Claire Nicolas De Lamballerie

Andrés Pizzorno

Julien Fouret

Lea Szpiro

Blandine Padey

Julia Dubois

Thomas Julien

Aurélien Traversier

Victoria Dulière

Pauline Brun

Claire Nicolas De Lamballeriea#

Andrés Pizzornoa#

Julien Fouretb

Lea Szpiroa

Blandine Padeya

Julia Duboisa

Thomas Juliena

Aurélien Traversiera

Victoria Dulièrea

Pauline Bruna

Bruno Linaa

Manuel Rosa-Calatravaa

Olivier Terriera&
email: olivier.terrier@univ-lyon1.fr

Transcriptional profiling of immune and inflammatory responses in the context of SARS-CoV-2 fungal superinfection in a human airway epithelial model

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INTRODUCTION

The current pandemic of novel coronavirus disease 2019 , caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) began in Wuhan, Hubei province, China, in December 2019. As of May 18, 2020, there have been more than 4,628,903 confirmed COVID-19 cases in the world as reported by the WHO, including 312,009 deaths (WHO). SARS-CoV-2 is a beta-coronavirus closely related to the severe acute respiratory syndrome coronavirus-1 (SARS-CoV-1) and the Middle East respiratory syndrome coronavirus (MERS-CoV) that emerged in 2003 and 2012, respectively. These viruses are also transmitted from animals to humans and cause severe respiratory diseases in afflicted individuals.

In a short period of time, significant effort has been devoted to understanding the molecular basis of the pathology associated with SARS-CoV-2 infection in an attempt to guide work on treatment, vaccine and diagnostic test development. Numerous clinical studies have reported the pathophysiology of COVID-19 has similar aspects to that initially described for SARS-CoV, i.e. acute lung injury due to over-inflammation following early stages driven by infection and viral replication [START_REF] Chen | Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study[END_REF][START_REF] Guan | Clinical Characteristics of Coronavirus Disease 2019 in China[END_REF][START_REF] Huang | Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China[END_REF][START_REF] Mehta | COVID-19: consider cytokine storm syndromes and immunosuppression[END_REF][START_REF] Zhu | A Novel Coronavirus from Patients with Pneumonia in China[END_REF]. Nevertheless, the particular underlying mechanisms of these exuberant inflammatory responses in SARS-CoV-2 infection remain largely unknown and there is a need to expand our knowledge of the host's response. In this context, several recent omics-based approaches, including in vivo and in vitro transcriptional profiling studies, have highlighted specific aspects of the signature of infection that could contribute to COVID-19 (Blanco-Melo et al., 2020;[START_REF] Gordon | A SARS-CoV-2 protein interaction map reveals targets for drug repurposing[END_REF][START_REF] Messina | COVID-19: Viral-host interactome analyzed by network based-approach model to study pathogenesis of SARS-CoV-2 infection[END_REF][START_REF] Xiong | Transcriptomic characteristics of bronchoalveolar lavage fluid and peripheral blood mononuclear cells in COVID-19 patients[END_REF]. Blanco-Melo and colleagues, using transcriptional and serum profiling of COVID-19 patients, have notably shown that the SARS-CoV-2 infection signature was defined by low levels of Type I and III interferons juxtaposed to elevated chemokines and high expression of IL-6 (Blanco-Melo et al., 2020).

It is now well known that superinfections of bacterial/fungal origin can affect the course and severity of respiratory viral infections. For example, the co-pathogenesis of viruses and bacteria into the lung has been extensively studied, notably in the context of influenza superinfection by bacteria such as S. pneumoniae [START_REF] Bosch | Viral and Bacterial Interactions in the Upper Respiratory Tract[END_REF][START_REF] Mccullers | The co-pathogenesis of influenza viruses with bacteria in the lung[END_REF][START_REF] Morens | Predominant role of bacterial pneumonia as a cause of death in pandemic influenza: implications for pandemic influenza preparedness[END_REF][START_REF] Paget | Repurposing of Drugs as Novel Influenza Inhibitors From Clinical Gene Expression Infection Signatures[END_REF]. To date, there are limited data available on superinfections associated with COVID-19, though superinfections were reported in 10%-20% of SARS-CoV-2-infected adults admitted to Wuhan hospitals through the end of January 2020, and notably in 50%-100% of those who died [START_REF] Zhou | Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study[END_REF]. In intensive care units, COVID-invasive pulmonary aspergillosis [START_REF] Lescure | Clinical and virological data of the first cases of COVID-19 in Europe: a case series[END_REF]. Indeed, a recent study on the French COVID-19 cohort reported that 33% of critically ill COVID-19 patients also showed invasive aspergillosis [START_REF] Alanio | High prevalence of putative invasive pulmonary aspergillosis in critically ill COVID-19 patients[END_REF]. However, while the reasons for increased vulnerability to Aspergillus in COVID-19 patients remain undetermined, the putative contribution of Aspergillus to SARS-CoV-2 related lung inflammation and COVID-19 pathophysiology also constitutes a major unanswered question.

