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Association of Toll-like receptor 7 variants with life-threatening COVID-19 disease in males: findings from a nested case-control study

  1. Chiara Fallerini
  2. Sergio Daga
  3. Stefania Mantovani
  4. Elisa Benetti
  5. Nicola Picchiotti
  6. Daniela Francisci
  7. Francesco Paciosi
  8. Elisabetta Schiaroli
  9. Margherita Baldassarri
  10. Francesca Fava
  11. Maria Palmieri
  12. Serena Ludovisi
  13. Francesco Castelli
  14. Eugenia Quiros-Roldan
  15. Massimo Vaghi
  16. Stefano Rusconi
  17. Matteo Siano
  18. Maria Bandini
  19. Ottavia Spiga
  20. Katia Capitani
  21. Simone Furini
  22. Francesca Mari
  23. GEN-COVID Multicenter Study
  24. Alessandra Renieri  Is a corresponding author
  25. Mario U Mondelli
  26. Elisa Frullanti
  1. Medical Genetics, University of Siena, Italy
  2. Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Italy
  3. Division of Infectious Diseases and Immunology, Department of Medical Sciences and Infectious Diseases, Fondazione IRCCS Policlinico San Matteo, Italy
  4. Department of Mathematics, University of Pavia, Italy
  5. University of Siena, DIISM-SAILAB, Italy
  6. Infectious Diseases Clinic, Department of Medicine 2, Azienda Ospedaliera di Perugia and University of Perugia, Santa Maria Hospital, Italy
  7. Infectious Diseases Clinic, "Santa Maria" Hospital, University of Perugia, Italy
  8. Genetica Medica, Azienda Ospedaliero-Universitaria Senese, Italy
  9. Department of Internal Medicine and Therapeutics, University of Pavia, Italy
  10. Department of Infectious and Tropical Diseases, University of Brescia and ASST Spedali Civili Hospital, Italy
  11. Chirurgia Vascolare, Ospedale Maggiore di Crema, Italy
  12. Department of Biomedical and Clinical Sciences Luigi Sacco, University of Milan, Italy
  13. III Infectious Diseases Unit, ASST-FBF-Sacco, Italy
  14. Department of Preventive Medicine, Azienda USL Toscana Sud Est, Italy
  15. Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Italy
  16. Molecular Mechanisms of Oncogenesis, ISPRO Core Research Laboratory (CRL), Italy
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Cite this article as: eLife 2021;10:e67569 doi: 10.7554/eLife.67569

Abstract

Background:

Recently, loss-of-function variants in TLR7 were identified in two families in which COVID-19 segregates like an X-linked recessive disorder environmentally conditioned by SARS-CoV-2. We investigated whether the two families represent the tip of the iceberg of a subset of COVID-19 male patients.

Methods:

This is a nested case-control study in which we compared male participants with extreme phenotype selected from the Italian GEN-COVID cohort of SARS-CoV-2-infected participants (<60 y, 79 severe cases versus 77 control cases). We applied the LASSO Logistic Regression analysis, considering only rare variants on young male subsets with extreme phenotype, picking up TLR7 as the most important susceptibility gene.

Results:

Overall, we found TLR7 deleterious variants in 2.1% of severely affected males and in none of the asymptomatic participants. The functional gene expression profile analysis demonstrated a reduction in TLR7-related gene expression in patients compared with controls demonstrating an impairment in type I and II IFN responses.

Conclusions:

Young males with TLR7 loss-of-function variants and severe COVID-19 represent a subset of male patients contributing to disease susceptibility in up to 2% of severe COVID-19.

Funding:

Funded by private donors for the Host Genetics Research Project, the Intesa San Paolo for 2020 charity fund, and the Host Genetics Initiative.

Clinical trial number:

NCT04549831.

Introduction

Coronavirus disease 2019 (COVID-19), a potentially severe systemic disease caused by coronavirus SARS-CoV-2, is characterized by a highly heterogeneous phenotypic presentation, with the large majority of infected individuals experiencing only mild or no symptoms. However, severe cases can rapidly evolve toward a critical respiratory distress syndrome and multiple organ failure (Wu and McGoogan, 2020). COVID-19 still represents an enormous challenge for the world's healthcare systems almost 1 year after the first appearance in December 2019 in Wuhan, Huanan, Hubei Province of China. Although older age and the presence of cardiovascular or metabolic comorbidities have been identified as risk factors predisposing to severe disease (Hägg et al., 2020), these factors alone do not fully explain differences in severity (Stokes et al., 2020). Stokes EK et al. reported that male patients show more severe clinical manifestations than females with a statistically significant (p<0.00001) higher prevalence of hospitalizations (16% versus 12%), ICU admissions (3% versus 2%), and deaths (6% versus 5%) (Stokes et al., 2020). These results are in line with other reports indicating that gender may influence disease outcome (Garg et al., 2020; Goodman et al., 2020).

These findings suggest a role of host predisposing genetic factors in the pathogenesis of the disease, which may be responsible for different clinical outcomes as a result of different antiviral defense mechanisms as well as specific receptor permissiveness to virus and immunogenicity.

Recent evidence suggests a fundamental role of interferon genes in modulating immunity to SARS-CoV-2; in particular, rare variants have recently been identified in the interferon type I pathway that are responsible for inborn errors of immunity in a small proportion of patients and auto-antibodies against type I interferon genes in up to 10% of severe COVID-19 cases (Zhang et al., 2020; Bastard et al., 2020).

Toll-like receptors (TLRs) are crucial components in the initiation of innate immune responses to a variety of pathogens, causing the production of pro-inflammatory cytokines (TNF-α, IL-1, and IL-6) and type I and II Interferons (IFNs), that are responsible for innate antiviral responses. In particular, the innate immunity is very sensitive in detecting potential pathogens, activating downstream signaling to induce transcription factors in the nucleus, promoting synthesis and release of type I and type II IFNs in addition to a number of other proinflammatory cytokines, and leading to a severe cytokine release syndrome which may be associated with a fatal outcome. Interestingly, among the different TLRs, TLR7 recognizes several single-stranded RNA viruses including SARS-CoV-2 (Poulas et al., 2020). We previously showed that another RNA virus, hepatitis C virus (HCV), is able to inhibit CD4 T cell function via Toll-like receptor 7 (TLR7) (Mele et al., 2017). Recently, van der Made et al., 2020 have reported two independent families in which COVID-19 segregates like an X-linked recessive monogenic disorder conditioned by SARS-CoV-2 as an environmental factor.

Here, we performed a nested case-control study within our prospectively recruited GEN-COVID cohort with the aim to determine whether the two families described by van der Made et al. represent an ultra-rare situation or the tip of the iceberg of a larger subset of young male patients.

Materials and methods

Patients and samples

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A subset of 156 young (<60 years) male COVID-19 patients was selected from the Italian GEN-COVID cohort of 1,178 SARS-CoV-2-infected participants (https://sites.google.com/dbm.unisi.it/gen-covid) (Daga et al., 2021). The study (GEN-COVID) was consistent with Institutional guidelines and approved by the University Hospital (Azienda Ospedaliero-Universitaria Senese) Ethical Review Board, Siena, Italy (Prot n. 16929, dated March 16, 2020). We performed a nested case-control study (STREGA reporting guideline was used to support reporting of this study). Cases were selected according to the following inclusion criteria: i. male gender; ii. young age (<60 years); iii endotracheal intubation or CPAP/biPAP ventilation (79 participants). As controls, 77 participants were selected using the sole criterion of being oligo-asymptomatic not requiring hospitalization. Cases and controls represented the extreme phenotypic presentations of the GEN-COVID cohort. Exclusion criteria for both cases and controls were: i. SARS-CoV-2 infection not confirmed by PCR; ii. non-white ethnicity. Materials and methods details are listed in the Online Repository. A similar cohort from the second wave, composed of 83 young male COVID-19 patients, was used to expand the cohort.

Statistical methods

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We adopted the LASSO logistic regression, one of the most common Machine Learning algorithms for classification, that provides a feature selection method within the classification task able to enforce both the sparsity and the interpretability of the results (Tibshirani, 1996). In fact, the coefficients of the logistic regression model are directly related to the importance of the corresponding features, and LASSO regularization shrinks close to zero the coefficients of features that are not relevant in predicting the response, reducing overfitting and giving immediate interpretability of the model predictions in terms of few feature importance.

The principal components analysis (PCA) was applied prior to the LASSO logistic regression in order to remove samples that were clear outliers with respect to the first three principal components from the following analyses (deviating more than five standard deviations from the average).

A 10-fold cross-validation method was applied in order to test the performances. It provides the partition of the dataset into 10 batches, then nine batches are exploited for the training of the LASSO logistic regression and the remaining batch as a test, by repeating this procedure 10 times. The performance metrics are averaged on the 10 testing sets in order to avoid overfitting. The confusion matrix is built by summing up the predictions of the 10 testing folds. During the fitting procedure, the class unbalancing is tackled by penalizing the misclassification of the minority class with a multiplicative factor inversely proportional to the class frequencies.

