findings suggest that the IFNγrs2069705 SNP contributes to modulating the risk of IA, likely through the regulation of IFNγ mRNA levels.
FIG 2
FIG 2 IFNγ, TNF-α, and IL12p70 cytokine levels on stimulated PBMCs according to the IFNγrs2069705 genotypes. PBMCs from healthy donors were stimulated with Zymosan (5 μg/ml) and PHA (2 μg/ml) alone or in combination with LPS (100 ng/ml). Supernatants were harvested for IFNγ, TNFα, and IL12p70 analysis at 72 and 96 h.
In order to test this hypothesis, we measured IFNγ mRNA expression in PBMCs from healthy donors (n = 21) that were grouped according to the IFNγrs2069705 genotype. Importantly, we found that carriers of the IFNγrs2069705C allele (C/T+CC) showed a significantly increased level of IFNγ mRNA compared with those of subjects carrying the wild-type genotype (40.85 ± 11.65 versus 13.87 ± 5.43; P = 0.049) (Fig. 3A and B). Although this result pointed toward a role of this SNP in modulating IFNγ gene expression in PBMCs, we decided to further confirm this result by looking at the publicly available blood expression quantitative trait loci (eQTL) browser (http://genenetwork.nl/bloodeqtlbrowser/). Of note, we found that, in agreement with our gene expression data, this variant, located in the promoter region of the gene (but also those neighboring SNPs within the same linkage disequilibrium block), showed a positive correlation with IFNγ mRNA expression level that ranged between P = 1.01 · 10−3 and P = 1.70 · 10−3 (see Fig. S1 in the supplemental material).
FIG 3
FIG 3 IFNγ (A and B) and IL4R mRNA (C to F) and protein (G) expression levels according to IL4Rrs2107356 genotypes. (A and B) Correlations between IFNγrs2069705 genotypes and IFNγ mRNA expression levels were analyzed in PBMCs from healthy donors at baseline or after being stimulated with Aspergillus conidia. Correlations between IL4Rrs2107356 genotypes and IL4R mRNA expression levels were analyzed in blood samples from healthy donors (C and D) and in monocyte-derived macrophages at baseline or after stimulation with Aspergillus conidia (E and F). Correlations between this promoter variant and IL4R protein levels also were analyzed in different immune cells (CD19+, CD14+, and CD3+) by flow cytometry (G).
We next analyzed the correlation between the IL4Rrs2107356 SNP and IL4R mRNA expression levels in whole-blood samples collected from healthy donors (n = 43) and in monocyte-derived macrophages at baseline and after in vitro stimulation with A. fumigatus conidia (n = 12). Gene expression data from blood samples did not show differences in IL4R mRNA expression levels between homozygotes (GG versus AA; P = 0.304), but they showed a significantly increased level of expression in heterozygotes compared to those of samples with either wild-type or mutant homozygotes (AG versus GG [P = 0.0045] and AG versus AA [P = 0.031], respectively) (Fig. 3C). When carriers of the IL4Rrs2107356AA genotype were compared with those of carriers of the wild-type allele (according to the genetic model used in our genetic analysis), we failed to find statistical differences in IL4R mRNA expression levels (PAA vs GG+AG = 0.2937) (Fig. 3D). These findings did not support our hypothesis suggesting a functional effect for this promoter polymorphism on mRNA expression but were in agreement with data from the blood eQTL browser that reported no association of this promoter variant with IL4R mRNA expression levels in PBMCs. In line with these results, we found no correlation between the IL4Rrs2107356 variant and IL4R mRNA expression levels in monocyte-derived macrophages at baseline and after stimulation with A. fumigatus conidia (Fig. 3E and F). The lack of correlation between this promoter polymorphism and IL4R protein level also was confirmed by flow cytometry analysis in different immune cell types (CD19+, CD14+, and CD3+ cells) (Fig. 3G). We found a significantly increased level of IL4R in CD19+ cells in heterozygotes only compared with those subjects carrying the AA genotype (P = 0.032) (Fig. 3G), which was in line with our gene expression data from blood samples from healthy donors and confirmed that the effect attributed to this promoter variant cannot be explained by changes in mRNA or protein expression levels. Given that IL4R is internalized in a time-dependent manner after stimulation with IL4, we could not confidently measure the correlation of the IL4R2107356 SNP and IL4R protein levels under stimulation conditions (data not shown).