To better understand the biological impact of superinfection in the SARS-CoV-2 context we sought to determine and compare the host transcriptional response to SARS-CoV-2 versus that of a SARS-CoV-2 + Aspergillus superinfection. To reach this goal, we established a model of SARS-CoV-2 infection and superinfection in reconstituted human airway epithelia (HAE), based on previously published works (Nicolas de [START_REF] De Lamballerie | Characterization of cellular transcriptomic signatures induced by different respiratory viruses in human reconstituted airway epithelia[END_REF][START_REF] De Lamballerie | Characterization of cellular transcriptomic signatures induced by different respiratory viruses in human reconstituted airway epithelia[END_REF][START_REF] Pizzorno | Characterization and treatment of SARS-CoV-2 in nasal and bronchial human airway epithelia[END_REF]. Our analysis reveals that both simple infection and superinfection induce a strong deregulation of core components of innate immune and inflammatory responses, however, it also highlights unique transcriptional footprints of the SARS-CoV-2 + Aspergillus superinfection that provide valuable insight for the understanding not only of Aspergillusassociated COVID-19 but also for the management of severe forms of aspergillosis.

RESULTS

In order to identify similarities and differences between the host response to SARS-CoV-2 simple infection and Aspergillus superinfection, we sought to investigate the transcriptome of human respiratory epithelial cells during infection, in comparison with non-infected cells. Aware of the inherent limitations of experimental models using cell lines, we set up a model of infection/superinfection in a physiological model of reconstituted human airway epithelium (HAE). Developed from biopsies of nasal or bronchial cells differentiated in the air/liquid interphase, these HAE models, that we previously used with different respiratory viruses including SARS-CoV-2 (Nicolas de [START_REF] De Lamballerie | Characterization of cellular transcriptomic signatures induced by different respiratory viruses in human reconstituted airway epithelia[END_REF][START_REF] De Lamballerie | Characterization of cellular transcriptomic signatures induced by different respiratory viruses in human reconstituted airway epithelia[END_REF][START_REF] Pizzorno | Characterization and treatment of SARS-CoV-2 in nasal and bronchial human airway epithelia[END_REF] reproduce with high fidelity most of the main structural, functional and innate immune features of the human respiratory epithelium that play a central role during infection, hence constituting an interesting surrogate to study airway disease mechanisms. We infected nasal or bronchial HAE with SARS-CoV-2, and superinfection with Aspergillus was performed at 48 hours postinfection (hpi), which we had previously defined as the peak of acute SARS-CoV-2 infection in the HAE model [START_REF] Pizzorno | Characterization and treatment of SARS-CoV-2 in nasal and bronchial human airway epithelia[END_REF]. Mock-infected, infected (CoV) and superinfected samples (CoV+Asp) were harvested at 72 hpi (24 h after superinfection) to perform mRNA-characterized and validated in terms of viral production, impact on trans-epithelial resistance and apical release of IL-6, which we used as hallmarks of infection (Fig. 2A). Interestingly, in contrast with nasal HAE, we observed a significative increase on the relative viral production in the context of superinfection in bronchial HAE, associated with higher IL-6 levels and a stronger negative impact on trans-epithelial resistance (Fig. 1B).

