Polymorphisms within the TNFSF4 and MAPKAPK2 Loci Influence the Risk of Developing Invasive Aspergillosis: A Two-Stage Case Control Study in the Context of the aspBIOmics Consortium

Fungi Journal of Article Polymorphisms within the TNFSF4 and MAPKAPK2 Loci Influence the Risk of Developing Invasive Aspergillosis: A Two-Stage Case Control Study in the Context of the aspBIOmics Consortium Jose Manuel Sánchez-Maldonado 1,2,3,†, Ana Moñiz-Díez 1,2,3,†, Rob ter Horst 4, Daniele Campa 5, Antonio JoséCabrera-Serrano 1, Manuel Martínez-Bueno 6, María del Pilar Garrido-Collado 2,3, Francisca Hernández-Mohedo 2,3, Laura Fernández-Puerta 2,3, Miguel Ángel López-Nevot 7, Cristina Cunha 8,9, Pedro Antonio González-Sierra 2,3, Jan Springer 10, Michaela Lackner 11 , Laura Alcazar-Fuoli 12, Luana Fianchi 13, JoséMaría Aguado 14 , Livio Pagano 13 , Elisa López-Fernández 2,3, Esther Clavero 2,3, Leonardo Potenza 15, Mario Luppi 15, Lucia Moratalla 2,3, Carlos Solano 16, Antonio Sampedro 17, Manuel Cuenca-Estrella 12, Cornelia Lass-Flörl 11 , PCRAGA Study Group 18, Federico Canzian 19 , Juergen Loeffler 10, Yang Li 4,20 , Hermann Einsele 10, Mihai G. Netea 4,21, Lourdes Vázquez 22 , Agostinho Carvalho 8,9 , Manuel Jurado 1,2,3,† and Juan Sainz 1,2,3,23,*,†   Citation: Sánchez-Maldonado, J.M.; Moñiz-Díez, A.; ter Horst, R.; Campa, D.; Cabrera-Serrano, A.J.; Martínez- Bueno, M.; Garrido-Collado, M.d.P.; Hernández-Mohedo, F.; Fernández- Puerta, L.; López-Nevot, M.Á.; et al. Polymorphisms within the TNFSF4 and MAPKAPK2 Loci Influence the Risk of Developing Invasive Aspergillosis: A Two-Stage Case Control Study in the Context of the aspBIOmics Consortium. J. Fungi 2021,7, 4. https:// dx.doi.org/10.3390/jof7010004 Received: 22 November 2020 Accepted: 17 December 2020 Published: 23 December 2020 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This articleisanopen access article distributed under the terms and conditions of the CreativeCommonsAttribution(CCBY) license(https://creativecommons.org/ licenses/by/4.0/). 1Genomic Oncology Area, GENYO, Centre for Genomics and Oncological Research, Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, 18016 Granada, Spain; [email protected] (J.M.S.-M.); [email protected] (A.M.-D.); [email protected] (A.J.C.-S.); [email protected] (M.J.) 2Hematology Department, Virgen de las Nieves University Hospital, 18012 Granada, Spain; [email protected] (M.d.P.G.-C.); [email protected] (F.H.-M.); [email protected] (L.F.-P.); [email protected] (P.A.G.-S.); [email protected] (E.L.-F.); [email protected] (E.C.); [email protected] (L.M.) 3Instituto de Investigación Biosanitaria de Granada (ibs.GRANADA), Complejo Hospitales Universitarios de Granada/Universidad de Granada, 18016 Granada, Spain 4Department of Internal Medicine, Radboud Center for Infectious Diseases, Radboud University Nijmegen Medical Center, 6500HB Nijmegen, The Netherlands; [email protected].ac.at (R.t.H.); [email protected] (Y.L.); [email protected] (M.G.N.) 5Department of Genetics, University of Pisa, 56128 Pisa, Italy; [email protected] 6Genomic Medicine Area, GENYO, Centre for Genomics and Oncological Research, Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, 18016 Granada, Spain; [email protected] 7 Immunology Department, Virgen de las Nieves University Hospital, 18012 Granada, Spain; [email protected] 8Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, 4710-057 Braga, Portugal; [email protected] (C.C.); [email protected] (A.C.) 9ICVS/3B’s-PT Government Associate Laboratory, 4710-057 Braga/Guimarães, Portugal 10 Hematology Department, Universitätsklinikum Würzburg, Medizinische Klinik II, 97080 Würzburg, Germany; [email protected] (J.S.); [email protected] (J.L.); [email protected] (H.E.) 11 Division of Hygiene and Medical Microbiology, Medical University of Innsbruck, 6020 Innsbruck, Austria; [email protected] (M.L.); [email protected] (C.L.-F.) 12 Mycology Reference Laboratory, Centro Nacional de Microbiología, Instituto de Salud Carlos III, 28222 Madrid, Spain; [email protected] (L.A.-F.); [email protected] (M.C.-E.) 13 Istituto di Ematologia, UniversitàCattolica del S. Cuore, 00168 Rome, Italy; [email protected] (L.F.); [email protected] (L.P.) 14 Unit of Infectious Diseases, Hospital Universitario “12 de Octubre”, Instituto de Investigación Hospital “12 de Octubre” (i+12), 28041 Madrid, Spain; [email protected] 15 Department of Medical and Surgical Sciences, University of Modena and Reggio Emilia, AOU Policlinico, 41125 Modena, Italy; [email protected] (L.P.); [email protected] (M.L.) 16 Hematology Department, Hospital Clínico Universitario-INCLIVA, University of Valencia, 46010 Valencia, Spain; [email protected] 17 Department of Microbiology, Virgen de las Nieves University Hospital, 18012 Granada, Spain; [email protected] 18 Polymerase Chain Reaction/Aspergillus Galactomannan Antigen Study Group (PCRAGA Study Group), 37007 Salamanca, Spain; [email protected] J. Fungi 2021,7, 4. https://dx.doi.org/10.3390/jof7010004 https://www.mdpi.com/journal/jof J. Fungi 2021,7, 4 2 of 17 19 Genomic Epidemiology Group, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; [email protected] 20 Centre for Individualised Infection Medicine (CiiM) & TWINCORE, Joint Ventures between the Helmholtz-Centre for Infection Research (HZI) and the Hannover Medical School (MHH), 30625 Hannover, Germany 21 Department for Immunology & Metabolism, Life and Medical Sciences Institute (LIMES), University of Bonn, 53115 Bonn, Germany 22 Hematology Department, University Hospital of Salamanca, 37007 Salamanca, Spain; [email protected] 23 Department of Medicine, University of Granada, 18016 Granada, Spain *Correspondence: [email protected]; Tel.: +34-958-715500; Fax: +34-958-637071 † These authors contributed equally to this work. Abstract: Here, we assessed whether 36 single nucleotide polymorphisms (SNPs) within the TNFSF4 and MAPKAPK2 loci influence the risk of developing invasive aspergillosis (IA). We conducted a twostage case control study including 911 high-risk patients diagnosed with hematological malignancies that were ascertained through the aspBIOmics consortium. The meta-analysis of the discovery and replication populations revealed that carriers of the TNFSF4 rs7526628T/T genotype had a significantly increased risk of developing IA (p= 0.00022). We also found that carriers of the TNFSF4 rs7526628T allele showed decreased serum levels of TNFSF14 protein (p= 0.0027), and that their macrophages had a decreased fungicidal activity (p= 0.048). In addition, we observed that each copy of the MAPKAPK2 rs12137965G allele increased the risk of IA by 60% (p= 0.0017), whereas each copy of the MAPKAPK2 rs17013271T allele was estimated to decrease the risk of developing the disease (p= 0.0029). Mechanistically, we found that carriers of the risk MAPKAPK2 rs12137965G allele showed increased numbers of CD38+IgM-IgD- plasmablasts in blood (p= 0.00086), whereas those harboring two copies of the allele had decreased serum concentrations of thymic stromal lymphopoietin (p= 0.00097). Finally, we also found that carriers of the protective MAPKAPK2 rs17013271T allele had decreased numbers of CD27-IgM-IgD- B cells (p= 0.00087) and significantly lower numbers of CD14+ and CD14+CD16- cells (p= 0.00018 and 0.00023). Altogether, these