In order to evaluate the significance of the association between TLR7 variants and COVID severity, the Fisher’s Exact Test was used.

For the quantitative PCR assay, the fold changes in mRNA expression level per gene were compared between the individual patients and controls using an unpaired t test on the log-transformed fold changes. p Values < 0.05 were considered statistically significant.

In vitro peripheral blood mononuclear cell (PBMC) experiments

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Peripheral blood mononuclear cells (PBMC) were isolated by Ficoll‐Hypaque (GE Healthcare Bio-Sciences AB) density gradient centrifugation as previously described (Mantovani et al., 2019). 5 × 105 PBMC from COVID-19 patients 6 months after recovery and six unaffected male and female controls were stimulated for 4 hr with the TLR7 agonist imiquimod at 5 μg/mL or cell culture medium. Total RNA extraction was performed with RNeasy Plus Mini kit and gDNA eliminator mini spin columns (QIAGEN, Hilden, Germany), following the manufacturer's instructions. First-strand cDNA was synthesized from total RNA using High-Capacity cDNA Reverse Transcription Kit following the manufacturer's instructions (Thermo Fisher Scientific, Waltham, Massachusetts, United States). The Advanced Universal SYBR Green Supermix (BioRad, Redmond, WA, United States) was used. All reactions were performed in triplicates using the CFX96 Real-Time machine detection system (BioRad, Redmond, WA, United States) and each sample was amplified in duplicate. The following primers were used:

TLR7Fw Primer5’-CATCAAGAGGCTGCAGATTAAA-3’
Rv Primer5’-GAAAAGATGTTGTTGGCCTCA-3’
IFN-γFw Primer5’-TGACCAGAGCATCCAAAAGA-3’
Rv Primer5’-CTCTTCGACCTCGAAACAGC-3’
IRF7Fw Primer5’-CCATCTTCGACTTCAGAGTCTTC-3’
Rv Primer5’-TCTAGGTGCACTCGGCACAG-3’
ISG15Fw Primer5’-GACAAATGCGACGAACCTCT-3’
Rv Primer5’-GAACAGGTCGTCCTGCACAC-3’
IFN-aFw Primer5’-GACTCCATCTTGGCTGTGA-3’
Rv Primer5’-TGATTTCTGCTCTGACAACCT-3’
HRPT1Fw Primer5’-TGACACTGGCAAAACAATGCA-3’
Rv Primer5’-GGTCCTTTTCACCAGCAAGCT-3’

A total of 2.5 × 105 PBMC from COVID-19 patients and healthy controls were maintained in RPMI-1640 supplemented with 10% of FCS, 1% antibiotic antimycotic solution, 1% L-glutamine and 1% Sodium Pyruvate (Sigma-Aldrich, St. Louis, MO, USA) and stimulated in vitro for 4 hr with Lipopolysaccharide (LPS) at 1 μg/ml or cell culture medium and the Protein Transport Inhibitor GolgiStop (BD Biosciences, San Diego, CA, USA). After washing, PBMC were stained for surface cell marker using mouse anti-CD14PerCP-Cy5.5 (BD Biosciences) and anti-CD3BV605 (BD Biosciences) monoclonal antibody (mAb). Cells were fixed with BD Cytofix/Cytoperm and permeabilized with the BD Perm/Wash buffer (BD Biosciences) according to the manufacturer's instructions, in the presence of anti-IL6BV421 (BD Biosciences) mAb. Ex-vivo TLR7 intracellular expression was evaluated in PBMC from patients and controls by flow cytometry. 2,5 × 105 PBMC were stained for surface markers using anti-CD19BV605, anti-CD14PerCP-Cy5.5 and anti-CD3BV421 (BD Biosciences) mAbs. Cells were fixed and permeabilized in the presence of anti-TLR7 Alexa Fluor 488 (R and D System, Minneapolis, MN, USA) mAb or isotype control as described above. After staining cells were washed, immediately fixed in CellFix solution (BD Biosciences) and analysed. Cell acquisition was performed on a 12-color FACSCelesta (BD Biosciences, San Diego, CA, USA) instrument. Data analysis was performed with the Kaluza 2.1 software (Beckman Coulter).

Protein stability prediction

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The protein structure of Human Toll Like Receptor, UniProtKB ID Q9NYK1 [https://www.uniprot.org/uniprot/Q9NYK1], was obtained by homology modeling using Swiss Model tool (Waterhouse et al., 2018). The selected template protein with 97% of sequence identity was the Crystal structure of monkey TLR7 with PDB ID 5GMF [https://www.rcsb.org/structure/5GMF]. The two Val to Asp missense mutations were analysed by using different protein stability predictors like Polyphen-2 (Adzhubei et al., 2010), SIFT (Ng and Henikoff, 2003), and DynaMut (Rodrigues et al., 2018).

Transfection experiments of TLR7 variants

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PCR based site-directed mutagenesis was performed in pUNO-hTLR7 plasmid (Invivogen), kindly provided by Ugo D’Oro (GSK Vaccines, Siena, Italy) (Iavarone et al., 2011), to generate specific plasmids for each TLR7 variant, including those considered neutral (mutagenic primers available on request).

All point mutations except for p.Arg920Lys were confirmed by Sanger sequencing. HEK293 cells were maintained in DMEM supplemented with 10% FBS, 1% L-Glutamine and 1% penicillin/streptomycin at 37°C with 5% CO2. Transient transfections were performed using Lipofectamine 2000 (Invitrogen) according to manufacturer’s instructions: 3 × 105 cell/well were seeded the day before, and then transfected with 2 μg of DNA. After 24 hr, the cells were stimulated with Imiquimod at 1 μg/ml for 4 hr and then total RNA was extracted with RNeasy Mini Kit (QIAGEN, Hilden, Germany). For each sample, cDNA was synthesized from 1 μg of total RNA using QantiTect Reverse Transcription kit (QIAGEN, Hilden, Germany) according to manufacturer’s instructions. The expression of IFN-a in stimulated and unstimulated cells was evaluated by qRT-PCR using the same procedure as described for PBMCs.

Results and discussion

We applied LASSO logistic regression analysis, after correcting for Principal Components, to a synthetic boolean representation of the entire set of genes of the X chromosome on the extreme phenotypic ends of the male subset of the Italian GEN-COVID cohort (https://sites.google.com/dbm.unisi.it/gen-covid) (Daga et al., 2021). The GEN-COVID study was consistent with Institutional guidelines and approved by the University Hospital (Azienda Ospedaliero-Universitaria Senese) Ethical Review Board, Siena, Italy (Prot n. 16929, dated March 16, 2020). Only rare variants (≤1% in European Non-Finnish population) were considered in the boolean representation: the gene was set to one if it included at least a missense, splicing, or loss-of-function rare variant, and 0 otherwise. Fisher Exact test was then used for the specific data validation.

Toll-like receptor 7 (TLR7) was picked up as one of the most important susceptibility genes by LASSO Logistic Regression analysis (Figure 1). We then queried the COVID-19 section of the Network of Italian Genome (NIG) database (http://www.nig.cineca.it/, specifically, http://nigdb.cineca.it) that houses the entire GEN-COVID cohort represented by more than 1000 WES data of COVID-19 patients and SARS-CoV-2 infected asymptomatic participants (Bastard et al., 2020). By selecting for young (<60 year-old) males, we obtained rare (MAF ≤ 1%) TLR7 missense variants predicted to impact on protein function (CADD > 12.28) in 5 out of 79 male patients (6.3%) with life-threatening COVID-19 (hospitalized intubated and hospitalized CPAP/BiPAP) and in none of the 77 SARS-CoV2 infected oligo-asymptomatic male participants.

Rare TLR7 variants and association with COVID-19.

LASSO logistic regression on boolean representation of rare variants of all genes of the X chromosome is presented. TLR7 is picked up by LASSO logistic regression as one of the most important genes on the X chr (Panel A). The LASSO logistic regression model provides an embedded feature selection method within the binary classification tasks (male patients with life-threatening COVID-19 vs infected asymptomatic male participants). The upward histograms (positive weights) reflect a susceptible behavior of the features to the target COVID-19, whereas the downward histograms (negative weights) a protective action. Panel B represents the cross-validation accuracy score for the grid of LASSO regularization parameters; the error bar is given by the standard deviation of the score within the 10 folds; the red circle (1.26) corresponds to the parameter chosen for the fitting procedure. Performances are evaluated through the confusion matrix of the aggregated predictions in the 10 folds of the cross-validation (Panel C) and with the boxplot (Panel D) of accuracy (60% average value), precision (59%), sensitivity (75%), specificity (43%), and ROC-AUC score (68%). The box extends from the Q1 to Q3 quartile, with a line at the median (Q2) and a triangle for the average.

We then investigated a similar cohort coming from the Italian second wave composed of male patients under 60 years of age without comorbidities (56 cases and 27 controls) was used to expand the cohort. All participants were white European. We found a TLR7 variant in one of 56 cases (1.7%) and in none of 27 controls. Overall, the association between the presence of TLR7 rare variants and severe COVID-19 was significant (p=0.037 by Fisher Exact test, Table 1).