Differential expression analysis of mRNA-seq data compared to the mock-infected condition identified 1638 and 454 differentially expressed genes (DEGs) in SARS-CoV-2-infected nasal and bronchial HAE, respectively (FC ≥ 2, Benjamini-Hochberg adjusted p-value < 0.01). In the context of CoV+Asp superinfection, the number of DEGs was notably higher, with 2979 and 3235 genes in nasal and bronchial HAE, respectively (Extended data. File 1). Interestingly, there was an important overlap between CoV infection and CoV+Asp superinfection associated DEGs in both nasal and bronchial models. As illustrated in the Venn diagrams (Fig. 1C), more than 96% (nasal HAE) and 99% (bronchial HAE) of DEGs of the CoV signature were also part of the CoV+Asp signature. To provide further functional interpretation of the global transcriptional signatures, we performed a functional enrichment analysis on the CoV and CoV+Asp nasal and bronchial models using the web-based DAVID toolkit. Gene Ontology (GO), UniProt (KW) and Reactome (RC) terms where considered enriched when their Bonferroni-adjusted corrected enrichment p-value was < 0.01 (Fig. 1D). As anticipated, a large part of the most enriched and shared terms between all experimental conditions was related to the host response to infection (Regulation of response to cytokine stimulus, regulation of defense response, regulation of response to biotic stimulus) and also to cornification, which regroups genes mostly involved in cell-death mechanisms (Fig. 1D). Interestingly, functional enrichments specific to CoV infection or CoV+Asp superinfection were also highlighted. For example, CoV infection but no superinfection induced the upregulation of a gene cluster involved in Type I interferon pathway (Fig. 1D). Conversely, gene clusters harboring terms associated with epithelium physiology and cilium movements (regulation of cellular component movements, cell projection assembly, intraflagellar transport) but also with intra and extracellular signaling (post-translational protein phosphorylation, protein localization to extracellular membrane, peptide secretion) were exclusively enriched in the context of CoV+Asp superinfection (Fig. 1D). In parallel, given the importance of the exacerbated immune response observed mainly in severe COVID-19 cases, we further analyzed the inflammation-related terms and identified similar, yet to a different extent, regulation patterns for genes involved in the positive regulation of inflammatory response (GO:0050729) between nasal and bronchial HAE. As exemplified in the heatmap (Fig. 1D), deregulated genes such as calgranulin coding genes S100A12 and S100A9/A8 and downregulated genes such as hallmark of CoV+Asp superinfection, in contrast to CoV infection (Fig. 1D). Similar observations were performed using different terms related to inflammation (Extended data Fig. 1), hence suggesting a very different inflammation signature resulting from superinfection.

Altogether, our results indicate that the CoV+Asp superinfection presents a transcriptomic signature that recapitulates the overall signature of a simple CoV infection, but with both a particularly distinct regulation of the inflammatory response and the additional regulation of many biological processes related to the physiology of epithelia. Of note, we observed a relatively similar global pattern of regulation between the nasal and bronchial HAE models, differing primarily in the magnitude rather than the nature of the responses to CoV and CoV+Asp superinfection.

In order to explore in more depth the transcriptomic signature of the superinfection, we then performed a differential analysis of CoV+Asp signature using that of CoV infection as baseline.

This focused analysis highlighted 248 and 769DEGs in the CoV+Asp nasal and bronchial HAE, respectively (FC ≥ 2, adjusted p-value < 0.01), which allowed us to spot finer differences between the two epithelium models. Indeed, the volcano plots in Fig. 2A and B representing all DEGs induced by the CoV+Asp versus the CoV condition show interesting differences in both the scale of deregulation and the nature of the most deregulated genes between the upper and lower respiratory tract tissues. Interestingly, many genes involved in the regulation of the inflammatory response such as HOX1, IL1B, IL1A, IL17C are found amongst the most upregulated genes in the nasal HAE model (Fig. 2A). These genes are also upregulated in the bronchial HAE model (Extended data file 2). To provide further functional interpretation of the superinfection signature, we performed a functional enrichment analysis, using the same strategy previously described. We only observed a limited number of enriched clusters of downregulated genes, mostly in bronchial HAE, all of them being related to epithelial physiology and cell movement (e.g. cytoskeleton-dependent intracellular transport, protein localization to cilium, cilium movement GO terms, Fig. 2C). In line with this observation, our analysis highlighted several clusters of enriched upregulated genes related to epithelial physiology (e.g. locomotion, movement of cell or subcellular component GO terms, Fig. 2C).