results suggest a role of the TNFSF4 and MAPKAPK2 genes in determining IA risk. Keywords: invasive aspergillosis; TNFSF4;MAPKAPK2; genetic susceptibility; B cells; monocytes; serum biomarkers; TSLP; TNFSF14 1. Introduction Invasive aspergillosis (IA) is the second-most common opportunistic mycosis, and frequently affects allogenic stem cell and solid transplantation hosts [ 1 ], but also hematological patients receiving myeloablative therapies and critically ill patients [ 2 , 3 ]. Epidemiological studies have demonstrated that, despite the advent of new antifungal agents, IA incidence remains unacceptably high and it is associated with poor prognosis [ 4 ]. Although the molecular mechanisms underlying host defense against invasive fungal infections have been extensively studied during recent years [ 5 ], the etiopathogenesis of this opportunistic infection is still poorly understood, which hampers the development of new and effective therapeutic strategies. A large number of candidate gene association studies developed over the last decade have also shown that genetic determinants influence both the susceptibility and outcome of the infection [ 6 ]. However, most of these studies have been focused on pathogen-recognition receptors (PRRs) (PTX3,Dectin-1,Dectin-2, DC-SIGN,TLRs,NOD,MelLec,MBL) [ 7 – 14 ], cytokines (IFNG,IL1 gene cluster, IL6,IL8, IL10,IL12B) [ 15 – 17 ], chemokines (CXCL10) [ 18 ], and cell surface receptors (TNFR1,TNFR2, CX3CR1) [ 19 – 23 ], whereas only a few studies have addressed the role of intracellular signaling downstream of PRRs [ 22 , 24 , 25 ], effector T lineage factors, or master regulators of cytokine biosynthesis [26]. Several investigations have demonstrated that TNFSF4 (also known as OX40L) expression is upregulated in Aspergillus fumigatus sensitized mice [ 27 ]. TNFSF4 mediates adhesion J. Fungi 2021,7, 4 3 of 17 of activated T cells to endothelial cells during the infection, which is a key process to induce the expression of leukocyte adhesion molecules (E-selectin, VCAM-1) and secretion of proinflammatory cytokines [ 28 ]. In addition, it has been reported that, after activation by thymic stromal lymphopoietin (TSLP), the ligation of TNFSF4 during the interaction of T and dendritic cells (DCs) is a crucial process for clonal expansion of antigen-specific T-cells and generation of memory T cells in response to A. fumigatus [ 29 ]. On the other hand, it has been reported that MAPKAPK2, a downstream substrate of the p38MAPK (also known as MK2), is a key factor in the modulation of cytokine production during infection by phosphorylating and inactivating the mRNA-destabilizing and translation-inhibiting protein tristetraprolin (TTP) [ 30 ]. In addition, it has been reported that MAPKAPK2 protein is implicated in the modulation of LPS-induced macrophage activation and acute lung injury by regulating let-7e miRNA [ 31 ] and likely by inhibiting TTP that controls the posttranscriptional repression of cytokine biosynthesis in macrophages [32]. Considering the above-reported findings, we decided to investigate whether 36 single nucleotide polymorphisms (SNPs) within the TNFSF4 and MAPKAPK2 loci could influence the risk of developing IA by modulating immune responses and/or immune cell populations. For that purpose, we conducted a two-stage candidate gene association study including 991 hematological patients, of whom 164 were diagnosed with proven or probable IA. In order to shed light into the molecular mechanisms underlying the most interesting associations, we also investigated the role of the TNFSF4 and MAPKPAK2 variants in modulating immune responses against Aspergillus fumigatus conidia. Furthermore, we evaluated whether TNFSF4 and MAPKPAK2 SNPs correlated with a panel of 103 serological and plasmatic inflammatory proteins, 7 serum steroid hormones, and counts of 91 blood-derived immune cell populations. Given the key role of macrophages in the host defense against A. fumigatus, we also evaluated whether selected SNPs could correlate with fungicidal activity of macrophages in an independent cohort of 108 healthy blood donors. 2. Methods 2.1. Discovery and Replication Populations A total of 911 hematological patients at high risk of IA were included in this two-stage case control study. All patients were allotransplanted or diagnosed with medullar aplasia, acute myeloid leukemia, or acute lymphoid leukemia receiving intensive chemotherapy regimens. The discovery population consisted of 423 European hematological patients, 67 diagnosed with proven or probable IA and 356 disease-matched patients without any sign of infection and lacking pulmonary infiltrate for a period of, at least, 12 months. Patients diagnosed with possible IA (unreliable diagnosis) were excluded from the study. The replication population included 488 disease-matched patients, 97 of those diagnosed with proven or probable IA. Both discovery and replication populations were ascertained through the aspBIOmics consortium (www.aspbiomics.eu) that aims to identify and to characterize new biomarkers for an effective management of IA using a broad range of approaches including genetic association studies but also RNA, DNA, and proteome analysis of A. fumigatus and multiple-ELISA assays and transcriptome analysis of immune cells ex vivo. Demographic and clinical characteristics of the hematological patients included in the discovery and replication cohorts have been reported in detail elsewhere [22,24,33]. The ethical committees of all participating centers and hospitals approved the study: Virgen de las Nieves University hospital (0702/12, Granada, Spain), University Hospital of Salamanca (NCT01742026, Salamanca, Spain), Clinic University Hospital of Valencia (NCT01742026, Valencia, Spain), Centro Nacional de Microbiología (NCT01742026, Madrid, Spain), University of Würzburg (173/11, Würzburg, Germany), Medical University of Innsbruck (UN4529 and 04/2014, Innsbruck, Austria), UniversitàCattolica del S. Cuore (0029458/16 and 0003932/17, Rome, Italy), and University of Modena and Reggio Emilia (2629/16, Modena, Italy). Approval for the functional genomics studies was obtained from the Arnhem-Nijmegen Ethical Committee (42561.091.12, Nijmegen, The Netherlands) and from the Ethics Subcommittees for Life and Health Sciences of the University of Minho J. Fungi 2021,7, 4 4 of 17 (SECVS, 014/2015, Braga, Portugal), and the National Commission for the Protection of Data (1950/015, Braga, Portugal). In accordance with the Declaration of Helsinki, all participants provided their written informed consent to participate in the study. Proven and probable IA cases were diagnosed by experienced physicians and pathologists according to the 2008 EORTC/MSG criteria [ 34 ] and all centers used galactomannan as microbiological criteria for IA diagnosis. 