Table 1
Fisher exact test of the overall combined cohorts in young males (<60 years).
Clinical categoryN. wild-type variants (97.84%)N. pathological variants (2.15%)Total
Severely affected males1296135
Asymptomatic males1040104
Total2336239 (Grand Total)
  1. p-value=0.0037.

We then investigated the presence of TLR7 rare variants in the entire male cohort of 561 COVID-19 patients (261 cases and 300 controls) regardless of age. We found TLR7 rare missense variants in three additional patients over 60 years of age, including two cases (who shared the p.Ala1032Thr variant) and one control (C1), bearing the p.Val222Asp variant, predicted to have a low impact on protein function (CADD of 5.36) (Table 2).

Table 2
TLR7 variants in severely affected Italian males -all ages- (cases).
Nucleotide changeAmino acid changedbSNPCADDExAC_
NFE
Function*N. of patientsClinical category†AgeCohortPatient ID
 c.901T>CSer301Pro-26.4N/ALOF1346ItalianP3
 c.2759G>AArg920Lysrs18968181116.520.0002LOF‡1449ItalianP6
 c.3094G>AAla1032Thrrs14724466222.30.0006LOF2365/66ItalianP7/P8
 c.655G>AVal219Ilers14931402312.280.0003HYPO1432ItalianP1
 c.863C>TAla288Valrs20014665815.370.000012Neutral1357ItalianP2
 c.1343C>TAla448Valrs574378113.080.00465Neutral2353/58ItalianP4/P5
  1. CADD, Combined Annotation Dependent Depletion; ExAC, Exome Aggregation Consortium; NFE, Non-Finnish European;

    *Function: HYPO, hypomorphic; LOF, loss-of-function;

  2. †Clinical category: 4, Hospitalized and intubated; 3, Hospitalized and CPAP-BiPAP and high-flows oxygen treated; 2, Hospitalized and treated with conventional oxygen support only; 1, Hospitalized without respiratory support; 0, Not hospitalized oligo/asymptomatic individuals.

    ‡based on in silico prediction.

In order to functionally link the presence of the identified TLR7 missense variants and the effect on the downstream type I IFN-signaling, we performed a gene expression profile analysis in peripheral blood mononuclear cells (PBMCs) isolated from patients following recovery, after stimulation with the TLR7 agonist imiquimod, as reported by van der Made et al., 2020. To explore all TLR7 variants identified, we examined PBMCs from the control and all cases except P4 and P6 because them were not available. However, P4 and P5 shared the same variant. This analysis showed a statistically significant decrease of all TLR7-related genes for two variants (Ser301Pro and Ala1032Thr) identified in cases P3, P7, and P8 compared with healthy controls (Ctl) demonstrating a complete impairment of TLR7 signaling pathways in response to TLR7 stimulation (Figure 2, panel A and Table 2). The variant Val219Ile (P1) showed a hypomorphic effect determining a statistically significant decrease in mRNA levels only for IRF7 (directly activated by TLR7) and IFN-γ (Figure 2, panel A). Two Ala to Val variants identified in severely affected patients, Ala288Val and Ala448Val, were functionally neutral, that is not predicted to impair the TLR7 signaling pathways. This was confirmed by biochemical and structural analysis on the crystal structure of TLR7 protein (https://www.uniprot.org/uniprot/Q9NYK1). The prediction performed with different computational approaches showed both variants as benign with no effects on structural stabilization. Interestingly, the p.Val222Asp variant (C1) proved to be functionally neutral, in keeping with it being identified in the control and not in cases (Figure 2, panel A).

Gene expression profile analysis in peripheral blood mononuclear cells (PBMCs) and in HEK293 cells transfected with the functional variants after stimulation with a TLR7 agonist for 4 hr.

(A) 5 × 105 PBMCs from COVID-19 patients and six unaffected male and female controls were stimulated for 4 hr with the TLR7 agonist imiquimod at 5 μg/mL or cell culture medium. Quantitative PCR assay was performed and the 2-ΔΔCt calculated using HPRT1 as housekeeping gene. Fold change in mRNA expression of TLR7 and type 1 IFN-related genes ISG15, IRF7, IFN-ɑ and IFN-γ induced by TLR7 agonist imiquimod was compared with cell culture medium. Ctl indicates healthy controls (white bar); C1, the asymptomatic mutated control (diagonal lines bar); P2, P5, cases with neutral variants (vertical lines bar); P1, P3, P8, P7 cases with functional variants (gray bar) (as in Table 2). (B) Histograms of intracellularly expressed TLR7 protein in HEK293 cells transfected with the different TLR7 plasmids. (C) Gene expression profile analysis of IFN-ɑ in transfected cells after stimulation with the TLR7 agonist imiquimod. WT indicates cells transfected with WT TLR7 plasmid. Quantitative PCR assay was performed and the 2-ΔΔCt calculated using HPRT1 as housekeeping gene. Fold change in mRNA expression induced by imiquimod was compared with cell culture medium. Error bars show standard deviation. p values were calculated for the reduction using an unpaired t test: *p<0.05; **p<0.01; ***p<0.001; ****p<0.0001.

TLR7 expression was evaluated in monocytes and B cells from patients and healthy controls by flow cytometry. Patients and controls expressed the TLR7 protein at the intracellular level. The functional capacity of PBMCs was evaluated after stimulation with the TLR4 agonist lipopolysaccharide (LPS). Of note, LPS-induced production of IL6 by monocytes was similar in patients and controls (data not shown).

In order to validate the functional effect of TLR7 variants, we have performed transfection experiments in HEK293 cells, cloning a dedicated TLR7 plasmid for each of them. Transfection experiments were performed in HEK293 cells that do not express endogenous TLR7 (Chehadeh and Alkhabbaz, 2013) and expression of TLR7 protein was examined by flow cytometry 24 hr after transfection, showing expression of TLR7 protein at the intracellular level in all cases (Figure 2, panel B). We then evaluated the expression of IFN-a in imiquimod stimulated and unstimulated cells by qRT-PCR employing the same assay described for PBMCs, confirming the results obtained in PBMCs for the screened variants (Figure 2, panel C).

Segregation analysis was available for two cases, P3 and P8 (Figure 3). In the two pedigrees, the disease nicely segregated as an X-linked disorder conditioned by environmental factors, that is SARS-CoV-2 (Figure 3, panel B). This was also supported by functional analysis on all TLR7-related genes (Figure 3, panel A). For example, expression profile analysis for IRF7 gene in male mutated patient P8 confirmed a statistically significant reduction compared to the wild-type brother (Figure 3, panel A). Of note, only the infected mutated male had severe COVID-19, whereas the infected not mutated brother (II-2 of P8) was asymptomatic (Figure 3, panel C).

Segregation analysis.

Fold change in mRNA expression following Imiquimod stimulation of TLR7 itself and its main effectors, IRF7, ISG15, IFN-alpha, and IFN-gamma is shown in Panel A. Gray columns represent individuals harboring the TLR7 variant and black columns are severely affected SARS-CoV-2 cases. Pedigree (Panel B) and respective segregation of TLR7 variant and COVID-19 status (Panel C) are also shown. Squares represent male family members; circles, females. Individuals infected by SARS-CoV-2 are indicated by a virus cartoon close to the individual symbol ().

Our results showed that the two families reported by van der Made et al., 2020. with loss-of-function variants in males with severe COVID-19 with a mean age of 26 years represent a subset of COVID-19 male patients. Specifically, missense deleterious variants in the X-linked recessive TLR7 gene may represent the cause of disease susceptibility to COVID-19 in up to 2% of severely affected young male cases (3/135, 2.2%). The same result was obtained for the entire male cohort, irrespective of age, with TLR7 deleterious variants in 5/261 cases (1.9%). Since not all identified variants were functionally effective, the true percentage could be slightly lower in young males. Overall, males with rare missense variants shown here developed COVID-19 at a mean age of 56.5 years, considerably later than 26 years, in agreement with a predicted smaller impact on the protein than the loss of function deleterious variants reported by van der Made et al., 2020. Similarly, the identified rare missense TLR7 variants impaired the mRNA expression of TLR7 as well as the downstream pathway. The observation reported here may lead to consider TLR7 screening in severely affected male patients in order to start personalized interferon treatment for those with this specific genetic disorder.

Data availability

Sequencing data have been deposited in CINECA through http://www.nig.cineca.it/, specifically, http://nigdb.cineca.it., in the COVID-19 section through http://nigdb.cineca.it./registration/login.php. There are no restrictions on data access. Only registration is needed.