On the other hand, terms related to signaling, host response and immunity were markedly more upregulated in the bronchial HAE model (Fig. 2C & Extended data Fig. 2). In addition to the gene enrichment profile shared between the nasal and bronchial models, our study also highlighted several overlapping biological processes such as the cytokine signaling and IL10 signaling pathways (Fig. 2C), which is consistent with the most upregulated DEGs shown in Fig. 2A and Extended data file 2. To better visualize these observations, we applied a proteinprotein interactions analysis using STRING network to investigate the DEGs corresponding to system, inflammatory response, interleukin-10 signaling) enriched in the bronchial and nasal superinfection signatures along with their functional interactions (Fig. 3A and3B). The two interactome maps with mostly upregulated DEGs illustrate the strong functional interconnections among several cytokines/chemokines (blue) and receptors (yellow) involved in the immune and inflammatory responses occurring in the context of a SARS-CoV-2 + Aspergillus superinfection. Interestingly, our analysis underlined the role of type III IFN (IFNL1, INFL2 andINFL3) and several cytokine-coding genes such as CXCL2/CXCL8 (Fig. 3A and 3B) that are upregulated following simple SARS-CoV-2 infection and even more upregulated in the context of superinfection, arguably illustrating an enhanced specific response to control infection in both models.

DISCUSSION

Invasive pulmonary aspergillosis (IPA), which typically occurs in an immunocompromised host, represents an important cause of morbidity and mortality worldwide [START_REF] Clancy | COVID-19, superinfections and antimicrobial development: What can we expect?[END_REF]. Superinfections were extensively documented in the case of influenza infections, with the latter being usually described to "pave the way" for bacterial superinfections, but several severe influenza cases have also been reported to develop invasive pulmonary aspergillosis 5/19/2020 9:59:00 AM. An increasing amount of evidence points towards a relatively high prevalence of superinfections, including invasive aspergillosis, to be associated with COVID-19 [START_REF] Alanio | High prevalence of putative invasive pulmonary aspergillosis in critically ill COVID-19 patients[END_REF][START_REF] Lescure | Clinical and virological data of the first cases of COVID-19 in Europe: a case series[END_REF][START_REF] Zhou | Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study[END_REF]. However, the underlying mechanisms remain to be characterized. In the present study, we sought to better understand the biological impact of superinfections by determining and comparing the host transcriptional response to SARS-CoV-2 versus SARS-CoV-2 + Aspergillus superinfection. Collectively, our results show a much stronger host response to superinfection in the bronchial epithelial model compared to its nasal counterpart. In both models, functional analyses show that the SARS-CoV-2 + Aspergillus superinfection signature reflects important changes in the expression regulation of genes involved not only in epithelium physiology but also in the regulation of host immune and inflammatory responses compared to that of the simple SARS-CoV-2 infection.

The reconstituted HAE model of infection/superinfection appears as a valuable support for the study of respiratory viral infections and virus-host interactions in highly biologically relevant experimental conditions. Previous results by our group using this model, constituted of fully differentiated and functional human primary cells, have provided meaningful contributions to the characterization of the kinetics of viral infection as well as on the tissue-level remodeling line with our present observation [START_REF] Pizzorno | Characterization and treatment of SARS-CoV-2 in nasal and bronchial human airway epithelia[END_REF]. Whereas no major differences in terms of global superinfection signatures where observed between HAE models of nasal or bronchial origin, the second part of our study highlighted more subtle differences between the two models in terms of scale of deregulation (fold change and p-value), as well as in the nature of the most deregulated genes (Fig. 2C). Ziegler and colleagues have recently reported that differences of infectivity and consecutive host responses between different cell subsets (type II pneumocytes, nasal goblet secretory cells) are linked to varying ACE2/TMPRSS2 levels, ACE2 expression being linked to the IFN response [START_REF] Ziegler | ) Topology and especially child to parent's relations to apply the parent-child method[END_REF]. The discrepancies we observed in the two HAE models could be explained by differences of cell type composition that could be interesting to further explore using combinations of additional experimental models, including ACE2/TMPRSS3 expression and single cell RNA-seq approaches.

Our analysis of the superinfection signature revealed an important role of physiology and cilium-related genes, which could reflect an additional negative impact of Aspergillus infection on the epithelium integrity (trans-epithelial resistance, Fig. 1B) and mucociliary clearance, in good agreement with previous observations by our group following different types of viral infection in HAE (Nicolas de Lamballerie et al., 2019). In the specific context of SARS-CoV-2 infection, the additional deleterious effect on epithelium integrity induced by aspergillosis might contribute to the enhanced disease severity reported in the clinic while arguably increasing the risk of additional bacterial of fungal superinfections, similarly to what has been described in the case of other respiratory viral infections [START_REF] Smith | Influenza virus infection of human ciliated respiratory epithelial cells in culture[END_REF][START_REF] Wu | The differentiated airway epithelium infected by influenza viruses maintains the barrier function despite a dramatic loss of ciliated cells[END_REF].