2.2. DNA Extraction, SNP Selection, Genotyping, and Quality Control Genomic DNA from hematological patients at high risk of IA was extracted from saliva or blood samples using the Oragene ® -DNA Self-Collection kit (Oragene) or the QIAamp DNA Blood Mini kit (Valencia, CA, EEUU) according to manufacturer’s instructions. Common tagging SNPs (MAF > 5% and r 2≥ 0.8) were selected for the MAPKAPK2 and TNFSF4 loci using Haploview according to the Gabriel et al. method [ 35 ]. Given the large size of the TNFSF4 gene and the high number of tagging SNPs found, we decided to select for genotyping only those tagging TNFSF4 SNPs with a predicted biological function according to the data publicly available in the integrated Regulome database (www.regulomedb.org/), and eQTL browsers (www.gtexportal.org/home/ and https://genenetwork.nl/bloodeqtlbrowser/). A total of 36 SNPs were selected for genotyping in the discovery and replication cohorts (Supplementary Table S1). Genotyping of selected SNPs was performed using KASP ® and Taqman ® probes according to manufacturer’s instructions (LGC Genomics, Hoddesdon, UK). For quality control, ~5% of DNA samples were randomly included as duplicates and concordance between duplicate samples was ≥99.0%. 2.3. Hardy–Weinberg Equilibrium, Genetic Association Analysis, and Meta-Analysis Deviation from Hardy–Weinberg equilibrium (HWE) was tested in controls (non-IA cases) by chi-square ( χ2 ), and logistic regression adjusted for age, sex, and country of origin was used to assess the associations of the TNFSF4 and MAPKAPK2 polymorphisms with IA risk assuming log-additive, dominant, and recessive models of inheritance. Those SNPs with the lowest p-value in the discovery population according to each genetic model were advanced for replication and meta-analysis of the two populations using a fixed effect model was performed to validate the association observed. Correction for multiple testing was performed using the Bonferroni method but also considering the two inheritance models tested (the log-additive/dominant that showed collinearity and the recessive model). According to this strategy, the significant threshold for the meta-analysis was set to 0.000694 (0.05/36SNPs/2models). Overall statistical power was calculated using Quanto (v.12.4) assuming a log-additive model and a baseline risk of 3% for hematological patients [36]. 2.4. Linkage Disequilibrium (LD) and Haplotype Analysis Haplotype frequencies were estimated using the expectation–maximization (EM) algorithm and haplotypes were reconstructed using SNPtool and Haploview and block structures were determined according to the method of Gabriel et al. [ 35 ]. Haplotype association analysis was performed using the haplo.stats package and association estimates were adjusted for age, sex, and country of origin. 2.5. Cell Isolation and Differentiation and Functional Analysis of the TNFSF4 and MAPKAPK2 Variants With the aim of determining whether those SNPs associated with IA risk had a role in modulating immune responses, we performed in vitro stimulatory experiments and we measured cytokine production (IFN γ , IL1Ra, IL1 β , IL6, IL8, IL10, TNF α , IL17, and IL22) after stimulation of peripheral blood mononuclear cells (PBMCs), whole blood, or monocyte-derived macrophages (MDM) from 408 healthy subjects of the 500FG from the Human Functional Genomics Project (HFGP) with Aspergillus fumigatus conidia. Given the impact of steroid hormones in modulating immune responses, we also evaluated the J. Fungi 2021,7, 4 5 of 17 correlation of SNPs with serum levels of 7 steroid hormones (androstenedione, cortisol, 11-deoxy-cortisol, 17-hydroxy progesterone, progesterone, testosterone, and 25 hydroxy vitamin D3) in a subset of the HFGP subjects without hormonal replacement therapy or oral contraceptives (n= 280). After log transformation, correlation between SNPs and cytokine expression quantitative trait loci (cQTLs) or serum steroid hormone levels was evaluated using linear regression adjusted for age and sex in R (http://www.r-project.org/). Significance thresholds were set to 0.00185 and 0.0024 (0.05/3SNPs/9cytokines-7hormones) for cQTL and steroid hormone analysis, respectively. 2.6. Correlation between TNFSF4 and MAPKAPK2 Polymorphisms and Cell Counts of 91 Blood-Derived Immune Cell Populations and Serum/Plasmatic Proteomic Profile We also investigated whether TNFSF4 and MAPKAPK2 polymorphisms had an impact on blood cell counts by analyzing a set of 91 manually annotated immune cell populations and genotype data from the HFGP cohort that included 408 healthy subjects (Supplementary Table S2). Cell populations were measured by 10-color flow cytometry (Navios flow cytometer, Beckman Coulter, Miami, Florida, USA) after blood sampling (2–3 h) and cell count analysis was performed using the Kaluza software (Beckman Coulter, v.1.3). In order to reduce inter-experimental noise and increase statistical power cell count, analysis was performed by calculating parental and grandparental percentages, which were defined as the percentage of a certain cell type within the subpopulation of cells from which it was isolated [ 37 ]. Detailed laboratory protocols for cell isolation, reagents, gating, and flow cytometry analysis have been reported elsewhere [ 38 ] and the accession number for the raw flow cytometry data and analyzed data files are available upon request to the authors (http://hfgp.bbmri.nl). A proteomic analysis was also performed in serum and plasma samples from the HFGP cohort. Circulating proteins were measured using the commercial Olink ® Inflammation panel (Olink, Uppsala, Sweden) that resulted in the measurement of 103 different biomarkers (Supplementary Table S3). Protein levels were expressed on a log2- scale as normalized protein expression values, and normalized using bridging samples to correct for batch variation. Correlation between SNPs and protein levels or blood cell counts was evaluated using linear regression adjusted for age and sex in R. Considering the number of proteins (n= 103), cell populations (n= 91), and polymorphisms (n= 3) tested, p-values of 0.00016 and 0.000183 were set as significant thresholds for the proteomic and cell-level variation analysis. 2.7. Assessment of Fungicidal Activity Finally, we also analyzed the impact of selected SNPs on fungicidal activity in human monocyte-derived macrophages (MDM) from an independent cohort including 108 healthy subjects. For that purpose, human MDM were infected with live A. fumigatus conidia at an effector-to-target ratio of 1:10 for 1 h. After removing the non-ingested conidia, MDM were allowed to kill internalized conidia for 2 h. To measure their fungicidal ability, MDM were lysed by quickly freezing at − 80 ◦ C and thawing at 37 ◦ C (releasing ingested conidia) and cell lysates were mixed thoroughly, and serial dilutions were made in phosphate buffered solution (PBS) and plated on Sabouraud dextrose agar. Following a 2-day incubation, the number of colony-forming units (CFU) was enumerated and the percentage of CFU inhibition was calculated. CFU counts of conidia treated in the same experimental conditions but in the absence of macrophages were used as controls. Detailed information about fungicidal experiments has been previously reported in detail [ 22 , 24 , 33 ]. Statistical significance of in vitro fungicidal experiments was evaluated using an unpaired t-test with or without Welch’s correction or Mann–Whitney U test. A p ≤ 0.05 was considered significant (Prism v6.0). 