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Decision letter

  1. Frank L van de Veerdonk
    Reviewing Editor; University Medical Center, Netherlands
  2. Jos WM van der Meer
    Senior Editor; University Medical Centre, Netherlands

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

The authors provide solid evidence for the role of Toll-like receptor 7 in host defense against SARS Coronavirus-2. Based on the initial observation by Van der Made et al. (JAMA 324:1-22, 2020) that mutations in TLR-7 may lead to severe and even lethal COVID in young males, the authors found missense deleterious TLR-7 mutations in some 2 % of severe COVID male patients. In these patients there is a severe impairment of the Type-I and type-II interferon responses.

Decision letter after peer review:

Congratulations, we are pleased to inform you that your article, "Association of Toll-like receptor 7 variants with life-threatening COVID-19 disease in males", has been accepted for publication in eLife.

https://doi.org/10.7554/eLife.67569.sa1

Author response

[Editors' note: we include below the reviews that the authors received from another journal, along with the authors’ responses.]

Editor's specific comments:

Please see the reviewers' comments below.

Reviewer #1: Major comments:

The authors should include a section on Statistical Methods that includes

  • a brief mention of Fisher's exact test for Table 1

  • a brief rationale for the use of LASSO logistic regression (with a reference

  • a brief explanation of why principal components analysis was applied prior to the LASSO logistic regression

  • a description of the cross-validation method and construction of the confusion matrix (with a reference)

Added in the Online Repository file.

Reviewer #2:

Fallerini et al. study TLR7 variants in males with mild compared with severe COVID19 infections in an Italian and Spanish cohort.

Comments

The methods suggest that 1,178 patients were included in the analysis, while it was 156 Italians and 122 Spanish. The fact that all were white European should be noted. Refine. The PBMC analysis of gene expression should also be noted.

A subset of 156 <60-year old male COVID-19 patients was selected from the Italian GENCOVID cohort of 1,178 SARS-CoV-2-infected subjects. We refined it in the text. We have now specified that all individuals were of European Caucasoid ethnicity in the Abstract and in the text as well as for PBMC analysis of gene expression.

Capsule summary – this section should be rewritten to highlight the key results in a quantitative format. Introductory statements should be removed.

Agreed and modified as suggested.

"strong predisposing factors". This statement should be toned down and be more precise as only 4% of the affected cohort had this variant.

As suggested by the reviewer, we have toned down the statement in the Capsule Summary.

Quantitate the relative risk of severe disease in males compared with females

More information has been provided to quantitate the relative risk of severe disease in males compared with females.

Reference 4. This reference is from 2004, when SARS-CoV-2 was not around. Amend.

Agreed and the reference replaced, updating the paragraph in the text.

The fact that details of methods are in an online repository should be stated.

Done.

5 out of 79 patients – add percentage

As suggested by the reviewer, the percentage (6.32%) has been added.

Round 0.0366 to 0.04.

Done.

Describe results in more detail/quantitatively

Agreed and rephrased as suggested.

Round 57.5 to 58 years

Done.

Table 1 – change "Marginal Row totals" to "total" in column and row headings. Round 0.0366 to 0.04.

Done (Table 1).

Table 2 – Describe in footer what is meant by "clinical category" 3 and 4.

Done (Table 2).

Figure 1 Refine some sentences in this figure legend focusing on the facts pertaining to the figure, without interpretation. Panel B currently comes after panel C – suggest reordering; or removing as it's significant is unclear.

Agreed and modified as suggested by the reviewer. More specifically, legend to Figure 1 is now more factual, and figure panels have been reordered and coordinated with the legend.

Figure 1 – Panel B might be removed. Panel E- it is unclear which line refers to which – revise.

As suggested by the reviewer, Figure 1 has been refined with the reordering of the panels and the exclusion of Panel E. Panel C (ex Panel B), reporting the confusion matrix, could be useful for the evaluation of the number of false negative/false positive of the classification.

Figure 2 – focus on comparison between affected patients and Ctl, not C1 – amend in all panels. For clarity, leave out comparison between C1 and patients – just comment in text.

We agree on the changes proposed for Figure 2 and modified statistics accordingly.

Figure 3 – table – reorder with generation I members first, then generation II members and finally generation III members. With females in the pedigree, clarify whether or not they required any hospital treatment.

As suggested by the reviewer, the generations in Figure 3 have been rearranged. We have specified in the text that females did not require hospital admission.

Reviewer #3:

In this manuscript, the authors report a higher frequency of rare TLR7 variants in younger (<60 years) males with life-threatening COVID-19 than in a control group with asymptomatic or oligosymptomatic infection. PBMC from three patients with TLR7 variants and life-threatening disease, from one subject with TLR7 variant and oligosymptomatic infection and from 4 healthy controls were challenged in vitro with imiquimod (a TLR7 agonist), and impaired expression of IRF7 was demonstrated in PBMC from patients with life-threatening disease. The authors conclude that deleterious TLR7 variants may account for up to 4% of severe disease in male subjects.

The study expands on a recent observation of two families in which COVID-19 segregated as an Xlinked recessive trait conditioned by SARS-CoV-2 infection.

Overall, the study is interesting. However, some of the conclusions are overstated. Some methodological aspects need to be better defined. the organization of the manuscript should be improved, and reference to recent important findings by other groups on monogenic variants associated with life-threatening COVOD-19 must be added.

Major comments:

1) Some of the conclusions raised by the authors are overstated. In particular, in Figure 2, IRF7 and IFN- are the only transcripts that appear to be differentially expressed between patients with life threatening disease and healthy controls. For TLR7, this difference exists only between controls and P8, and for ISG15 between the healthy controls vs. P3 and P8.

As suggested by the reviewer, we have now tested all TLR7 variants and modified the conclusions according to our recent findings. Thus we showed a significant impairment of TLR7 signalling pathway in the Ser301Pro, His630Tyr, and Ala1032Thr variants.

In the text, the authors emphasize the difference in the expression of these genes between patients with life-threatening disease and the oligosymptomatic SARS-CoV-2 infected patients, but this is not relevant if there is no difference versus healthy controls. Furthermore, there are technical and methodological weaknesses that need to be addressed. In particular, expression of TLR7 protein should be examined by flow cytometry.

We thank the reviewer for raising this important point. Indeed, we have partially replied above to this question by reviewer 2 and we modified Figure 2 according to his/her comment. It is important to emphasize that functional experiments were carried out in those patients from whom PBMC were available for further experiments. Expression of TLR7 protein has now been examined by flow cytometry in monocytes and B cells from patients and healthy controls showing that both expressed the TLR7 protein at the intracellular level.

To formally prove that these TLR7 variants are loss of function (or hylomorphic), transfection experiments should be performed in TLR7-deficient cells, and response to the TLR7 agonist should be examined.

We thank the reviewer for this suggestion. We believe that transfections are really important when primary cells cannot be retrieved from mutated patients. The availability of PBMC carrying the different TLR7 variants identified in this study would make transfections redundant. Notably, in this revised version, we were able to expand the number of variants analyzed.

Finally, it is not known at what point in the course of the disease PBMC from the patients were collected.

PBMC from all patients were collected approximately 6 months after recovery.

An impaired response may also reflect the specific functional status of the cells in that particular moment of the infection. This is why the transfection experiments mentioned above are particularly important.

To evaluate the functional status of the cell, we stimulated PBMC from patients and healthy controls with the TLR4 agonist lipopolysaccharide (LPS). The intracellular production of IL6 was evaluated in monocytes. The frequencies of IL6+CD14+ cells were comparable in patients and healthy controls demonstrating that the cells of the patients were functionally active.

2) Important recent advances in the genetic basis of COVID-19 have been neglected, perhaps because the manuscript was submitted around the time when these discoveries were made publicly available. In any case, the recent description of deleterious variants in genes involved in type I IFN synthesis or signaling to these molecules (Zhang et al., Science 2020) should be cited and commented

As suggested by the reviewer, a proper section and the relative reference to the Zhang Q. paper has been added.

3) The manuscript suffers from some organizational deficiencies. Figure 1 is cited only once in the text, but it is composed of multiple panels which are not properly mentioned and commented. The legend to this figure reported first on panel A, then on panel C (before mentioning panel B).

We thank the reviewer for the suggestion. As also requested by reviewer 2, Figure 1 has been refined and panels reordered with the exclusion of ROC curves (Panel E). Legend to Figure 1 is now clearer and coherent with the order of panels.

Minor comments:

4) Table 2 reports on the Clinical Category of the patients, however no mention is made in the text in regard to how were the clinical categories defined

As also suggested by reviewer 2, we have added a footer to Table 2 carrying a detailed description of the clinical categories and of all abbreviations listed in the table.

5) Patients from Spain were included to expand the number of patients studied. Mention of approval from the local Institutional Review Board(s) is missing for this patient population.

We have now mentioned the Spanish Institutional Review Board approval in the Online Repository file and in the main text.

6) Segregation of the disease in the family of P6 is shown in Figure 3. However, these are only circumstantial supportive data (due to the fact that only few individuals from this family were infected with SARS-CoV-2). As such, the figure should be moved to Supplementary. If the authors insist on commenting on it, then data on X-chromosome inactivation in PBMC lineages from female carriers of the TLR7 variant should be provided.