Finally, considering the association of some of the immune-modulating SNPs with the risk of developing IA and given the correlation of some of these genetic markers with cytokine levels, we tested the capacity of these variants to confidently predict the disease risk. We assessed the predictive capacity of a model built with significant SNPs and demographic and clinical variables, whereas the reference model included only demographic and clinical variables. Despite the modest population size, we found that a predictive model including 4 variants associated with IA, age, gender, allo-HSCT, and antifungal prophylaxis status showed an improvement in discriminatory ability to predict the disease compared with the ability of the reference model (AUC of 0.659 and 95% CI of 0.596 to 0.722 [P = 0.000005] versus AUC of 0.564 and 95% CI of 0.499 to 0.630 [P = 0.064; P−2 log likehood ratio test = 0.00052]) (Table 5 and Fig. 4). Importantly, we also observed that a model built with a similar number of nonsignificant SNPs, together with demographic and clinical variables, did not show any significant change in predictive capacity compared with the reference model (see Table S3 in the supplemental material), which confirmed the utility of significant SNPs in predicting IA. The consistency of the predictive results was supported through a 50.000 permutation test (50.000perm) that showed that none of the 50.000-permuted models had a better prediction capacity than our genetic model built with SNPs significantly associated with IA [AUCsort-average of 0.6001, SDsort-AUC of 0.0158, and Z score of 3.7361; PZ score value(50.000perm) = 9.34 · 10−05] (Table 5; also see the supplemental material).
TABLE 5
TABLE 5 Discriminative-value AUC for models with or without immune-modulating variants
Model and parameter or SNPP valueOR (95% CI)AUC (95% CI)aP value
Reference model    
    Age0.8981.001 (0.985–1.017)  
    Gender0.0331.721 (1.045–2.835)  
    Allo-SCT0.7850.934 (0.570–1.529)  
    Prophylactic status0.7901.080 (0.612–1.906)0.564 (0.499–0.630)c0.064
Predictive model built with 4 significant SNPsb    
    IL8rs22273070.0241.952 (1.093–3.489)  
    IL12Brs32122270.0160.508 (0.292–0.884)  
    IFNγrs20697050.0310.583 (0.358–0.952)  
    VEGFArs69000170.0401.814 (1.026–3.207)  
    Age0.9511.001 (0.984–1.018)  
    Gender0.0641.626 (0.972–2.719)  
    Allo-HSCT0.7570.923 (0.557–1.532)  
    Prophylactic status0.5251.210 (0.672–2.179)0.659 (0.596–0.722)c0.000005
a
Including age, gender, and allo-HSCT and prophylactic status as variables never dropped from models.
b
IL4Rrs2107356 and VEGFArs2146323 polymorphisms were not significant and were dropped from the model.
c
These models showed a statistically different prediction capacity (−2-log likelihood ratio test; df = 4; P = 0.00052). Residual deviance (reference model), 433.21; residual deviance (significant SNP model), 413.31. After removing missing values, 455 subjects (85 IA and 370 non-IA cases) were available for prediction capacity analysis. The following values were determined by permutation analysis: average AUC of null distribution (50.000 models), 0.6001; SD50.000AUC, 0.0158; Z score, 3.7361; PZ score value(50.000perm), 9.34 · 10−05.
FIG 4
FIG 4 Receiver operating characteristics (ROC) curve analysis. ROC curves summarize the accuracy of prediction for each model. The model including SNPs significantly associated with IA and demographic and clinical variables (marked in blue) showed a significantly improved predictive capacity compared with that of a reference model including only demographic and clinical variables (marked in red).