2.8. In Silico Functional Analysis Besides the functional analysis of the TNFSF4 and MAPKAPK2 polymorphisms reported above, we also used the HaploReg SNP annotation tool to further investigate the functional consequences of each specific variant (http://www.broadinstitute.org/ J. Fungi 2021,7, 4 6 of 17 mammals/haploreg/haploreg.php). Finally, we assessed whether any of the potentially interesting markers correlated with mRNA expression levels of their respective genes using data from the GTex portal (www.gtexportal.org/home/). 3. Results 3.1. Characteristics of Study Subjects Overall, this two-stage case control study included 911 hematological patients at high risk of IA. Of those, a total of 164 patients were diagnosed with proven or probable IA. Demographic and clinical characteristics of the hematological patients included are summarized in Table 1. Briefly, IA and non-IA groups had a similar age and underlying disease distribution in both the discovery and replication cohorts. In addition, both IA and non-IA groups showed a similar proportion of patients who underwent allogeneic stem cell transplantation in both study cohorts, as shown in Table 1. Nonetheless, IA was more frequently found in men than in women (p= 0.001) and, as expected, the use of antifungal prophylaxis was less frequent among those patients diagnosed with IA (53.85% vs. 66.79%, p= 0.008; Table 1). 3.2. Association Analysis and Functional Impact of TNFSF4 and MAPKAPK2 Polymorphisms All SNPs analyzed were in HWE (p< 0.001) in the control group (non-IA cases). In the discovery population, logistic regression analyses adjusted for age, sex, and country of origin revealed that each copy of the TNFSF4 rs4357565C ,TNFSF4 rs7526628T ,MAPKAPK2 rs12126682G ,MAPKAPK2 rs11119267C ,MAPKAPK2 rs12123706T , and MAPKAPK2 rs12137965G alleles increased the risk of developing IA by 0.5 to 2-fold (OR = 1.47–2.06), whereas each copy of the MAPKAPK2 rs17013271T and MAPKAPK2 rs4845123A alleles decreased the risk of infection (OR = 0.48 and 0.59; Table 2). Interestingly, we observed that most of these SNPs also impacted on IA risk according to other models of inheritance (dominant or recessive), which reinforced the idea of a true effect of the TNFSF4 and MAPKAPK2 loci in modulating disease risk. Considering these potentially interesting findings, we decided to advance for replication those TNFSF4 and MAPKAPK2 SNPs that showed significant results in the discovery population. Importantly, the meta-analysis of the discovery and replication cohorts confirmed a consistent association of the TNFSF4 rs7526628T/T genotype with an increased risk of developing IA that remained statistically significant after correction for multiple testing (OR Meta-recessive = 2.25, 95% CI 1.46–3.46, p= 0.00022; Table 3). Even though it did not survive multiple testing correction, we also found that carriers of the TNFSF4 rs7526628T allele had an increased risk of developing the infection, which indicated that a single copy of the risk allele might be sufficient to drive disease susceptibility (OR Meta-dominant = 1.52, 95% CI 1.17–1.96, p= 0.0015; Table 3). In support of the hypothesis suggesting a role of the TNFSF4 in modulating IA risk, the proteomic serum analysis of the 500FG cohort from the HFGP showed that carriers of the TNFSF4 rs7526628T allele had decreased serum concentration of TNFSF14 protein (p= 0.0027; Figure 1A), a membrane-bound protein transiently expressed on activated T cells, natural killer (NK) cells, and immature dendritic cells that is involved in promoting proinflammatory responses in multiple contexts and induces T cell proliferation and lung tissue remodeling. In line with these findings, we also found in an independent cohort of healthy donors that macrophages from carriers of the TNFSF4 rs7526628T allele showed a decreased fungicidal activity against A. fumigatus conidia when compared with those harboring the most common TNFSF4 rs7526628C/C genotype (P Dominant = 0.048; Figure 1B ). Although we did not find any positive correlation between this SNP and cytokine production (IFN γ , IL1Ra, IL1 β , IL6, IL8, IL10, TNF α , IL17, and IL22) after stimulation of peripheral blood mononuclear cells (PBMCs) with A. fumigatus conidia, these results together with our genetic findings pointed to a weak but still functional effect of the TNFSF4 rs7526628 SNP that might influence the killing activity of macrophages and TNFSF14-mediated T cell proliferation and lung tissue remodeling. J. Fungi 2021,7, 4 7 of 17 Table 1. Baseline and clinical characteristic of patients with or without invasive aspergillosis (IA). Discovery Population Overall (n= 423) IA patients (n= 67) non-IA patients (n= 356) pvalue Demographic variables Age (Average ±SD) 52.35 ±15.37 53.00 ±12.69 52.23 ±15.82 0.663 Sex ratio (male/female) 1.11 (222/200) 1.79 (43/24) 1.02 (179/176) 0.037 Hematological disease AML 328 (77.54) 49 (73.13) 279 (78.37) 0.346 ALL 58 (13.71) 14 (20.90) 44 (12.36) 0.062 other 37 (08.75) 4 (05.97) 33 (09.27) 0.380 allo-SCT 137 (32.39) 20 (29.85) 117 (32.87) 0.629 Ever received prophylaxis †244 (64.89) 22 (43.13) 222 (68.31) 0.0004 Replication population Overall (n= 488) IA patients (n= 97) non-IA patients (n= 391) pvalue Demographic variables Age (Average ±SD) 50.90 ±17.12 51.42 ±15.72 50.76 ±17.44 0.734 Sex ratio (male/female) 1.32 (278/210) 2.13 (66/31) 1.18 (212/179) 0.014 Hematological disease AML 317 (64.96) 63 (65.95) 254 (64.96) 0.998 ALL 55 (11.27) 10 (10.31) 45 (11.51) 0.738 other 116 (23.77) 24 (24.74) 92 (23.53) 0.802 allo-SCT 251 (51.43) 54 (55.67) 197 (50.38) 0.351 Ever received prophylaxis †193 (64.12) 41 (62.12) 152 (64.68) 0.702 Overall population Overall (n= 911) IA patients (n= 164) non-IA patients (n= 747) pvalue Demographic variables Age (Average ±SD) 51.57 ±16.35 52.07 ±14.58 51.46 ±16.71 0.637 Sex ratio (male/female) 1.22 (500/410) 1.98 (109/55) 1.10 (391/355) 0.001 Hematological disease AML 645 (70.80) 112 (68.29) 533 (71.35) 0.435 ALL 113 (12.40) 24 (14.63) 89 (11.91) 0.339 other 153 (16.80) 28 (17.07) 125 (16.74) 0.916 allo-SCT 388 (42.59) 74 (45.12) 314 (42.03) 0.469 Ever received prophylaxis †437 (64.55) 63 (53.85) 374 (66.79) 0.008 Abbreviations: allo-SCT: Allogeneic stem cell transplantation, AML: acute myeloid leukemia, ALL: acute lymphoid leukemia. p ≤ 0.05 was considered significant and shown in bold. † Prophylaxis status was available in 677 patients, 376 subjects from the discovery cohort (51 IA and 325 non-IA patients) and 301 subjects from the replication cohort (66 IA and 235 non-IA patients). Percentage was calculated according to the number of subjects having prophylaxis information. J. Fungi 2021, 7, x FOR PEER REVIEW 8 of 17 Figure 1. Correlation between the TNFSF4rs7526628 SNP and serum levels of TNFSF14 (pg/mL) (A) and fungicidal activity (%) of monocyte-derived macrophages (B). Grey line represents the mean with error bars (standard deviation). Table 2. Association analysis of selected SNPs within the TNFSF4 and MAPKAPK2 loci and IA risk in the discovery cohort. SNP_rsID Gene Risk Allele OR (95% CI) δ p OR (95% CI) Ϯ p OR (95% CI) ¥ p rs10489269 TNFSF4 A 1.23 (0.56–2.70) 0.61 1.08 (0.47–2.51) 0.86 NA (NA-NA) NA rs7518045 TNFSF4 G 1.08 (0.54–2.15) 0.83 0.91 (0.41–1.98) 0.80 NA (NA-NA) NA rs10489266 TNFSF4 G 0.56 (0.27–1.17) 0.10 0.58 (0.27–1.26) 0.15 NA (NA-NA) NA rs2205959 TNFSF4 G 0.85 (0.57–1.27) 0.44 1.11 (0.61–2.00) 0.73 0.46 (0.20–1.09) 0.058 rs61828280 TNFSF4 G 0.63 (0.30–1.32) 0.20 