In addition to the segregation of the disease in the family of P6, we have performed segregation analysis also in a further available pedigree (family of P3) confirming previous findings. As suggested by the reviewer, we have also provided a functional analysis for all TLR7-related genes in both families (Figure 3).

Reviewer #4:

This study points to a possible risk factor of severe Covid-19 in males carrying variants of TLR7, thus confirming and potentially extending a previously published study (van der Made et al).

A serious limitation is that only 2 variants (P2 and P3) have been functionally validated.

Thank you for this valuable comment. We have now functionally validated all TLR7 variants (see above responses to other reviewers).

Data in Figure 2 for P1 do not convincingly show a functional effect of that particular variant (Val. 219 Ile).

We agree with the reviewer that the TLR7 variant carried by patient P1 (Val219Ile) has a smaller functional impact suggesting a hypomorphic effect.

If no material is available for the other patients, it is feasible to express the variants in cell lines and to test them. An assay of interferon type I production will be overall more convincing.

We have now tested PBMC from 7 of 8 cases and, since the missing patient (P4) carried the same mutation of P5, we have now functionally validated all TLR7 variants. IFN-ɑ (type IIFN) was analyzed as gene expression (Figure 2).

Other comments:

– There is an inconsistency in figures, i.e number of at risk cases 150 (table 1) or 156 (text)

The total number of severely affected males was 156 (as mentioned in the text). Among them, 150 subjects did not have mutations in the TRL7 gene (Table 1).

– Were the 77 controls from the first cohort males ?

Yes, all SARS-CoV-2-infected subjects included in the analysis from both cohorts were males. We have now clarified this in the text.

– Figure 3 is actually anecdotal especially since the TLR7 variant present in this family has not been functionally validated

Segregation analysis was confirmed in two distinct pedigrees from Italy and Spain patients (P3 and P6) and also supported by functional analysis in all TLR7-related genes (Figure 3).

– The recent paper by Zhang Q. et al. on interferon I pathway variants as risk factors for severe Covid-19 should be cited (Science, 2020 (6515):abbd4570).

Done.

Response to second decision letter

Reviewer #2

Thank you to the authors for addressing my previous queries. There remain some further comments that I suggest need addressing. Line numbers refer to clean untracked manuscript:

Suggest being more specific regarding number / percentage (round all percentages to one decimal places) of patients in the Italian, Spanish and total cohort that had pathogenic TLR7 gene variants – e.g. Italian – 2/79 (2.5%) (not 5), Spanish – 1/77 (1.3%), total ?/272 (?%) – - not clear how many there were in the entire male cohort.

Done.

All percentages were rounded to one decimal place.

We have removed the numbers from the Abstract and explained better in the text: 3/156 (1,9%) pathogenic TLR7 gene variants in severely affected young males and 5/261 (1,9%) in the entire male case cohort, irrespective of age.

Two reported families is not a "fraction". What is meant by the "broader and complex host genome situation”. Suggest revising these conclusions to be more accurate and clear. Remove word "significantly".

Done.

Clinical implications – Revise to be more specific and focused "This new yet complex scenario" means little to the reader.

Done.

Capsular summary – what was the exact size of the total cohort studied?

The Italian young male cohort includes 156 patients and the Spanish one 122 patients. We have revised the sentence.

Percentages of male and female ICU admission and deaths are almost the same – add in significance levels for this and also hospitalization data. If not significant state this.

Done.

5 out of 79 patients – Table suggests that 2 of this cohort had pathogenic variants? Round percentages to one decimal place.

Done.

Were these "rare missense mutations" considered pathogenic? How many patients were there in the entire cohort – ?272?

Among the additional “rare missense mutations” found in the entire male cohort of 561 COVID-19 individuals (regardless of age), the one found in the cases has been shown to be LOF (p.Ala1032Thr) and the one found in the control has been shown to be neutral (p.Val222Asp). We have revised the sentence to make it clearer.

2% is rather small percentage of the total – suggest toning down "tip of the iceberg" and being more focused revising phrase "broader genome scenario".

Done.

Round percentage to one decimal place. 3/156 does not equate to figures given in results text above. Also does not seem to include the other two TLR7 variants found in the older males – were the variants in the older males considered pathogenic or just VUS? If the later, you might consider separating the analysis and conclusions to focus on males under 60?

Percentages were rounded to one decimal place. The two older males shared the same mutation (p.Ala1032Thr) that has been shown to be LOF. We have updated the text to make the paragraph clearer.

Table 1 – Suggest changing N of mutated patients to 3 and the terminology to pathogenic variants?

Table 1 refers to the statistical analysis of sequencing data done before functional studies on all variants.

Table 2 – Suggest listing just the 3 patients with pathogenic variants (a), or otherwise putting the VUS in a separate part of the table (b)

The table has been divided, grouping the LOF mutations together followed by the Hypo mutation and the neutral two.

Figure 3B – can you add data on the 3rd pedigree (2nd Italian family) with a pathogenic variant.

Done. We added the 3rd pedigree in Figure 3.

Reviewer #3:

In the revised version of the manuscript, the authors have tones down some statements and corrected some errors as per the reviewers' recommendations. They have also added new data to address other comments, however far from clarifying the observations raised by the reviewers, these new data raise new important questions and fail to demonstrate internal consistency.

Major comments:

1) This reviewer had requested that the authors perform transfection experiments of TLR7 variants into TLR7 knock-out cells in order to demonstrate causality. The authors have argued that availability of patient PBMC is sufficient to address this point, as it allows functional testing. There are two problems with this. First, unless rescue experiments are performed in the patient cells, it is not possible to conclude that the functional effects are directly related to the TLR7 variants. Second, and more importantly, the TLR7 protein expression data produced by the authors in the response to reviewers (and cited as "data not shown in the text") are inconsistent with the mRNA data included in Figure 2 and 3. In particular, TLR7 mRNA expression was markedly reduced in P3, P6, P7 and P8 as compared to controls. However, TLR7 protein expression was no different in P6 and in controls. While for P7 one could conclude that TLR7 protein expression was reduced, no data are provided for P3 and P8. Although different experimental conditions were used to analyze TLR7 mRNA and protein expression, it is very hard to reconcile normal TLR7 protein expression, but markedly reduced mRNA expression, in P6. These data require a more robust experimental setting, and confirm the importance of using transfection experiments.

Regarding TLR7 expression in PBMCs, there was a misunderstanding. Figure 2 and Figure 3 refer to the mRNA fold change (activated/basal mRNA levels ratio) and not to absolute mRNA levels. Therefore, these results are not comparable with protein expression data.

Transfection experiments are usually requested when (i) patient cells are not available for every mutation presented; (ii) it is the first time that a gene is associated with a disorder. We have shown the effect of each variant in patient-specific cells and the gene has already been associated with the disease (ref. 8). Thus, functional analysis in patients’ and control PBMC represent a robust outcome to support our conclusions.

However, we have considered the request of the reviewer and, in this short time, we have performed transfection experiments for the variants expected to have a functional effect, cloning a dedicated TLR7 plasmid for each of them. PCR based site-directed mutagenesis was performed in pUNO-hTLR7 plasmid (Invivogen) to generate specific plasmids for the single variants. Transfection experiments were performed in HEK293 cells that do not express endogenous TLR7 (Chehadeh and Alkhabbaz 2013). Cells were maintained in DMEM supplemented with 10% FBS, 1% L-Glutamine and 1% pen/strep at 37°C with 5% CO2. Transient transfections were performed using Lipofectamine 2000 (Invitrogen) according to manufacturer’s protocol: 3x104 cell/well were seeded the day before in 6 well plates, and then transfected with 2μg of DNA. Expression of TLR7 protein was examined by flow cytometry 24 hours after transfection, showing expression of TLR7 protein at the intracellular level in all cases (Figure 2B).

After 24 hours from transfection, the cells were stimulated in duplicate experiments with Imiquimod at 1μg/ml for 4 hours and then total RNA was extracted with RNeasy Mini Kit (Qiagen), according to manufacturer’s protocol. cDNA was synthesized from 1μg of total RNA using QuantiTect Reverse Transcription kit (Qiagen) according to the manufacturer’s instructions. We evaluated expression of IFN-ɑ in Imiquimod stimulated and unstimulated cells by qRT-PCR using the same assay described for PBMCs, confirming the results obtained in PBMCs (Figure 2C).

2) The segregation data shown in Figure 3 are not meaningful and do not provide substantial support to the authors' claims. In particular, for pedigree II, also males who did not inherit the TLR7 variant should be tested for IFN-a, ISG15 and IFN-g mRNA expression. Without this essential internal control, the data provided do not help. Incidentally, labels on the X-axis of all mRNA expression data in Figure 3 are misaligned.

Done. Figure 3 has been updated and X-axis labels aligned.

Minor comments:

1) Throughout the manuscript, the authors should avoid use of the word “mutation” and replace it with “variant” or “deleterious variant” as appropriate

Done

Response to third decision letter

Reviewer #2: Thank you for addressing most of my previous comments and suggestions. A few comments remain:

1) In Abstract Results – detail number numerator/denominator (percentage) of pathological TLR7 variants found in the overall affected groups.