Another major finding of our study relates to the impact of infection/superinfections on interferon and inflammatory responses, notably when compared to the limited transcriptomic data available in literature to date. Using primary cells, Blanco-Melo and colleagues recently reported that SARS-CoV-2 induces limited IFN-I and III responses [START_REF] Blanco-Melo | Imbalanced Host Response to SARS-CoV-2 Drives Development of COVID-19[END_REF].

Our data are not entirely consistent with these findings. Whereas we also demonstrate a very limited involvement of the type I interferon response in the context of simple SARS-CoV-2 infection -in contrast with data previously obtained with other viruses (Nicolas de Lamballerie et al., 2019), our analysis highlights an important upregulation of crucial genes involved in the type IIII interferon response. This response is even more pronounced in the context of superinfection (Fig. 3A and3B). Type III interferon (IFN-λ) is known to play a key role in innate and adaptive mucosal immune responses to infection [START_REF] Ye | Interferon-λ orchestrates innate and adaptive mucosal immune responses[END_REF]. Interestingly, IFN-λ has been identified as a critical regulator of neutrophil activation to prevent fungal infection such as invasive pulmonary aspergillosis [START_REF] Espinosa | Type III interferon is a critical regulator of innate antifungal immunity[END_REF]. Our data suggest a divergence between type I and type III interferon responses, with enhanced activation of the latter in the increased neutrophil activation as an important first line of adaptive defense against these two pathogens. This hypothesis is further supported by the observed induction of several monocyte-and neutrophil associated chemokines, such as CCL2, CXCL2 and CXCL3 (Fig. 3A andB). Interestingly, our results suggest a strong activation of adaptive immune response in the context of SARS-CoV-2 infection, in line with currently available clinical data from COVID-19 patients, which generally present high levels of circulating neutrophils [START_REF] Chen | Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study[END_REF][START_REF] Qin | Dysregulation of immune response in patients with COVID-19 in Wuhan, China[END_REF]. These observations support an important role of neutrophil recruitment in the response to COVID-19, more particularly in the context of fungal superinfection with an exacerbation of pro-inflammatory response. In that sense, the upregulation of key genes belonging to the IL-10 pathway observed in our analysis (Fig. 2C), previously demonstrated to play a deleterious role in innate resistance to systemic aspergillosis [START_REF] Clemons | Role of IL-10 in invasive aspergillosis: increased resistance of IL-10 gene knockout mice to lethal systemic aspergillosis[END_REF], constitutes an illustration of how such "enhanced" state of inflammation could contribute to increase severity.

In conclusion, our transcriptional profiling approach revealed unique features of the SARS-CoV-2 + Aspergillus superinfection signature, characterized on one side by an "enhanced" version of that induced by SARS-CoV infection, but also by specific changes on respiratory tissue physiology, a distinct regulation of type I and type III interferons, and an over-induction of inflammatory response. While we acknowledge that it would be rather bold to make a statement on the possible severity associated with Aspergillus superinfection in a pre-existing COVID-19 pathological context based solely on our results, our observations suggest that the immunomodulation induced by SARS-CoV-2 infection could establish a favorable context for the development of severe forms of aspergillosis .This could constitute an important aspect to be considered for the immunological follow-up of COVID-19 patients with aspergillosis. On top of that, the characteristic infection signatures described in this study provide valuable insight in the perspective of possible future treatments targeting the COVID-19 inflammatory response, which could result in counter-productive effects for the management of aspergillosis.

MATERIALS AND METHODS

Reconstituted human airway epithelial model

MucilAirTM HAE reconstituted from human primary cells obtained from nasal or bronchial biopsies, were provided by Epithelix SARL (Geneva, Switzerland) and maintained in air-liquid interphase with specific culture medium in Costar Transwell inserts (Corning, NY, USA) according to the manufacturer's instructions. For infection experiments, apical poles were gently washed twice with warm OptiMEM medium (Gibco, ThermoFisher Scientific) and then infected directly with 150 l dilution of virus in OptiMEM medium, at a multiplicity of infection (MOI) of 0.1 [START_REF] Pizzorno | Characterization and treatment of SARS-CoV-2 in nasal and bronchial human airway epithelia[END_REF]. For mock infection, the same procedure was followed using OptiMEM as inoculum. Superinfection was performed in SARS-CoV-2 infected cells at 48h post-infection, with apical inoculation of 10 l of Aspergillus in OptiMEM at a MOI of 1.