0.66 (0.30–1.42) 0.27 NA (NA-NA) NA rs4916320 TNFSF4 G 0.76 (0.40–1.44) 0.38 0.77 (0.40–1.49) 0.43 NA (NA-NA) NA rs4357565 TNFSF4 C 1.73 (1.13–2.63) 0.011 2.13 (1.21–3.74) 0.0077 1.69 (0.67–4.24) 0.28 rs1342038 TNFSF4 A 0.68 (0.44–1.03) 0.065 0.73 (0.41–1.30) 0.29 0.40 (0.16–1.02) 0.056 rs947505 TNFSF4 A 1.17 (0.68–2.01) 0.58 1.07 (0.60–1.92) 0.82 4.17 (0.62–28.3) 0.17 rs56307807 TNFSF4 C 0.95 (0.45–2.02) 0.90 0.85 (0.38–1.94) 0.70 4.32 (0.26–72.4) 0.33 rs6425219 TNFSF4 T 1.08 (0.72–1.63) 0.71 1.07 (0.62–1.87) 0.80 1.19 (0.51–2.76) 0.69 rs1418191 TNFSF4 C 1.47 (0.85–2.54) 0.18 1.63 (0.88–3.00) 0.13 0.87 (0.09–8.34) 0.90 rs1578624 TNFSF4 C 0.86 (0.58–1.27) 0.44 0.73 (0.41–1.29) 0.29 0.97 (0.49–1.92) 0.92 rs7526628 TNFSF4 T 1.72 (1.16–2.55) 0.0072 1.87 (1.02–3.42) 0.038 2.27 (1.22–4.20) 0.0068 rs7549074 TNFSF4 C 1.43 (0.90–2.26) 0.14 1.34 (0.77–2.31) 0.30 2.95 (0.90–9.66) 0.089 rs6700269 TNFSF4 T 1.74 (0.97–3.12) 0.075 1.94 (0.9–3.77) 0.059 1.56 (0.17–14.2) 0.71 rs12140760 TNFSF4 A 0.83 (0.56–1.21) 0.33 0.79 (0.45–1.37) 0.40 0.75 (0.35–1.58) 0.44 rs17013271 DYRK3 T 0.48 (0.32–0.73) 0.0004 0.48 (0.28–0.83) 0.0087 0.18 (0.05–0.59) 0.0005 rs6540512 DYRK3 A 1.29 (078–2.11) 0.32 1.47 (0.83–2.58) 0.19 0.52 (0.06–4.37) 0.51 rs12126682 DYRK3 G 1.47 (1.01–2.14) 0.044 1.62 (0.89–2.97) 0.11 1.77 (0.94–3.34) 0.084 rs4845123 DYRK3 A 0.59 (0.38–0.90) 0.012 0.60 (0.35–1.03) 0.063 0.27 (0.08–0.89) 0.012 rs17435120 DYRK3|MAPKAPK2 G 1.42 (0.98–2.06) 0.065 1.44 (0.80–2.58) 0.21 1.85 (0.98–3.49) 0.065 rs12407425 DYRK3|MAPKAPK2 A 0.85 (0.46–1.58) 0.61 0.81 (0.41–1.59) 0.53 1.29 (0.14–11.8) 0.82 rs11119267 DYRK3|MAPKAPK2 C 1.49 (1.00–2.22) 0.047 2.40 (1.26–4.59) 0.0051 1.07 (0.51–2.25) 0.86 rs12038489 DYRK3|MAPKAPK2 A 1.03 (0.58–1.81) 0.92 1.08 (0.57–2.04) 0.82 0.67 (0.07–6.02) 0.71 rs61815610 MAPKAPK2 T 0.79 (0.41–1.52) 0.47 0.82 (0.42–1.62) 0.56 NA (NA-NA) NA rs10863788 MAPKAPK2 A 0.73 (0.49–1.09) 0.12 0.83 (0.48–1.44) 0.51 0.37 (0.14–0.98) 0.027 rs12123706 MAPKAPK2 T 1.74 (1.12–2.70) 0.014 2.35 (1.35–4.10) 0.0022 0.86 (0.24–3.12) 0.81 rs4072677 MAPKAPK2 G 0.85 (0.58–1.24) 0.39 0.92 (0.52–1.63) 0.77 0.65 (0.31–1.35) 0.23 rs61815626 MAPKAPK2 A 0.35 (0.10–1.17) 0.049 0.34 (0.10–1.18) 0.054 NA (NA-NA) NA rs12030124 MAPKAPK2 T 1.21 (0.72–2.03) 0.48 1.29 (0.72–2.28) 0.40 0. (0.09–6.73) 0.81 rs4256810 MAPKAPK2 C 0.72 (0.46–1.13) 0.14 0.75 (0.44–1.29) 0.29 0.36 (0.08–1.56) 0.76 rs28394820 MAPKAPK2 C 0.80 (0.47–1.35) 0.39 0.88 (0.50–1.55) 0.65 NA (NA-NA) NA rs4073250 MAPKAPK2 T 0.69 (0.43–1.11) 0.12 0.70 (0.39–1.25) 0.22 0.35 (0.08–1.55) 0.11 rs7515374 MAPKAPK2||IL10 C 0.86 (0.58–1.28) 0.46 0.88 (0.50–1.54) 0.66 0.71 (0.58–1.28) 0.46 rs12137965 MAPKAPK2||IL10 G 2.06 (1.34–3.16) 0.0011 3.01 (1.68–5.38) 0.0001 1.38 (0.48–3.95) 0.56 Figure 1. Correlation between the TNFSF4rs7526628 SNP and serum levels of TNFSF14 (pg/mL) (A) and fungicidal activity (%) of monocyte-derived macrophages (B). Grey line represents the mean with error bars (standard deviation). J. Fungi 2021,7, 4 8 of 17 Table 2. Association analysis of selected SNPs within the TNFSF4 and MAPKAPK2 loci and IA risk in the discovery cohort. SNP_rsID Gene Risk Allele OR (95% CI) δpOR (95% CI) J. Fungi 2021, 7, x FOR PEER REVIEW 9 of 17 All analyses were adjusted for age, sex, and country of origin. p < 0.05 in bold. δ Logistic regression analysis assuming a log-additive model of inheritance. Ϯ Logistic regression analysis assuming a dominant model of inheritance. ¥ Logistic regression analysis assuming a recessive model of inheritance. Table 3. Association analysis of the most significant SNPs within TNFSF4 and MAPKAPK2 regions in the meta-analysis of study cohorts. Discovery Population (n = 423) Replication Population (n = 488) Meta-Analysis (n = 911) SNP_rsID Risk Allele OR (95% CI) δ p OR (95% CI) δ p OR (95% CI) δ p PHet rs4357565 C 1.73 (1.13–2.63) 0.011 0.95 (0.64–1.42) 0.81 1.26 (0.94–1.68) 0.118 0.043 rs4357565 C 1.69 (0.67–4.24) ¥ 0.28 1.31 (0.55–3.11) ¥ 0.55 1.48 (0.79–2.78) ¥ 0.227 0.693 rs7526628 T 1.72 (1.16–2.55) 0.0072 1.38 (0.98–1.94) 0.068 1.52 (1.17–1.96) 0.0015 0.41 rs7526628 T 2.27 (1.22–4.20) ¥ 0.0068 2.23 (1.22–4.06) ¥ 0.011 2.25 (1.46–3.46) ¥ 0.0002 2 0.97 rs17013271 T 0.48 (0.32–0.73) 0.0004 0.85 (0.58–1.23) 0.38 0.66 (0.50–0.87) 0.0029 0.05 rs17013271 T 0.48 (0.28–0.83)  0.0087 0.73 (0.44–1.23)  0.24 0.60 (0.41–0.87)  0.0071 0.27 rs12126682 G 1.47 (1.01–2.14) 0.044 0.82 (0.56–1.19) 0.29 1.10 (0.84–1.43) 0.486 0.032 rs12126682 G 1.62 (0.89–2.97)  0.11 0.81 (0.48–1.34)  0.41 1.08 (0.73–1.60) 0.685 0.086 rs11119267 C 1.49 (1.00–2.22) 0.047 1.40 (0.97–2.02) 0.069 1.44 (1.10–1.89) 0.0080 0.822 rs11119267 C 2.40 (1.26–4.59)  0.0051 1.55 (0.86–2.78)  0.13 1.89 (1.22–2.92) 0.0040 0.326 rs12123706 T 1.74 (1.12–2.70) 0.014 0.84 (0.55–1.29) 0.43 1.20 (0.88–1.62) 0.254 0.020 rs12123706 T 2.35 (1.35–4.10)  0.0022 0.88 (0.52–1.48)  0.63 1.40 (0.95–2.04) 0.086 0.012 rs12137965 G 2.06 (1.34–3.16) 0.0011 1.28 (0.85–1.91) 0.24 1.60 (1.19–2.15) 0.0017 0.11 rs12137965 G 3.01 (1.68–5.38)  0.0001 1.26 (0.77–2.08)  0.36 1.81 (1.25–2.65)  0.0019 0.030 All analyses were adjusted for age, sex, and country of origin. p < 0.05 in bold. δ Logistic regression analysis assuming a log-additive model of inheritance. Ϯ Logistic regression analysis assuming a dominant model of inheritance. ¥ Logistic regression analysis assuming a recessive model of inheritance.On the other hand, the meta-analysis of the discovery and replication study cohorts showed that each copy of the MAPKAPK2rs12137965G allele increased the risk of developing IA by 60% (OR- Meta-additive = 1.60, 95% CI 1.19–2.15, p = 0.0017). In line with this finding, we found that carriers of the MAPKAPK2rs11119267C allele had an increased risk of developing the infection (ORMeta-dominant = 1.89, 95% CI 1–22-2.92, p = 0.0040; Table 3) whereas each copy of the MAPKAPK2rs17013271T allele was estimated to decrease the risk of developing IA (ORMeta-additive = 0.66, 95% CI 0.50–0.87, p = 0.0029; Table 3). The effect of the MAPKAPK2rs17013271 SNP on IA risk was also observed when a dominant model of inheritance was assumed (C/C vs. C/T+T/T), which reinforced the role of the MAPKAPK2 gene in modulating IA risk (ORMeta-dominant = 0.60, 95% CI 0.41–0.87, p = 0.0071; Table 3). Although none of these genetic associations remained significant after correction for multiple testing, we found that carriers of the risk MAPKAPK2rs12137965G allele showed increased levels of CD38+IgM-IgD- plasmablasts in blood (p = 0.00086; Figure 2A). In addition, we observed that carriers of the MAPKAPK2rs12137965G/G genotype also showed decreased levels of thymic stromal lymphopoietin (TSLP) in serum (p = 0.00097; Figure 2B). pOR (95% CI) ¥p rs10489269 TNFSF4 A 1.23 (0.56–2.70) 0.61 1.08 (0.47–2.51) 0.86 NA (NA-NA) NA rs7518045 TNFSF4 G 1.08 (0.54–2.15) 0.83 0.91 (0.41–1.98) 0.80 NA (NA-NA) NA rs10489266 TNFSF4 G 0.56 (0.27–1.17) 0.10 0.58 (0.27–1.26) 0.15 NA (NA-NA) NA rs2205959 TNFSF4 G 0.85 (0.57–1.27) 0.44 1.11 (0.61–2.00) 0.73 0.46 (0.20–1.09) 0.058 rs61828280 TNFSF4 G 0.63 (0.30–1.32) 0.20 0.66 (0.30–1.42) 0.27 NA (NA-NA) NA rs4916320 TNFSF4 G 0.76 (0.40–1.44) 0.38 0.77 (0.40–1.49) 0.43 NA (NA-NA) NA rs4357565 TNFSF4 C 1.73 (1.13–2.63) 0.011 2.13 (1.21–3.74) 0.0077 1.69 (0.67–4.24) 0.28 rs1342038 TNFSF4 A 0.68 (0.44–1.03) 