The requested detail has been added.

2) Authors should be able to calculate statistical significance of gender differences in the Stokes et al. paper themselves using online Chi-square calculator from the raw data in the paper – suggest amending sentence rather than say "even if they reported descriptive analyses without statistical comparisons"

Statistical significance has been calculated and the sentence has been modified

accordingly.

3) Percentages should be x.y, rather than x,y.

Done

4) In text and Table IIa – suggest removing details regarding gene variants that have no (neutral) functional/clinical significance and highlighting only predicted pathological variants, as non pathological variants are of no clinical relevance / not disease causing. Remove Table IIb. Figure legends will need to be adjusted accordingly.

We have removed Table 2B as requested.

However, we did not remove information on neutral variants since these variants were not previously published and we performed structural and functional analyses to validate their functionality. We thus feel that their characterization could be an added value to the paper.

5) Table I: add percentage affected in column (N. mutated patients). Suggest revising headings to "N. WT variants; N. pathological variants", rather than mutated patients.

Done

Reviewer #3:

Minor comments: The authors have adequately revised the manuscript. They have also performed transfection experiments and tested experimentally the functional effects of the TLR7 variants identified. These are very important data that support the authors'; conclusions. Surprisingly, they have elected to show them only in the point-by-point reply to the reviewers. These data should be added to the main manuscript (as Supplementary data, if so needed), because they provide strong support to the authors' findings.

As suggested from the reviewer, we have added results of transfection experiments to the manuscript as panel B and C to new Figure 2. Text and figure legend have been modified accordingly. Experimental details have been added in the “online repository file”.

https://doi.org/10.7554/eLife.67569.sa2

Article and author information

Author details

  1. Chiara Fallerini

    1. Medical Genetics, University of Siena, Siena, Italy
    2. Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
    Contribution
    Formal analysis, Writing - original draft
    Contributed equally with
    Sergio Daga and Stefania Mantovani
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7386-3224
  2. Sergio Daga

    1. Medical Genetics, University of Siena, Siena, Italy
    2. Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
    Contribution
    Formal analysis, Methodology, Writing - original draft
    Contributed equally with
    Chiara Fallerini and Stefania Mantovani
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6419-9456
  3. Stefania Mantovani

    Division of Infectious Diseases and Immunology, Department of Medical Sciences and Infectious Diseases, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
    Contribution
    Methodology, Writing - original draft
    Contributed equally with
    Chiara Fallerini and Sergio Daga
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5885-2842
  4. Elisa Benetti

    Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
    Contribution
    Software, Formal analysis, Methodology, Writing - original draft
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0819-604X
  5. Nicola Picchiotti

    1. Department of Mathematics, University of Pavia, Pavia, Italy
    2. University of Siena, DIISM-SAILAB, Siena, Italy
    Contribution
    Software, Methodology, Writing - original draft
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3454-7250
  6. Daniela Francisci

    1. Infectious Diseases Clinic, Department of Medicine 2, Azienda Ospedaliera di Perugia and University of Perugia, Santa Maria Hospital, Perugia, Italy
    2. Infectious Diseases Clinic, "Santa Maria" Hospital, University of Perugia, Perugia, Italy
    Contribution
    Methodology, Writing - original draft
    Competing interests
    No competing interests declared
  7. Francesco Paciosi

    1. Infectious Diseases Clinic, Department of Medicine 2, Azienda Ospedaliera di Perugia and University of Perugia, Santa Maria Hospital, Perugia, Italy
    2. Infectious Diseases Clinic, "Santa Maria" Hospital, University of Perugia, Perugia, Italy
    Contribution
    Methodology, Writing - original draft
    Competing interests
    No competing interests declared
  8. Elisabetta Schiaroli

    Infectious Diseases Clinic, Department of Medicine 2, Azienda Ospedaliera di Perugia and University of Perugia, Santa Maria Hospital, Perugia, Italy
    Contribution
    Methodology, Writing - original draft
    Competing interests
    No competing interests declared
  9. Margherita Baldassarri

    1. Medical Genetics, University of Siena, Siena, Italy
    2. Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
    Contribution
    Formal analysis, Methodology, Writing - original draft
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0391-1980
  10. Francesca Fava

    1. Medical Genetics, University of Siena, Siena, Italy
    2. Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
    3. Genetica Medica, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
    Contribution
    Formal analysis, Methodology, Writing - original draft
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4363-2353
  11. Maria Palmieri

    1. Medical Genetics, University of Siena, Siena, Italy
    2. Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
    Contribution
    Methodology, Writing - original draft
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3014-1552
  12. Serena Ludovisi

    1. Division of Infectious Diseases and Immunology, Department of Medical Sciences and Infectious Diseases, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
    2. Department of Internal Medicine and Therapeutics, University of Pavia, Pavia, Italy
    Contribution
    Methodology, Writing - original draft
    Competing interests
    No competing interests declared
  13. Francesco Castelli

    Department of Infectious and Tropical Diseases, University of Brescia and ASST Spedali Civili Hospital, Brescia, Italy
    Contribution
    Methodology, Writing - original draft
    Competing interests
    No competing interests declared
  14. Eugenia Quiros-Roldan

    Department of Infectious and Tropical Diseases, University of Brescia and ASST Spedali Civili Hospital, Brescia, Italy
    Contribution
    Methodology, Writing - original draft
    Competing interests
    No competing interests declared
  15. Massimo Vaghi

    Chirurgia Vascolare, Ospedale Maggiore di Crema, Crema, Italy
    Contribution
    Methodology, Writing - original draft
    Competing interests
    No competing interests declared
  16. Stefano Rusconi

    1. Department of Biomedical and Clinical Sciences Luigi Sacco, University of Milan, Milan, Italy
    2. III Infectious Diseases Unit, ASST-FBF-Sacco, Milan, Italy
    Contribution
    Methodology, Writing - original draft
    Competing interests
    No competing interests declared
  17. Matteo Siano

    Department of Biomedical and Clinical Sciences Luigi Sacco, University of Milan, Milan, Italy
    Contribution
    Methodology, Writing - original draft
    Competing interests
    No competing interests declared
  18. Maria Bandini

    Department of Preventive Medicine, Azienda USL Toscana Sud Est, Siena, Italy
    Contribution
    Methodology, Writing - original draft
    Competing interests
    No competing interests declared
  19. Ottavia Spiga

    1. University of Siena, DIISM-SAILAB, Siena, Italy
    2. Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Siena, Italy
    Contribution
    Data curation, Formal analysis, Methodology, Writing - original draft
    Competing interests
    No competing interests declared
  20. Katia Capitani

    1. Medical Genetics, University of Siena, Siena, Italy
    2. Molecular Mechanisms of Oncogenesis, ISPRO Core Research Laboratory (CRL), Firenze, Italy
    Contribution
    Formal analysis, Methodology, Writing - original draft
    Competing interests
    No competing interests declared
  21. Simone Furini

    Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
    Contribution
    Data curation, Software, Formal analysis, Supervision, Validation, Methodology, Writing - original draft
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1099-8279
  22. Francesca Mari

    1. Medical Genetics, University of Siena, Siena, Italy
    2. Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
    3. Genetica Medica, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
    Contribution
    Data curation, Methodology, Writing - original draft, Project administration
    Competing interests
    No competing interests declared
  23. GEN-COVID Multicenter Study