DISCUSSION

In this study, we report an association between genetic variants within IL4R, IL8, IL12B, and IFNγ genes and the risk of developing IA. Carriers of the IL4Rrs2107356A/A and IL8rs2227307G/G genotypes had a significantly increased risk of developing the infection, whereas patients carrying the IL12Brs3212227C and IFNγrs2069705C alleles showed a substantially decreased risk of IA than those harboring the wild-type allele/genotype. Although none of these associations persisted after a restrictive correction for multiple testing, we found that the association of the IL4Rrs2107356 and IFNγrs2069705 polymorphisms reached marginal significance in the allo-HSCT patient population, which pointed toward an impact of these variants in modulating the risk of developing IA, particularly in high-risk populations. Based on these results but also those suggesting overall associations of polymorphisms within the IL4R, IL8, IL12B, and IFNγ genes at the conventional significance threshold of P ≤ 0.05, it seems plausible to suggest that polymorphisms within these genes affect gene function and therefore contribute to the pathogenesis of IA. We hypothesized that both IL4Rrs2107356 and IFNγrs2069705 polymorphisms, located in the promoter region of their respective genes, affect gene expression and, consequently, have an effect in modulating IL4- and IFNγ-mediated immune responses against Aspergillus. Likewise, we hypothesized that IL8rs2227307 and IL12Brs3212227 intronic polymorphisms affect alternative splicing of IL8 and IL12B mRNA and even alter mRNA expression, thereby dysregulating IL8- and IL12-mediated Th1 immune responses against fungal pathogens. In line with these arguments, a number of previous studies have reported associations of these polymorphisms with immune-related diseases (55, 56) and infections (5760), including IA (45). In particular, we confirmed an association previously reported by Mezger et al. between the IFNγrs2069705 SNP and the risk of IA (45). This supports our hypothesis that this association is true and that this variant plays a role in modulating the immune response against Aspergillus.
Recent studies in humans and animal models have demonstrated that IL4/IL4R, IL8, IL12 and IFNγ have a central role in IA (19, 6165). In particular, IFNγ seems to be a key factor, as its enhanced production (14, 66) or its therapeutic administration (63) boosts the production of free oxygen radicals and the neutrophil-mediated damage of fungal hyphae and promotes resistance to the infection (19, 67). It is also well documented that the production of IL8 and IL12p70 by epithelial and dendritic cells in response to conidia enhances Th1-mediated immune responses (61, 68, 69) and increases resistance to IA (62), whereas its neutralization produces a marked increase in susceptibility to IA (15, 19). IL12p70 also mediates enhanced cytotoxic activity of NK cells and CD8+ T cells and promotes the secretion of IFNγ by CD4+ T cells, which is an essential process for an efficient clearance of inhaled Aspergillus fumigatus spores. Conversely, IL4 secretion activates Th2-CD4+ T cell immune responses and leads to a significant decrease in Th1 immune responses and, consequently, increases susceptibility to Aspergillus infection (19, 70). Similarly, it also has been demonstrated that the lack of IL4 cytokine increases Th1 immune responses characterized by high-level production of IL12 and an enhanced IL12-mediated production of IFNγ by T lymphocytes, thereby leading to an increase in resistance to developing the infection (19).
In light of these results and in order to better characterize the role of IFNγ, IL4R, IL8, and IL12B polymorphisms in modulating immune responses against fungal antigens and/or specific stimulatory molecules, we proceeded to perform functional assays in PBMCs and monocyte-derived macrophages from healthy donors. Importantly, we found that subjects carrying the IFNγrs2069705C allele showed an increased ability to kill A. fumigatus conidia compared to that of subjects carrying the wild-type allele. This important finding supports our genetic findings as well as those from a previous study (45), suggesting that the IFNγrs2069705 promoter variant plays a key role in modulating the strength of immune responses against Aspergillus, likely through the modulation on IFNγ mRNA expression. Importantly, we also observed that, under stimulating conditions, PBMCs from carriers of the IFNγrs2069705C allele showed an increased production of IFNγ and TNF cytokines compared to that of individuals carrying the wild-type allele. In addition to this, we found that the IFNγrs2069705 SNP correlated with IFNγ mRNA levels in PBMCs from healthy donors, which again suggested that this polymorphism, or another causative polymorphism in strong linkage disequilibrium with it, is involved not only in the control of IFNγ production but also in the subsequent induction of TNF production. Taking all these findings together, we propose a central role for this variant in determining the risk of IA in allo-HSCT and in leukemia patients undergoing intensive chemotherapy.
Although genetic data suggested that polymorphisms within IL4R, IL8, and IL12B influence the risk of developing IA, functional experiments did not show any correlation between these SNPs and their respective mRNA and/or protein levels. Therefore, we suggest that these variants exert their biological function by modulating other biological processes, such as mRNA processing (splicing or turnover) or mRNA stability, or even act at the posttranscriptional level. Further studies now are warranted to replicate our findings and to experimentally identify the functional role of these SNPs in determining the risk of IA.
Finally, given the genetic and/or functional effect observed for variants within IFNγ, IL4R, IL8, IL12B, and VEGFA genes, we found it interesting to determine the impact of these variants in predicting the disease risk. We found that a model built with IL8rs2227307, IL12Brs3212227, IFNγrs2069705, and VEGFArs6900017 SNPs showed a significantly improved discriminatory ability to predict the disease compared with a model that included demographic and clinical variables. Importantly, when a similar number of nonsignificant SNPs was added to the reference model, we did not observe any significant change in predictive capacity, which confirmed that only a model built with these significant SNPs could have the capacity to predict the infection. In support of this finding, we also observed that the AUC of this model was systematically higher than the AUC observed for 50.000 iterative models, which emphasizes the importance of considering predictive models to assist in the clinical decision-making process and to improve novel strategies to prevent IA occurrence.
This study has both strengths and limitations. Study strengths include a multicenter population-based design, a relatively large sample size, and the large number of genetic polymorphisms analyzed. This allowed us, for the first time, to perform predictive analyses to assess the potential utility of genetic variants in predicting with confidence the risk of developing IA. Potential weaknesses include limited antifungal prophylaxis data availability for a subset of patients and a relatively small number of proven or probable IA cases that limited the study's statistical power to rule out spurious associations. To minimize this limitation, the most relevant associations were functionally validated.
In conclusion, our data suggest that immune-modulating polymorphisms have an impact on the risk of IA, and that the genotyping of these variants could help to predict the risk of IA and could be useful in establishing a risk-adapted antifungal prophylaxis strategy.