0.065 0.73 (0.41–1.30) 0.29 0.40 (0.16–1.02) 0.056 rs947505 TNFSF4 A 1.17 (0.68–2.01) 0.58 1.07 (0.60–1.92) 0.82 4.17 (0.62–28.3) 0.17 rs56307807 TNFSF4 C 0.95 (0.45–2.02) 0.90 0.85 (0.38–1.94) 0.70 4.32 (0.26–72.4) 0.33 rs6425219 TNFSF4 T 1.08 (0.72–1.63) 0.71 1.07 (0.62–1.87) 0.80 1.19 (0.51–2.76) 0.69 rs1418191 TNFSF4 C 1.47 (0.85–2.54) 0.18 1.63 (0.88–3.00) 0.13 0.87 (0.09–8.34) 0.90 rs1578624 TNFSF4 C 0.86 (0.58–1.27) 0.44 0.73 (0.41–1.29) 0.29 0.97 (0.49–1.92) 0.92 rs7526628 TNFSF4 T 1.72 (1.16–2.55) 0.0072 1.87 (1.02–3.42) 0.038 2.27 (1.22–4.20) 0.0068 rs7549074 TNFSF4 C 1.43 (0.90–2.26) 0.14 1.34 (0.77–2.31) 0.30 2.95 (0.90–9.66) 0.089 rs6700269 TNFSF4 T 1.74 (0.97–3.12) 0.075 1.94 (0.9–3.77) 0.059 1.56 (0.17–14.2) 0.71 rs12140760 TNFSF4 A 0.83 (0.56–1.21) 0.33 0.79 (0.45–1.37) 0.40 0.75 (0.35–1.58) 0.44 rs17013271 DYRK3 T 0.48 (0.32–0.73) 0.0004 0.48 (0.28–0.83) 0.0087 0.18 (0.05–0.59) 0.0005 rs6540512 DYRK3 A 1.29 (078–2.11) 0.32 1.47 (0.83–2.58) 0.19 0.52 (0.06–4.37) 0.51 rs12126682 DYRK3 G 1.47 (1.01–2.14) 0.044 1.62 (0.89–2.97) 0.11 1.77 (0.94–3.34) 0.084 rs4845123 DYRK3 A 0.59 (0.38–0.90) 0.012 0.60 (0.35–1.03) 0.063 0.27 (0.08–0.89) 0.012 rs17435120 DYRK3|MAPKAPK2 G 1.42 (0.98–2.06) 0.065 1.44 (0.80–2.58) 0.21 1.85 (0.98–3.49) 0.065 rs12407425 DYRK3|MAPKAPK2 A 0.85 (0.46–1.58) 0.61 0.81 (0.41–1.59) 0.53 1.29 (0.14–11.8) 0.82 rs11119267 DYRK3|MAPKAPK2 C1.49 (1.00–2.22) 0.047 2.40 (1.26–4.59) 0.0051 1.07 (0.51–2.25) 0.86 rs12038489 DYRK3|MAPKAPK2 A 1.03 (0.58–1.81) 0.92 1.08 (0.57–2.04) 0.82 0.67 (0.07–6.02) 0.71 J. Fungi 2021,7, 4 9 of 17 Table 2. Cont. SNP_rsID Gene Risk Allele OR (95% CI) δpOR (95% CI) J. Fungi 2021, 7, x FOR PEER REVIEW 9 of 17 All analyses were adjusted for age, sex, and country of origin. p < 0.05 in bold. δ Logistic regression analysis assuming a log-additive model of inheritance. Ϯ Logistic regression analysis assuming a dominant model of inheritance. ¥ Logistic regression analysis assuming a recessive model of inheritance. Table 3. Association analysis of the most significant SNPs within TNFSF4 and MAPKAPK2 regions in the meta-analysis of study cohorts. Discovery Population (n = 423) Replication Population (n = 488) Meta-Analysis (n = 911) SNP_rsID Risk Allele OR (95% CI) δ p OR (95% CI) δ p OR (95% CI) δ p PHet rs4357565 C 1.73 (1.13–2.63) 0.011 0.95 (0.64–1.42) 0.81 1.26 (0.94–1.68) 0.118 0.043 rs4357565 C 1.69 (0.67–4.24) ¥ 0.28 1.31 (0.55–3.11) ¥ 0.55 1.48 (0.79–2.78) ¥ 0.227 0.693 rs7526628 T 1.72 (1.16–2.55) 0.0072 1.38 (0.98–1.94) 0.068 1.52 (1.17–1.96) 0.0015 0.41 rs7526628 T 2.27 (1.22–4.20) ¥ 0.0068 2.23 (1.22–4.06) ¥ 0.011 2.25 (1.46–3.46) ¥ 0.0002 2 0.97 rs17013271 T 0.48 (0.32–0.73) 0.0004 0.85 (0.58–1.23) 0.38 0.66 (0.50–0.87) 0.0029 0.05 rs17013271 T 0.48 (0.28–0.83)  0.0087 0.73 (0.44–1.23)  0.24 0.60 (0.41–0.87)  0.0071 0.27 rs12126682 G 1.47 (1.01–2.14) 0.044 0.82 (0.56–1.19) 0.29 1.10 (0.84–1.43) 0.486 0.032 rs12126682 G 1.62 (0.89–2.97)  0.11 0.81 (0.48–1.34)  0.41 1.08 (0.73–1.60) 0.685 0.086 rs11119267 C 1.49 (1.00–2.22) 0.047 1.40 (0.97–2.02) 0.069 1.44 (1.10–1.89) 0.0080 0.822 rs11119267 C 2.40 (1.26–4.59)  0.0051 1.55 (0.86–2.78)  0.13 1.89 (1.22–2.92) 0.0040 0.326 rs12123706 T 1.74 (1.12–2.70) 0.014 0.84 (0.55–1.29) 0.43 1.20 (0.88–1.62) 0.254 0.020 rs12123706 T 2.35 (1.35–4.10)  0.0022 0.88 (0.52–1.48)  0.63 1.40 (0.95–2.04) 0.086 0.012 rs12137965 G 2.06 (1.34–3.16) 0.0011 1.28 (0.85–1.91) 0.24 1.60 (1.19–2.15) 0.0017 0.11 rs12137965 G 3.01 (1.68–5.38)  0.0001 1.26 (0.77–2.08)  0.36 1.81 (1.25–2.65)  0.0019 0.030 All analyses were adjusted for age, sex, and country of origin. p < 0.05 in bold. δ Logistic regression analysis assuming a log-additive model of inheritance. Ϯ Logistic regression analysis assuming a dominant model of inheritance. ¥ Logistic regression analysis assuming a recessive model of inheritance.On the other hand, the meta-analysis of the discovery and replication study cohorts showed that each copy of the MAPKAPK2rs12137965G allele increased the risk of developing IA by 60% (OR- Meta-additive = 1.60, 95% CI 1.19–2.15, p = 0.0017). In line with this finding, we found that carriers of the MAPKAPK2rs11119267C allele had an increased risk of developing the infection (ORMeta-dominant = 1.89, 95% CI 1–22-2.92, p = 0.0040; Table 3) whereas each copy of the MAPKAPK2rs17013271T allele was estimated to decrease the risk of developing IA (ORMeta-additive = 0.66, 95% CI 0.50–0.87, p = 0.0029; Table 3). The effect of the MAPKAPK2rs17013271 SNP on IA risk was also observed when a dominant model of inheritance was assumed (C/C vs. C/T+T/T), which reinforced the role of the MAPKAPK2 gene in modulating IA risk (ORMeta-dominant = 0.60, 95% CI 0.41–0.87, p = 0.0071; Table 3). Although none of these genetic associations remained significant after correction for multiple testing, we found that carriers of the risk MAPKAPK2rs12137965G allele showed increased levels of CD38+IgM-IgD- plasmablasts in blood (p = 0.00086; Figure 2A). In addition, we observed that carriers of the MAPKAPK2rs12137965G/G genotype also showed decreased levels of thymic stromal lymphopoietin (TSLP) in serum (p = 0.00097; Figure 2B). pOR (95% CI) ¥p rs61815610 MAPKAPK2 T 0.79 (0.41–1.52) 0.47 0.82 (0.42–1.62) 0.56 NA (NA-NA) NA rs10863788 MAPKAPK2 A 0.73 (0.49–1.09) 0.12 0.83 (0.48–1.44) 0.51 0.37 (0.14–0.98) 0.027 rs12123706 MAPKAPK2 T 1.74 (1.12–2.70) 0.014 2.35 (1.35–4.10) 0.0022 0.86 (0.24–3.12) 0.81 rs4072677 MAPKAPK2 G 0.85 (0.58–1.24) 0.39 0.92 (0.52–1.63) 0.77 0.65 (0.31–1.35) 0.23 rs61815626 MAPKAPK2 A 0.35 (0.10–1.17) 0.049 0.34 (0.10–1.18) 0.054 NA (NA-NA) NA rs12030124 MAPKAPK2 T 1.21 (0.72–2.03) 0.48 1.29 (0.72–2.28) 0.40 0. (0.09–6.73) 0.81 rs4256810 MAPKAPK2 C 0.72 (0.46–1.13) 0.14 0.75 (0.44–1.29) 0.29 0.36 (0.08–1.56) 0.76 rs28394820 MAPKAPK2 C 0.80 (0.47–1.35) 0.39 0.88 (0.50–1.55) 0.65 NA (NA-NA) NA rs4073250 MAPKAPK2 T 0.69 (0.43–1.11) 0.12 0.70 (0.39–1.25) 0.22 0.35 (0.08–1.55) 0.11 rs7515374 MAPKAPK2||IL10 C 0.86 (0.58–1.28) 0.46 0.88 (0.50–1.54) 0.66 0.71 (0.58–1.28) 0.46 rs12137965 MAPKAPK2||IL10 G 2.06 (1.34–3.16) 0.0011 3.01 (1.68–5.38) 0.0001 1.38 (0.48–3.95) 0.56 All analyses were adjusted for age, sex, and country of origin. p< 0.05 in bold. δ Logistic regression analysis assuming a log-additive model of inheritance. J. Fungi 2021, 7, x FOR PEER REVIEW 9 of 17 All analyses were adjusted for age, sex, and country of origin. p < 0.05 in bold. δ Logistic regression analysis assuming a log-additive model of inheritance. Ϯ Logistic regression analysis assuming a dominant model of inheritance. ¥ Logistic regression analysis assuming a recessive model of inheritance. Table 3. Association analysis of the most significant SNPs within TNFSF4 and MAPKAPK2 regions in the meta-analysis of study cohorts. Discovery Population (n = 423) Replication Population (n = 488) Meta-Analysis (n = 911) SNP_rsID Risk Allele OR (95% CI) δ p OR (95% CI) δ p OR (95% CI) δ p PHet rs4357565 C 1.73 (1.13–2.63) 0.011 0.95 (0.64–1.42) 0.81 1.26 (0.94–1.68) 0.118 0.043 rs4357565 C 1.69 (0.67–4.24) ¥ 0.28 1.31 (0.55–3.11) ¥ 0.55 1.48 (0.79–2.78) ¥ 0.227 0.693 rs7526628 T 1.72 (1.16–2.55) 0.0072 1.38 (0.98–1.94) 0.068 1.52 (1.17–1.96) 0.0015 0.41 rs7526628 T 2.27 (1.22–4.20) ¥ 0.0068 2.23 (1.22–4.06) ¥ 0.011 2.25 (1.46–3.46) ¥ 0.0002 2 0.97 rs17013271 