    Medical Genetics, University of Siena, Siena, Italy
    Contribution
    Conceptualization, Methodology, Writing - original draft
    Competing interests
    No competing interests declared
    1. Floriana Valentino, Medical Genetics, University of Siena, Siena, Italy
    2. Gabriella Doddato, Medical Genetics, University of Siena, Siena, Italy
    3. Annarita Giliberti, Medical Genetics, University of Siena, Siena, Italy
    4. Rossella Tita, Genetica Medica, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
    5. Sara Amitrano, Genetica Medica, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
    6. Mirella Bruttini, Medical Genetics, University of Siena, Siena, Italy
    7. Susanna Croci, Medical Genetics, University of Siena, Siena, Italy
    8. Ilaria Meloni, Medical Genetics, University of Siena, Siena, Italy
    9. Maria Antonietta Mencarelli, Genetica Medica, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
    10. Caterina Lo Rizzo, Genetica Medica, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
    11. Anna Maria Pinto, Genetica Medica, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
    12. Laura Di Sarno, Medical Genetics, University of Siena, Siena, Italy
    13. Giada Beligni, Medical Genetics, University of Siena, Siena, Italy
    14. Andrea Tommasi, Medical Genetics, University of Siena, Siena, Italy
    15. Nicola Iuso, Medical Genetics, University of Siena, Siena, Italy
    16. Francesca Montagnani, Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
    17. Massimiliano Fabbiani, Dept of Specialized and Internal Medicine, Tropical and Infectious Diseases Unit, Azienda Ospedaliera Universitaria Senese, Siena, Italy
    18. Barbara Rossetti, Dept of Specialized and Internal Medicine, Tropical and Infectious Diseases Unit, Azienda Ospedaliera Universitaria Senese, Siena, Italy
    19. Giacomo Zanelli, Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
    20. Elena Bargagli, Unit of Respiratory Diseases and Lung Transplantation, Department of Internal and Specialist Medicine, University of Siena, Siena, Italy
    21. Laura Bergantini, Unit of Respiratory Diseases and Lung Transplantation, Department of Internal and Specialist Medicine, University of Siena, Siena, Italy
    22. Miriana D’Alessandro, Unit of Respiratory Diseases and Lung Transplantation, Department of Internal and Specialist Medicine, University of Siena, Siena, Italy
    23. Paolo Cameli, Unit of Respiratory Diseases and Lung Transplantation, Department of Internal and Specialist Medicine, University of Siena, Siena, Italy
    24. David Bennett, Unit of Respiratory Diseases and Lung Transplantation, Department of Internal and Specialist Medicine, University of Siena, Siena, Italy
    25. Federico Anedda, Dept of Emergency and Urgency, Medicine, Surgery and Neurosciences, Unit of Intensive Care Medicine, Siena University Hospital, Siena, Italy
    26. Simona Marcantonio, Dept of Emergency and Urgency, Medicine, Surgery and Neurosciences, Unit of Intensive Care Medicine, Siena University Hospital, Siena, Italy
    27. Sabino Scolletta, Dept of Emergency and Urgency, Medicine, Surgery and Neurosciences, Unit of Intensive Care Medicine, Siena University Hospital, Siena, Italy
    28. Federico Franchi, Dept of Emergency and Urgency, Medicine, Surgery and Neurosciences, Unit of Intensive Care Medicine, Siena University Hospital, Siena, Italy
    29. Maria Antonietta Mazzei, Department of Medical, Surgical and Neuro Sciences and Radiological Sciences, Unit of Diagnostic Imaging, University of Siena, Siena, Italy
    30. Susanna Guerrini, Department of Medical, Surgical and Neuro Sciences and Radiological Sciences, Unit of Diagnostic Imaging, University of Siena, Siena, Italy
    31. Edoardo Conticini, Rheumatology Unit, Department of Medicine, Surgery and Neurosciences, University of Siena, Policlinico Le Scotte, Siena, Italy
    32. Luca Cantarini, Rheumatology Unit, Department of Medicine, Surgery and Neurosciences, University of Siena, Policlinico Le Scotte, Siena, Italy
    33. Bruno Frediani, Rheumatology Unit, Department of Medicine, Surgery and Neurosciences, University of Siena, Policlinico Le Scotte, Siena, Italy
    34. Danilo Tacconi, Department of Specialized and Internal Medicine, Infectious Diseases Unit, San Donato Hospital Arezzo, San Donato Hospital Arezzo, Arezzo, Italy
    35. Chiara Spertilli, Department of Specialized and Internal Medicine, Infectious Diseases Unit, San Donato Hospital Arezzo, San Donato Hospital Arezzo, Arezzo, Italy
    36. Marco Feri, Dept of Emergency, Anesthesia Unit, San Donato Hospital, Arezzo, Italy
    37. Alice Donati, Dept of Emergency, Anesthesia Unit, San Donato Hospital, Arezzo, Italy
    38. Raffaele Scala, Department of Specialized and Internal Medicine, Pneumology Unit and UTIP, San Donato Hospital, Arezzo, Italy
    39. Luca Guidelli, Department of Specialized and Internal Medicine, Pneumology Unit and UTIP, San Donato Hospital, Arezzo, Italy
    40. Genni Spargi, Department of Emergency, Anesthesia Unit, Misericordia Hospital, Grosseto, Italy
    41. Marta Corridi, Department of Emergency, Anesthesia Unit, Misericordia Hospital, Grosseto, Italy
    42. Cesira Nencioni, Department of Specialized and Internal Medicine, Infectious Diseases Unit, Misericordia Hospital, Grosseto, Italy
    43. Leonardo Croci, Department of Specialized and Internal Medicine, Infectious Diseases Unit, Misericordia Hospital, Grosseto, Italy
    44. Gian Piero Caldarelli, Clinical Chemical Analysis Laboratory, Misericordia Hospital, Grosseto, Italy
    45. Maurizio Spagnesi, Department of Preventive Medicine, Azienda USL Toscana Sud Est, Siena, Italy
    46. Davide Romani, Department of Preventive Medicine, Azienda USL Toscana Sud Est, Siena, Italy
    47. Paolo Piacentini, Department of Preventive Medicine, Azienda USL Toscana Sud Est, Siena, Italy
    48. Elena Desanctis, Department of Preventive Medicine, Azienda USL Toscana Sud Est, Siena, Italy
    49. Silvia Cappelli, Department of Preventive Medicine, Azienda USL Toscana Sud Est, Siena, Italy
    50. Anna Canaccini, Territorial Scientific Technician Department, Azienda USL Toscana Sud Est, Siena, Italy
    51. Agnese Verzuri, Territorial Scientific Technician Department, Azienda USL Toscana Sud Est, Siena, Italy
    52. Valentina Anemoli, Territorial Scientific Technician Department, Azienda USL Toscana Sud Est, Siena, Italy
    53. Agostino Ognibene, Clinical Chemical Analysis Laboratory, San Donato Hospital, Arezzo, Italy
    54. Antonella D’Arminio Monforte, Department of Health Sciences, Clinic of Infectious Diseases, ASST Santi Paolo e Carlo, University of Milan, Milano, Italy
    55. Federica Gaia Miraglia, Department of Health Sciences, Clinic of Infectious Diseases, ASST Santi Paolo e Carlo, University of Milan, Milano, Italy
    56. Massimo Girardis, Department of Anesthesia and Intensive Care, University of Modena and Reggio Emilia, Modena, Italy
    57. Sophie Venturelli, Department of Anesthesia and Intensive Care, University of Modena and Reggio Emilia, Modena, Italy
    58. Stefano Busani, Department of Anesthesia and Intensive Care, University of Modena and Reggio Emilia, Modena, Italy
    59. Andrea Cossarizza, Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, Modena, Italy
    60. Andrea Antinori, HIV/AIDS Department, National Institute for Infectious Diseases, IRCCS, Lazzaro Spallanzani, Rome, Italy
    61. Alessandra Vergori, HIV/AIDS Department, National Institute for Infectious Diseases, IRCCS, Lazzaro Spallanzani, Rome, Italy
    62. Arianna Emiliozzi, HIV/AIDS Department, National Institute for Infectious Diseases, IRCCS, Lazzaro Spallanzani, Rome, Italy
    63. Arianna Gabrieli, Department of Biomedical and Clinical Sciences Luigi Sacco, University of Milan, Milan, Italy
    64. Agostino Riva, III Infectious Diseases Unit, ASST-FBF-Sacco, Milan, Italy
    65. Pier Giorgio Scotton, Department of Infectious Diseases, Treviso Hospital, Local Health Unit 2 Marca Trevigiana, Treviso, Italy
    66. Francesca Andretta, Department of Infectious Diseases, Treviso Hospital, Local Health Unit 2 Marca Trevigiana, Treviso, Italy
    67. Sandro Panese, Clinical Infectious Diseases, Mestre Hospital, Venezia, Italy
    68. Renzo Scaggiante, Infectious Diseases Clinic, ULSS1, Belluno, Italy
    69. Francesca Gatti, Infectious Diseases Clinic, ULSS1, Belluno, Italy
    70. Saverio Giuseppe Parisi, Department of Molecular Medicine, University of Padova, Padua, Italy
    71. Stefano Baratti, Department of Molecular Medicine, University of Padova, Padua, Italy
    72. Melania Degli Antoni, Department of Infectious and Tropical Diseases, University of Brescia and ASST Spedali Civili Hospital, Brescia, Italy
    73. Matteo Della Monica, Medical Genetics and Laboratory of Medical Genetics Unit, A.O.R.N. "Antonio Cardarelli", Naples, Italy
    74. Carmelo Piscopo, Medical Genetics and Laboratory of Medical Genetics Unit, A.O.R.N. "Antonio Cardarelli", Naples, Italy
    75. Mario Capasso, Department of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, Naples, Italy
    76. Roberta Russo, Department of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, Naples, Italy
    77. Immacolata Andolfo, Department of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, Naples, Italy
    78. Achille Iolascon, Department of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, Naples, Italy
    79. Giuseppe Fiorentino, Unit of Respiratory Physiopathology, AORN dei Colli, Monaldi Hospital, Naples, Italy
    80. Massimo Carella, Division of Medical Genetics, Fondazione IRCCS Casa Sollievo della Sofferenza Hospital, San Giovanni Rotondo, San Giovanni Rotondo, Italy
    81. Marco Castori, Division of Medical Genetics, Fondazione IRCCS Casa Sollievo della Sofferenza Hospital, San Giovanni Rotondo, San Giovanni Rotondo, Italy
    82. Giuseppe Merla, Department of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, Naples, Italy
    83. Gabriella Maria Squeo, Laboratory of Regulatory and Functional Genomics, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
    84. Filippo Aucella, Department of Medical Sciences, Fondazione IRCCS Casa Sollievo della Sofferenza Hospital, San Giovanni Rotondo, San Giovanni Rotondo, Italy
    85. Pamela Raggi, Clinical Trial Office, Fondazione IRCCS Casa Sollievo della Sofferenza Hospital, San Giovanni Rotondo, San Giovanni Rotondo, Italy
    86. Carmen Marciano, Clinical Trial Office, Fondazione IRCCS Casa Sollievo della Sofferenza Hospital, San Giovanni Rotondo, San Giovanni Rotondo, Italy
    87. Rita Perna, Clinical Trial Office, Fondazione IRCCS Casa Sollievo della Sofferenza Hospital, San Giovanni Rotondo, San Giovanni Rotondo, Italy
    88. Matteo Bassetti, Department of Health Sciences, University of Genova, Genova, Italy
    89. Antonio Di Biagio, Infectious Diseases Clinic, Policlinico San Martino Hospital, IRCCS for Cancer Research Genova, Genova, Italy
    90. Maurizio Sanguinetti, Microbiology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Catholic University of Medicine, Rome, Italy
    91. Luca Masucci, Microbiology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Catholic University of Medicine, Rome, Italy
    92. Serafina Valente, Department of Cardiovascular Diseases, University of Siena, Siena, Italy
    93. Marco Mandalà, Otolaryngology Unit, University of Siena, Siena, Italy
    94. Alessia Giorli, Otolaryngology Unit, University of Siena, Siena, Italy
    95. Lorenzo Salerni, Otolaryngology Unit, University of Siena, Siena, Italy
    96. Patrizia Zucchi, Department of Internal Medicine, ASST Valtellina e Alto Lario, Sondrio, Italy
    97. Pierpaolo Parravicini, Department of Internal Medicine, ASST Valtellina e Alto Lario, Sondrio, Italy
    98. Elisabetta Menatti, Study Coordinator Oncologia Medica e Ufficio Flussi Sondrio, Sondrio, Italy
    99. Tullio Trotta, First Aid Department, Luigi Curto Hospital, Polla, Salerno, Italy
    100. Ferdinando Giannattasio, First Aid Department, Luigi Curto Hospital, Polla, Salerno, Italy
    101. Gabriella Coiro, First Aid Department, Luigi Curto Hospital, Polla, Salerno, Italy
    102. Fabio Lena, Local Health Unit-Pharmaceutical Department of Grosseto, Toscana Sud Est Local Health Unit, Grosseto, Italy
    103. Domenico A Coviello, U.O.C. Laboratorio di Genetica Umana, IRCCS Istituto G. Gaslini, Genova, Italy
    104. Cristina Mussini, Infectious Diseases Clinics, University of Modena and Reggio Emilia, Modena, Italy
    105. Giancarlo Bosio, Department of Respiratory Diseases, Azienda Ospedaliera di Cremona, Cremona, Italy
    106. Enrico Martinelli, Department of Respiratory Diseases, Azienda Ospedaliera di Cremona, Cremona, Italy
    107. Sandro Mancarella, U.O.C. Medicina, ASST Nord Milano, Ospedale Bassini, Cinisello Balsamo, Italy
    108. Luisa Tavecchia, U.O.C. Medicina, ASST Nord Milano, Ospedale Bassini, Cinisello Balsamo, Italy
    109. Marco Gori, Université Côte d’Azur, Inria, France
    110. Lia Crotti, Istituto Auxologico Italiano, IRCCS, Department of Cardiovascular, Neural and Metabolic Sciences, San Luca Hospital, Milan, Italy
    111. Gianfranco Parati, Istituto Auxologico Italiano, IRCCS, Department of Cardiovascular, Neural and Metabolic Sciences, San Luca Hospital, Milan, Italy
    112. Chiara Gabbi, Independent Medical Scientist, Milan, Italy
    113. Isabella Zanella, Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
    114. Marco Rizzi, Unit of Infectious Diseases, ASST Papa Giovanni XXIII Hospital, Bergamo, Italy
    115. Franco Maggiolo, Unit of Infectious Diseases, ASST Papa Giovanni XXIII Hospital, Bergamo, Italy
    116. Diego Ripamonti, Unit of Infectious Diseases, ASST Papa Giovanni XXIII Hospital, Bergamo, Italy
    117. Tiziana Bachetti, Direzione Scientifica, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy
    118. Maria Teresa La Rovere, Istituti Clinici Scientifici Maugeri IRCCS, Department of Cardiology, Institute of Montescano, Pavia, Italy
    119. Simona Sarzi-Braga, Istituti Clinici Scientifici Maugeri, IRCCS, Department of Cardiac Rehabilitation, Institute of Tradate (VA), Tradate, Italy
    120. Maurizio Bussotti, Istituti Clinici Scientifici Maugeri, IRCCS, Department of Cardiac Rehabilitation, Institute of Milan, Milan, Italy
    121. Mario Chiariello, Istituto per lo Studio, la Prevenzione e la Rete Oncologica (ISPRO)-Core Research Laboratory and Consiglio Nazionale delle Ricerche-Istituto di Fisiologia Clinica, Siena, Italy
    122. Mary Ann Belli, ASST Nord Milano, Ospedale Bassini, Cinisello Balsamo, Italy
    123. Simona Dei, Health Management, Azienda USL Toscana Sudest, Tuscany, Italy
  24. Alessandra Renieri