Samples were collected from apical washes or basolateral medium at 72h post infection.

Variations in transepithelial electrical resistance (TEER) were measured using a dedicated volt-ohm meter (EVOM2, Epithelial Volt/Ohm Meter for TEER) and expressed as Ohm/cm2.

Pathogens

All experiments involving clinical samples and the manipulation of infectious SARS-CoV-2 were performed in biosafety level 3 (BSL-3) facilities, using appropriate protocols. The BetaCoV/France/IDF0571/2020 SARS-CoV-2 strain used in this study was isolated directly from a patient sample as described elsewhere [START_REF] Pizzorno | Characterization and treatment of SARS-CoV-2 in nasal and bronchial human airway epithelia[END_REF]. Viral stocks were prepared and quantified in Vero E6 cells (TCID50/mL). Aspergillus niger (ATCC 16404) was quantified on maltose extract agar plates (CFU/ml).

mRNA sequencing

Total RNA was extracted using the RNeasy Mini Kit (QIAGEN, ref 74104) with DNase treatment, following the manufacturer's instruction. 500 μg total RNA was used to prepare polyA-enriched RNA-seq libraries using the KAPA mRNA Hyper Prep Kit (Roche, ref.

KK8581/KK8581). Those libraries were prepared separately for each sample with 11 amplification cycles, then all libraries were equimolar-pooled before sequencing. Paired-end sequencing (2×100bp) was performed with Illumina NovaSeq 6000 sequencing platform on a SP flowcell (Illumina, ref: 20040326).

RNA-Seq data trimming

Raw reads were first cleaned with Cutadapt 2.8 [START_REF] Martin | Cutadapt removes adapter sequences from high-throughput sequencing reads[END_REF] to trim adapters ( AGATCGGAAGAGCACACGTCTGAACTCCAGTCA and reads) and low-quality ends (i.e terminal bases with phred quality score below 30). Only reads longer than 75bp and with less than 20% of N (undetermined base) after trimming were kept for further analysis.

Transcript abundance quantification

The Kallisto 0.46.1 software was used for reference-indexing and transcript abundance estimation [START_REF] Bray | Near-optimal probabilistic RNAseq quantification[END_REF]. The reference transcriptome based on NCBI RefSeq annotation release 109.20191205 and genome assembly build GRCh37.p13 were chosen for this analysis [START_REF] Maglott | Entrez Gene: gene-centered information at NCBI[END_REF]. Default options were used for Kallisto except for reads orientation that was specified. The Extended Data Table 2 provides a summary statistics of the pseudoalignment process.

Differential expression analysis

Differential expression analysis was performed with R 3.6.3. First abundances at transcriptlevel were imported using tximport 1.14.0 [START_REF] Soneson | Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences[END_REF]. Only coding mRNA transcripts were considered. Gene-level effective lengths were obtained by weighted mean with their expression values in FPKM (Fragment Per Kilobase Million) weights. Raw counts were also computed at gene-level by sum and used as input for DESeq2 1.26.0 [START_REF] Love | Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2[END_REF]. Normalization within DESeq2 used first gene-level effective length and then the size factor that was estimated using the function 'estimateSizeFactors'. Differential expression testing was performed using default parameters. P-values were adjusted using the Benjamini-Hochberg method (Benjamini and Hochberg, 1995) after the independent filtering implemented by default in DESeq2. A gene was considered differentially expressed if both the adjusted pvalue is below 0.01 and the induced change in expression is at least a two-fold increase (for up-regulated genes) or a two-fold decrease (for down-regulated genes). Finally, the NCBI gene IDs were mapped to Uniprot IDs using Uniprot cross-references (https://www.uniprot.org/database/DB-0118) [START_REF] Breuza | The UniProtKB guide to the human proteome[END_REF][START_REF] Maglott | Entrez Gene: gene-centered information at NCBI[END_REF]. First, Uniprot entries associated to several gene IDs were removed. Indeed, we are not focused on gene fusion products or read-through transcripts in this study. In case of a gene ID associated to several Uniprot entries, only the best entry based on the reviewed status and the annotation score was kept; if those values were not discriminant enough, only duplicated entries with a reviewed status and an annotation score of 5 were kept. The protein-protein interaction (PPI) network was analyzed with STRING 11.0 and visualized with Cytoscape 3.8.0.