T 0.48 (0.32–0.73) 0.0004 0.85 (0.58–1.23) 0.38 0.66 (0.50–0.87) 0.0029 0.05 rs17013271 T 0.48 (0.28–0.83)  0.0087 0.73 (0.44–1.23)  0.24 0.60 (0.41–0.87)  0.0071 0.27 rs12126682 G 1.47 (1.01–2.14) 0.044 0.82 (0.56–1.19) 0.29 1.10 (0.84–1.43) 0.486 0.032 rs12126682 G 1.62 (0.89–2.97)  0.11 0.81 (0.48–1.34)  0.41 1.08 (0.73–1.60) 0.685 0.086 rs11119267 C 1.49 (1.00–2.22) 0.047 1.40 (0.97–2.02) 0.069 1.44 (1.10–1.89) 0.0080 0.822 rs11119267 C 2.40 (1.26–4.59)  0.0051 1.55 (0.86–2.78)  0.13 1.89 (1.22–2.92) 0.0040 0.326 rs12123706 T 1.74 (1.12–2.70) 0.014 0.84 (0.55–1.29) 0.43 1.20 (0.88–1.62) 0.254 0.020 rs12123706 T 2.35 (1.35–4.10)  0.0022 0.88 (0.52–1.48)  0.63 1.40 (0.95–2.04) 0.086 0.012 rs12137965 G 2.06 (1.34–3.16) 0.0011 1.28 (0.85–1.91) 0.24 1.60 (1.19–2.15) 0.0017 0.11 rs12137965 G 3.01 (1.68–5.38)  0.0001 1.26 (0.77–2.08)  0.36 1.81 (1.25–2.65)  0.0019 0.030 All analyses were adjusted for age, sex, and country of origin. p < 0.05 in bold. δ Logistic regression analysis assuming a log-additive model of inheritance. Ϯ Logistic regression analysis assuming a dominant model of inheritance. ¥ Logistic regression analysis assuming a recessive model of inheritance.On the other hand, the meta-analysis of the discovery and replication study cohorts showed that each copy of the MAPKAPK2rs12137965G allele increased the risk of developing IA by 60% (OR- Meta-additive = 1.60, 95% CI 1.19–2.15, p = 0.0017). In line with this finding, we found that carriers of the MAPKAPK2rs11119267C allele had an increased risk of developing the infection (ORMeta-dominant = 1.89, 95% CI 1–22-2.92, p = 0.0040; Table 3) whereas each copy of the MAPKAPK2rs17013271T allele was estimated to decrease the risk of developing IA (ORMeta-additive = 0.66, 95% CI 0.50–0.87, p = 0.0029; Table 3). The effect of the MAPKAPK2rs17013271 SNP on IA risk was also observed when a dominant model of inheritance was assumed (C/C vs. C/T+T/T), which reinforced the role of the MAPKAPK2 gene in modulating IA risk (ORMeta-dominant = 0.60, 95% CI 0.41–0.87, p = 0.0071; Table 3). Although none of these genetic associations remained significant after correction for multiple testing, we found that carriers of the risk MAPKAPK2rs12137965G allele showed increased levels of CD38+IgM-IgD- plasmablasts in blood (p = 0.00086; Figure 2A). In addition, we observed that carriers of the MAPKAPK2rs12137965G/G genotype also showed decreased levels of thymic stromal lymphopoietin (TSLP) in serum (p = 0.00097; Figure 2B). Logistic regression analysis assuming a dominant model of inheritance. ¥Logistic regression analysis assuming a recessive model of inheritance. J. Fungi 2021,7, 4 16 of 17 23. Sainz, J.; Salas-Alvarado, I.; Lopez-Fernandez, E.; Olmedo, C.; Comino, A.; Garcia, F.; Blanco, A.; Gomez-Lopera, S.; Oyonarte, S.; Bueno, P.; et al. TNFR1 mRNA expression level and TNFR1 gene polymorphisms are predictive markers for susceptibility to develop invasive pulmonary aspergillosis. Int. J. Immunopathol. Pharmacol. 2010,23, 423–436. [CrossRef] [PubMed] 24. Lupianez, C.B.; Villaescusa, M.T.; Carvalho, A.; Springer, J.; Lackner, M.; Sanchez-Maldonado, J.M.; Canet, L.M.; Cunha, C.; Segura-Catena, J.; Alcazar-Fuoli, L.; et al. Common Genetic Polymorphisms within NFkappaB-Related Genes and the Risk of Developing Invasive Aspergillosis. Front. Microbiol. 2016,7, 1243. [CrossRef] [PubMed] 25. Cunha, C.; Giovannini, G.; Pierini, A.; Bell, A.S.; Sorci, G.; Riuzzi, F.; Donato, R.; Rodrigues, F.; Velardi, A.; Aversa, F.; et al. Genetically-determined hyperfunction of the S100B/RAGE axis is a risk factor for aspergillosis in stem cell transplant recipients. PLoS ONE 2011,6, e27962. [CrossRef] [PubMed] 26. Napolioni, V.; Pariano, M.; Borghi, M.; Oikonomou, V.; Galosi, C.; De Luca, A.; Stincardini, C.; Vacca, C.; Renga, G.; Lucidi, V.; et al. Genetic Polymorphisms Affecting IDO1 or IDO2 Activity Differently Associate With Aspergillosis in Humans. Front. Immunol. 2019,10, 890. [CrossRef] [PubMed] 27. Barrios, C.S.; Johnson, B.D.; Henderson, J.D.; Fink, J.N.; Kelly, K.J.; Kurup, V.P. The costimulatory molecules CD80, CD86 and OX40L are up-regulated in Aspergillus fumigatus sensitized mice. Clin. Exp. Immunol. 2005,142, 242–250. [CrossRef] 28. Chiang, L.Y.; Sheppard, D.C.; Gravelat, F.N.; Patterson, T.F.; Filler, S.G. Aspergillus fumigatus stimulates leukocyte adhesion molecules and cytokine production by endothelial cells in vitro and during invasive pulmonary disease. Infect. Immun. 2008 ,76, 3429–3438. [CrossRef] 29. Sun, L.; Chen, C.; Wu, J.; Dai, C.; Wu, X. TSLP-activated dendritic cells induce T helper type 2 inflammation in Aspergillus fumigatus keratitis. Exp. Eye Res. 2018,171, 120–130. [CrossRef] 30. Menon, M.B.; Gaestel, M. MK2-TNF-Signaling Comes Full Circle. Trends Biochem. Sci. 2018,43, 170–179. [CrossRef] 31. Wu, Y.; He, H.; Ding, Y.; Liu, S.; Zhang, D.; Wang, J.; Jiang, H.; Zhang, D.; Sun, L.; Ye, R.D.; et al. MK2 mediates macrophage activation and acute lung injury by regulating let-7e miRNA. Am. J. Physiol. Lung Cell. Mol. Physiol 2018 ,315, L371–L381. [CrossRef] [PubMed] 32. Ronkina, N.; Shushakova, N.; Tiedje, C.; Yakovleva, T.; Tollenaere, M.A.X.; Scott, A.; Batth, T.S.; Olsen, J.V.; Helmke, A.; Bekker- Jensen, S.H.; et al. The Role of TTP Phosphorylation in the Regulation of Inflammatory Cytokine Production by MK2/3. J. Immunol. 2019,203, 2291–2300. [CrossRef] [PubMed] 33. Lupianez, C.B.; Canet, L.M.; Carvalho, A.; Alcazar-Fuoli, L.; Springer, J.; Lackner, M.; Segura-Catena, J.; Comino, A.; Olmedo, C.; Rios, R.; et al. Polymorphisms in Host Immunity-Modulating Genes and Risk of Invasive Aspergillosis: Results from the AspBIOmics Consortium. Infect. Immun. 2015,84, 643–657. [CrossRef] [PubMed] 34. De Pauw, B.; Walsh, T.J.; Donnelly, J.P.; Stevens, D.A.; Edwards, J.E.; Calandra, T.; Pappas, P.G.; Maertens, J.; Lortholary, O.; Kauffman, C.A.; et al. 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. Clin. Infect. Dis. 2008,46, 1813–1821. [CrossRef] [PubMed] 35. Gabriel, S.B.; Schaffner, S.F.; Nguyen, H.; Moore, J.M.; Roy, J.; Blumenstiel, B.; Higgins, J.; DeFelice, M.; Lochner, A.; Faggart, M.; et al. The structure of haplotype blocks in the human genome. Science 2002,296, 2225–2229. [CrossRef] [PubMed] 36. Vallabhaneni, S.; Benedict, K.; Derado, G.; Mody, R.K. Trends in Hospitalizations Related to Invasive Aspergillosis and Mucormycosis in the United States, 2000–2013. Open Forum. Infect. Dis. 2017,4, ofw268. [CrossRef] 37. Orru, V.; Steri, M.; Sole, G.; Sidore, C.; Virdis, F.; Dei, M.; Lai, S.; Zoledziewska, M.; Busonero, F.; Mulas, A.; et al. Genetic variants regulating immune cell levels in health and disease. Cell 2013,155, 242–256. [CrossRef] 38. Aguirre-Gamboa, R.; Joosten, I.; Urbano, P.C.M.; van der Molen, R.G.; van Rijssen, E.; van Cranenbroek, B.; Oosting, M.; Smeekens, S.; Jaeger, M.; Zorro, M.; et al. Differential Effects of Environmental and Genetic Factors on T and B Cell Immune Traits. Cell Rep. 2016,17, 2474–2487. [CrossRef] 39. Sugamura, K.; Ishii, N.; Weinberg, A.D. Therapeutic targeting of the effector T-cell co-stimulatory molecule OX40. Nat. Rev. Immunol. 