ACKNOWLEDGMENTS

We thank Thomas Rogers (St. James's Hospital, Dublin, Ireland) for reviewing and editing the English language and António Marques (Hospital de Braga, Portugal) for providing the buffy coats.
M.J. and J.S. conceived the study and participated in its design and coordination. C.B.L. performed the genetic analyses. C.B.L., L.M.C., J.S.-C., A.O.-C., A. Carvalho, and J. Sainz performed in vitro analyses. L.A.-F., A. Carvalho, J. Springer, M. Lackner, A. Comino, C.O., R.R., M.C.-E., C.S., M.A.L.-N., A.F.-M., C.C., T.V., L.F., J.M.A., L. Pagano, E.L.-F., L. Potenza, M. Luppi, C.L.-F., J.L., H.E., L.V., and the PCRAGA Study Group coordinated patient's recruitment and provided the clinical data. J.S. analyzed the data. M.J. and J. Sainz drafted the manuscript. All authors read and approved the final version of the manuscript.
This study was supported by grants PI12/02688 from the Fondo de Investigaciones Sanitarias (Madrid, Spain), PIM2010EPA-00756 from the ERA-NET PathoGenoMics (0315900A), and the Collaborative Research Center/Transregio 124 FungiNet. C.C. is supported by the Fundação para a Ciência e Tecnologia, Portugal (SFRH/BPD/96176/2013). This study also was supported by a donation of Consuelo González Moreno, an acute myeloid leukemia survivor. We thank Astella Pharma Inc. for supporting this work.