    1. Medical Genetics, University of Siena, Siena, Italy
    2. Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
    3. Genetica Medica, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
    Contribution
    Conceptualization, Data curation, Supervision, Writing - original draft, Project administration
    For correspondence
    alessandra.renieri@unisi.it
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0846-9220
  25. Mario U Mondelli

    1. Division of Infectious Diseases and Immunology, Department of Medical Sciences and Infectious Diseases, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
    2. Department of Internal Medicine and Therapeutics, University of Pavia, Pavia, Italy
    Contribution
    Methodology, Writing - original draft
    Competing interests
    No competing interests declared
  26. Elisa Frullanti

    1. Medical Genetics, University of Siena, Siena, Italy
    2. Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
    Contribution
    Conceptualization, Data curation, Formal analysis, Supervision, Methodology, Writing - original draft, Project administration
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5634-031X

Funding

Private Donors for Host Genetics Research Project (D.L. n 18 of March 17)

  • Alessandra Renieri

Intesa San Paolo for 2020 charity fund (N.B.2020/0119)

  • Alessandra Renieri

Ministero dell’Istruzione, dell’Università e della Ricerca (Dipartimenti di Eccellenza 2018-2020)

  • Alessandra Renieri

Regione Toscana (Bando Ricerca COVID-19 Toscana)

  • Alessandra Renieri

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

This study is part of the GEN-COVID Multicenter Study, https://sites.google.com/dbm.unisi.it/gen-covid, the Italian multicenter study aimed at identifying the COVID-19 host genetic bases. Specimens were provided by the COVID-19 Biobank of Siena, which is part of the Genetic Biobank of Siena, member of BBMRI-IT, of Telethon Network of Genetic Biobanks (project no. GTB18001), of EuroBioBank, and of RD-Connect. We thank the CINECA consortium for providing computational resources and the Network for Italian Genomes (NIG) http://www.nig.cineca.it for its support. We thank private donors for the support provided to AR (Department of Medical Biotechnologies, University of Siena) for the COVID-19 host genetics research project (D.L n.18 of March 17, 2020). We also thank the COVID-19 Host Genetics Initiative (https://www.covid19hg.org/), MIUR project ‘Dipartimenti di Eccellenza 2018–2020’ to the Department of Medical Biotechnologies University of Siena, Italy, and ‘Bando Ricerca COVID-19 Toscana’ project to Azienda Ospedaliero-Universitaria Senese. We also thank Intesa San Paolo for the 2020 charity fund dedicated to the project N B/2020/0119 ‘Identificazione delle basi genetiche determinanti la variabilità clinica della risposta a COVID-19 nella popolazione italiana’.

Ethics

Clinical trial registration NCT04549831.

Human subjects: The GEN-COVID study was consistent with Institutional guidelines and approved by the University Hospital (Azienda Ospedaliero-Universitaria Senese) Ethical Review Board, Siena, Italy (Prot n. 16929, dated March 16, 2020).

Senior Editor

  1. Jos WM van der Meer, University Medical Centre, Netherlands

Reviewing Editor

  1. Frank L van de Veerdonk, University Medical Center, Netherlands

Publication history

  1. Received: February 16, 2021
  2. Accepted: February 24, 2021
  3. Accepted Manuscript published: March 2, 2021 (version 1)
  4. Version of Record published: March 23, 2021 (version 2)
  5. Version of Record updated: March 25, 2021 (version 3)

Copyright

© 2021, Fallerini et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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