In silico functional analysis

Based on mapped Uniprot IDs, gene set enrichment was performed using the parent-child term, called child, is considered relatively to its parents to compute the probability of enrichment (p-value). We considered three functional databases with parent-child relations:

Reactome (Fabregat et al., 2018), Uniprot Keyword (restricted to "Biological process" and "Disease") [START_REF] Breuza | The UniProtKB guide to the human proteome[END_REF] and Gene ontology (Restricted to "Biological process") (Ashburner et al., 2000;The Gene Ontology Consortium, 2015). Different files (available online:

URLs were all accessed the 04/02/2020) were parsed to define Uniprot ID associations with terms and identify child to parents relations within each functional database; URLs are specified in Extended Data Table 1. As previously defined [START_REF] Grossmann | Improved detection of overrepresentation of Gene-Ontology annotations with parent child analysis[END_REF], the p_min probability represents how well a child term can be enriched relative to its parents. The higher this probability is, the less informative is the child term relative to its parents. This is also a statistic that can be used for independent filtering to reduce the p-value adjustment burden. Therefore, all terms with a p_min probability higher than 10e-4 were filtered out before p-values adjustment using Bonferroni method [START_REF] Dunn | Multiple Comparisons among Means[END_REF]. This multiple-testing correction procedure was handled separately for each functional database and analyzed gene list. A minimum adjusted p-value of 0.05 has been set for significant enrichment.

Figure 1 (

 1 Figure1(A) Overview of experimental strategy (B) At 72h post-infection, for both nasal and bronchial HAE model, the relative viral production (intracellular) was determined using RTqPCR, and the impact of infection on epithelium integrity was monitored by measure of the transepithelial resistance (TERR Ohms/cm2). IL-6 was measured at the apical using a specific ALISA assay. (C) Nasal and bronchial gene signature overlap for SARS-CoV2 or SARS-CoV-2+Aspergillus infected conditions vs. Mock. We contrasted the significantly differentially expressed gene lists corresponding to CoV vs. Mock and CoV+Asp vs. Mock in nasal and bronchial HAE. Only genes above threshold (log2(FC)>1 or <-1 compared to the mock-infected condition and Benjamini-Hochberg adjusted p value < 0.01) were considered. (D) Overview of functional enrichment results for SARS-CoV2 or SARS-CoV-2+Aspergillus infected conditions vs. Mock. Considering (CoV vs. Mock) and (CoV+Asp vs. Mock) for both bronchial and nasal HAE, significantly up-or down-regulated gene lists (x-axis) were tested for significant enrichment using the parent-child strategy (see methods). If below the threshold (0.01), the adjusted pvalues corresponding to different terms (y-axis) are represented by point sizes (see legend). After clustering terms based on gene occurrences (binary distance & Ward algorithm) in 15 metagroups, only the top 2 (lowest adjusted p-value) were represented here. For the complete list of significant terms, see supplementary figure S1. The bar plot on the right represents the sizes of enriched terms (called child) in comparison to the size of their parents (see methods for definitions). (E) Positive regulation of the inflammatory response in nasal and bronchial HAE. We extracted the list of proteins associated with the term GO:0050729 and visualized the scaled log transformed expression results in a heatmap. Each row represents a gene and each column is an experimental condition. The color key indicates the scaled expression levels vs. Mock (red, low; blue, high).

Figure 2 (

 2 Figure 2 (A) & (B) Volcano plots of differentially expressed genes from (A) nasal and (B) bronchial tissue superinfected by Aspergillus. We considered significantly modulated genes (CoV+Asp vs. CoV) for both bronchial and nasal HAE (log2(FC)>1 or <-1 compared to the CoV condition and Benjamini-Hochberg adjusted p-value < 0.01). The color scale represents the log-transformed fold change values and ranges from low (red) to high (blue). (C) Overview of functional enrichment results of SARS-CoV-2+Aspergillus superinfection vs. SARS-CoV2 infection. Considering (CoV+Asp vs. CoV) for both bronchial and nasal epithelium type, significantly upor down-regulated gene lists (x-axis) were tested for significant enrichment using the parentchild strategy (see methods). If below the threshold (0.01), the adjusted p-values corresponding to different terms (y-axis) are represented by point sizes (see legend). After clustering terms based on gene occurrences (binary distance & Ward algorithm) in 15 metagroups, the top 2 in terms lowest p-value were represented here. For the complete list of significant terms, see Extended Data File 2. The bar plot on the right represents the sizes of enriched terms (called child) in comparison to the size of their parents (see methods for definitions).