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cover image Infection and Immunity
Infection and Immunity
Volume 84Number 3March 2016
Pages: 643 - 657
Editor: G. S. Deepe Jr., University of Cincinnati

History

Received: 31 October 2015
Returned for modification: 29 November 2015
Accepted: 5 December 2015
Published online: 24 February 2016

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Authors

C. B. Lupiañez
Genomic Oncology Department, GENYO, Centre for Genomics and Oncological Research, Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, Granada, Spain
Hematology Department, Virgen de las Nieves University Hospital, Granada, Spain
L. M. Canet
Genomic Oncology Department, GENYO, Centre for Genomics and Oncological Research, Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, Granada, Spain
Hematology Department, Virgen de las Nieves University Hospital, Granada, Spain
A. Carvalho
Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of Minho, Braga, Portugal
ICVS/3B's–PT Government Associate Laboratory, Braga/Guimarães, Portugal
L. Alcazar-Fuoli
Mycology Reference Laboratory, Centro Nacional de Microbiologia, Instituto de Salud Carlos III, Madrid, Spain
J. Springer
Universitätsklinikum Würzburg, Medizinische Klinik II, Würzburg, Germany
M. Lackner
Division of Hygiene and Medical Microbiology, Medical University of Innsbruck, Innsbruck, Austria
J. Segura-Catena
Genomic Oncology Department, GENYO, Centre for Genomics and Oncological Research, Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, Granada, Spain
Hematology Department, Virgen de las Nieves University Hospital, Granada, Spain
A. Comino
Experimental Research Unit, Virgen de las Nieves University Hospital, Granada, Spain
C. Olmedo
Experimental Research Unit, Virgen de las Nieves University Hospital, Granada, Spain
R. Ríos
Genomic Oncology Department, GENYO, Centre for Genomics and Oncological Research, Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, Granada, Spain
Hematology Department, Virgen de las Nieves University Hospital, Granada, Spain
A. Fernández-Montoya
Blood Transfusion Regional Centre and Sectorial Tissue Bank, Granada, Spain
M. Cuenca-Estrella
Mycology Reference Laboratory, Centro Nacional de Microbiologia, Instituto de Salud Carlos III, Madrid, Spain
C. Solano
Hematology Department, Clinic University Hospital of Valencia, Valencia, Spain
M. Á. López-Nevot
Immunology Department, Virgen de las Nieves University Hospital, Granada, Spain
C. Cunha
Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of Minho, Braga, Portugal
A. Oliveira-Coelho
Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of Minho, Braga, Portugal
T. Villaescusa
Hematology Department, University Hospital of Salamanca, Salamanca, Spain
Hematology Department, Jiménez Díaz Foundation, Madrid, Spain
L. Fianchi
Istituto di Ematologia, Università Cattolica del S. Cuore, Rome, Italy
J. M. Aguado
Unit of Infectious Diseases, Hospital Universitario 12 de Octubre, Instituto de Investigación Hospital 12 de Octubre (i+12), Madrid, Spain
L. Pagano
Istituto di Ematologia, Università Cattolica del S. Cuore, Rome, Italy
E. López-Fernández
Hematology Department, Virgen de las Nieves University Hospital, Granada, Spain
L. Potenza
Department of Medical and Surgical Sciences, University of Modena and Reggio Emilia, AOU Policlinico, Modena, Italy
M. Luppi
Department of Medical and Surgical Sciences, University of Modena and Reggio Emilia, AOU Policlinico, Modena, Italy
C. Lass-Flörl
Division of Hygiene and Medical Microbiology, Medical University of Innsbruck, Innsbruck, Austria
J. Loeffler
Universitätsklinikum Würzburg, Medizinische Klinik II, Würzburg, Germany
H. Einsele
Universitätsklinikum Würzburg, Medizinische Klinik II, Würzburg, Germany
L. Vazquez
Hematology Department, University Hospital of Salamanca, Salamanca, Spain
the PCRAGA Study Group
M. Jurado
Genomic Oncology Department, GENYO, Centre for Genomics and Oncological Research, Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, Granada, Spain
Hematology Department, Virgen de las Nieves University Hospital, Granada, Spain
J. Sainz
Genomic Oncology Department, GENYO, Centre for Genomics and Oncological Research, Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, Granada, Spain
Hematology Department, Virgen de las Nieves University Hospital, Granada, Spain

Editor

G. S. Deepe Jr.
Editor
University of Cincinnati

Notes

Address correspondence to J. Sainz, [email protected].

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