2004,4, 420–431. [CrossRef] 40. Murata, K.; Ishii, N.; Takano, H.; Miura, S.; Ndhlovu, L.C.; Nose, M.; Noda, T.; Sugamura, K. Impairment of antigen-presenting cell function in mice lacking expression of OX40 ligand. J. Exp. Med. 2000,191, 365–374. [CrossRef] [PubMed] 41. Takeda, I.; Ine, S.; Killeen, N.; Ndhlovu, L.C.; Murata, K.; Satomi, S.; Sugamura, K.; Ishii, N. Distinct roles for the OX40-OX40 ligand interaction in regulatory and nonregulatory T cells. J. Immunol. 2004,172, 3580–3589. [CrossRef] [PubMed] 42. Jenkins, S.J.; Perona-Wright, G.; Worsley, A.G.; Ishii, N.; MacDonald, A.S. Dendritic cell expression of OX40 ligand acts as a costimulatory, not polarizing, signal for optimal Th2 priming and memory induction in vivo .J. Immunol. 2007 ,179, 3515–3523. [CrossRef] [PubMed] 43. Salek-Ardakani, S.; Song, J.; Halteman, B.S.; Jember, A.G.; Akiba, H.; Yagita, H.; Croft, M. OX40 (CD134) controls memory T helper 2 cells that drive lung inflammation. J. Exp. Med. 2003,198, 315–324. [CrossRef] 44. Oakley, M.S.; Majam, V.; Mahajan, B.; Gerald, N.; Anantharaman, V.; Ward, J.M.; Faucette, L.J.; McCutchan, T.F.; Zheng, H.; Terabe, M.; et al. Pathogenic roles of CD14, galectin-3, and OX40 during experimental cerebral malaria in mice. PLoS ONE 2009 ,4, e6793. [CrossRef] 45. Duffus, K.; Lopez-Isac, E.; Teruel, M.; Simeon, C.P.; Carreria, P.; Ortego-Centeno, N.; Vicente, E.; Worthington, J.; Herrick, A.L.; Martin, J. Association of TNFSF4 (OX40L) polymorphisms with systemic sclerosis-related calcinosis. Rheumatology 2019 ,58, 1299–1301. [CrossRef] [PubMed] J. Fungi 2021,7, 4 17 of 17 46. Deng, Y.; Tsao, B.P. Genetic susceptibility to systemic lupus erythematosus in the genomic era. Nat. Rev. Rheumatol. 2010 ,6, 683–692. [CrossRef] 47. Jindra, P.T.; Conway, S.E.; Ricklefs, S.M.; Porcella, S.F.; Anzick, S.L.; Haagenson, M.; Wang, T.; Spellman, S.; Milford, E.; Kraft, P.; et al. Analysis of a Genetic Polymorphism in the Costimulatory Molecule TNFSF4 with Hematopoietic Stem Cell Transplant Outcomes. Biol. Blood Marrow Transplant. 2016,22, 27–36. [CrossRef] 48. Doherty, T.A.; Soroosh, P.; Khorram, N.; Fukuyama, S.; Rosenthal, P.; Cho, J.Y.; Norris, P.S.; Choi, H.; Scheu, S.; Pfeffer, K.; et al. The tumor necrosis factor family member LIGHT is a target for asthmatic airway remodeling. Nat. Med. 2011 ,17, 596–603. [CrossRef] 49. Herro, R.; Da Silva Antunes, R.; Aguilera, A.R.; Tamada, K.; Croft, M. Tumor necrosis factor superfamily 14 (LIGHT) controls thymic stromal lymphopoietin to drive pulmonary fibrosis. J. Allergy Clin. Immunol. 2015,136, 757–768. [CrossRef] 50. Nguyen, N.L.; Chen, K.; McAleer, J.; Kolls, J.K. Vitamin D regulation of OX40 ligand in immune responses to Aspergillus fumigatus. Infect. Immun. 2013,81, 1510–1519. [CrossRef] 51. Kreindler, J.L.; Steele, C.; Nguyen, N.; Chan, Y.R.; Pilewski, J.M.; Alcorn, J.F.; Vyas, Y.M.; Aujla, S.J.; Finelli, P.; Blanchard, M.; et al. Vitamin D3 attenuates Th2 responses to Aspergillus fumigatus mounted by CD4+ T cells from cystic fibrosis patients with allergic bronchopulmonary aspergillosis. J. Clin. Investig. 2010,120, 3242–3254. [CrossRef] [PubMed] 52. Lee, H.C.; Ziegler, S.F. Inducible expression of the proallergic cytokine thymic stromal lymphopoietin in airway epithelial cells is controlled by NFkappaB. Proc. Natl. Acad. Sci. USA 2007,104, 914–919. [CrossRef] 53. Zhang, K.; Shan, L.; Rahman, M.S.; Unruh, H.; Halayko, A.J.; Gounni, A.S. Constitutive and inducible thymic stromal lymphopoietin expression in human airway smooth muscle cells: Role in chronic obstructive pulmonary disease. Am. J. Physiol. Lung Cell. Mol. Physiol. 2007,293, L375–L382. [CrossRef] [PubMed] 54. Kashyap, M.; Rochman, Y.; Spolski, R.; Samsel, L.; Leonard, W.J. Thymic stromal lymphopoietin is produced by dendritic cells. J. Immunol. 2011,187, 1207–1211. [CrossRef] [PubMed] 55. Lee, K.H.; Cho, K.A.; Kim, J.Y.; Kim, J.Y.; Baek, J.H.; Woo, S.Y.; Kim, J.W. Filaggrin knockdown and Toll-like receptor 3 (TLR3) stimulation enhanced the production of thymic stromal lymphopoietin (TSLP) from epidermal layers. Exp. Dermatol. 2011 ,20, 149–151. [CrossRef] 56. Ni, G.; Chen, Y.; Wu, F.; Zhu, P.; Song, L. NOD2 promotes cell proliferation and inflammatory response by mediating expression of TSLP in human airway smooth muscle cells. Cell Immunol. 2017,312, 35–41. [CrossRef] 57. Qiao, J.; Li, A.; Jin, X. TSLP from RSV-stimulated rat airway epithelial cells activates myeloid dendritic cells. Immunol. Cell Biol. 2011,89, 231–238. [CrossRef] 58. Kato, A.; Favoreto, S., Jr.; Avila, P.C.; Schleimer, R.P. TLR3- and Th2 cytokine-dependent production of thymic stromal lymphopoietin in human airway epithelial cells. J. Immunol. 2007,179, 1080–1087. [CrossRef] 59. Ito, T.; Wang, Y.H.; Duramad, O.; Hori, T.; Delespesse, G.J.; Watanabe, N.; Qin, F.X.; Yao, Z.; Cao, W.; Liu, Y.J. TSLP-activated dendritic cells induce an inflammatory T helper type 2 cell response through OX40 ligand. J. Exp. Med. 2005 ,202, 1213–1223. [CrossRef] 60. Wang, Y.H.; Ito, T.; Wang, Y.H.; Homey, B.; Watanabe, N.; Martin, R.; Barnes, C.J.; McIntyre, B.W.; Gilliet, M.; Kumar, R.; et al. Maintenance and polarization of human TH2 central memory T cells by thymic stromal lymphopoietin-activated dendritic cells. Immunity 2006,24, 827–838. [CrossRef] 61. Jiang, Q.; Su, H.; Knudsen, G.; Helms, W.; Su, L. Delayed functional maturation of natural regulatory T cells in the medulla of postnatal thymus: Role of TSLP. BMC Immunol. 2006,7, 6. [CrossRef] [PubMed] 62. Osborn, M.J.; Ryan, P.L.; Kirchhof, N.; Panoskaltsis-Mortari, A.; Mortari, F.; Tudor, K.S. Overexpression of murine TSLP impairs lymphopoiesis and myelopoiesis. Blood 2004,103, 843–851. [CrossRef] [PubMed] 63. Redhu, N.S.; Saleh, A.; Halayko, A.J.; Ali, A.S.; Gounni, A.S. Essential role of NF-kappaB and AP-1 transcription factors in TNF-alpha-induced TSLP expression in human airway smooth muscle cells. Am. J. Physiol. Lung Cell. Mol. Physiol. 2011 ,300, L479–L485. [CrossRef] [PubMed] 64. Craxton, A.; Shu, G.; Graves, J.D.; Saklatvala, J.; Krebs, E.G.; Clark, E.A. p38 MAPK is required for CD40-induced gene expression and proliferation in B lymphocytes. J. Immunol. 1998,161, 3225–3236. 65. Sutherland, C.L.; Heath, A.W.; Pelech, S.L.; Young, P.R.; Gold, M.R. Differential activation of the ERK, JNK, and p38 mitogenactivated protein kinases by CD40 and the B cell antigen receptor. J. Immunol. 1996,157, 3381–3390. 66. Sreekanth, G.P.; Chuncharunee, A.; Sirimontaporn, A.; Panaampon, J.; Noisakran, S.; Yenchitsomanus, P.T.; Limjindaporn, T. SB203580 Modulates p38 MAPK Signaling and Dengue Virus-Induced Liver Injury by Reducing MAPKAPK2, HSP27, and ATF2 Phosphorylation. PLoS ONE 2016,11, e0149486. [CrossRef] 67. Pan, W.; Zhu, S.; Dai, D.; Liu, Z.; Li, D.; Li, B.; Gagliani, N.; Zheng, Y.; Tang, Y.; Weirauch, M.T.; et al. MiR-125a targets effector programs to stabilize Treg-mediated immune homeostasis. Nat. Commun. 2015,6, 7096. [CrossRef] 68. Swertz, M.A.; Dijkstra, M.; Adamusiak, T.; van der Velde, J.K.; Kanterakis, A.; Roos, E.T.; Lops, J.; Thorisson, G.A.; Arends, D.; Byelas, G.; et al. The MOLGENIS toolkit: Rapid prototyping of biosoftware at the push of a button. BMC Bioinform. 2010 . [CrossRef] 69. Wilkinson, M.D.; Dumontier, M.; Aalbersberg, I.J.; Appleton, G.; Axton, M.; Baak, A.; Blomberg, N.; Boiten, J.W.; da Silva Santos, L.B.; Bourne, P.E.; et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 2016 . [CrossRef]