Figure 3 (Extended Data Figure 1

 31 Figure3(A) & (B) Network visualization of immunity-associated proteins in superinfected HAE. We selected the 76 and 191 genes significantly differentially expressed (vs. CoV) in our study (respectively in nasal (A) and bronchial (B) HAE) associated with the Reactome "Interleukin-10 signaling" (R-HSA-6783783) and "cytokine signaling in immune system" pathway (R-HAS-1280215), the Uniprot keyword "inflammatory response" (KW-0395) and the Gene Ontology term "immune system process" (GO:0002376). The circles indicate genes modulated in our study in the superinfection context. The predicted associations are materialized by the dark lines. Cytokines/chemokines were highlighted in blue and receptors in yellow.

Acknowledgments

The authors would like to thank Epithelix (Switzerland) and IGENSEQ sequencing core facility (Institut du Cerveau ICM, Paris) for their help. This study was funded by CNRS, and Mérieux research grants. The sponsors had no role in study design, collection, analysis and interpretation of data, manuscript writing, or in the decision to submit the article for publication.

Topology databases/interpro/ParentChildTreeFile. txt Uniprot Details (name,Id) http://ftp.ebi.ac.uk/pub/databases/interpro/entry.list Uniprot

Term size Extended Data Table 2 Statistics of RNA-Seq fragment pseudo-alignment to the human transcriptome.


Olivier Terrier

Sébastien Dilly

Andrés Pizzorno

Julien Henri

Francis Berenbaum

Bruno Lina

Bruno Fève

Frédéric Adnet

Michèle Sabbah

Manuel Rosa-Calatrava

Broad-spectrum antiviral activity of naproxen: from Influenza A to SARS-CoV-2 Coronavirus

Keywords: Antiviral, drug repurposing, SARS-CoV-2, Influenza, structure-based drug design, inflammation, nucleoprotein

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1 Broad-spectrum antiviral activity of naproxen: from Influenza A to SARS-CoV-2 Coronavirus Olivier Terrier 1 , Sébastien Dilly 2# , Andrés Pizzorno 1# , Julien Henri 3 , Francis Berenbaum [START_REF] Stebbing | COVID-19: combining antiviral and anti-inflammatory treatments[END_REF] , Bruno Lina 1 , Bruno Fève 5 , Frédéric Adnet 6 , Michèle Sabbah 2 , Manuel Rosa-Calatrava 1& , Vincent Maréchal 2& , Anny Slama Schwok 2* 1: CIRI, Centre International de Recherche en Infectiologie, (Team VirPath), Univ Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, ENS de Lyon, F-69007, Lyon, France. The current pandemic of novel coronavirus disease 2019 (COVID- 19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) began in Wuhan, Hubei province, China, in December 2019 [START_REF] Huang | Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China[END_REF][START_REF] Zhou | Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study[END_REF] . As of April 22, 2020, there have been more than 2 475 723 confirmed cases of COVID-19, including 169 151 deaths in 213 countries, reported to WHO (https://www.who.int/emergencies/diseases/novelcoronavirus-2019). SARS-CoV-2 is a beta-coronavirus closely related to the severe acute respiratory syndrome coronavirus-1 (SARS-CoV-1) and the Middle East respiratory syndrome coronavirus (MERS-CoV), that emerged in 2003 and 2012, respectively. These viruses are also transmitted from animals to humans and cause severe respiratory diseases in afflicted individuals.

There is an urgent need for specific and effective antiviral drugs directed against pandemic SARS-CoV-2 to prevent the most severe forms of COVID-19. In that context, drug repurposing is a priority. Ongoing advances in our knowledge of this new virus and its pathogenesis are revealing an exacerbated inflammatory response in severe COVID-19 cases, with a similar cytokine storm observed in severe cases of H5N1 Influenza virus infection and 1918 Influenza A pandemics [START_REF] Fukuyama | The pathogenesis of influenza virus infections: the contributions of virus and host factors[END_REF] . In that regard, the symptoms of respiratory distress caused by COVID-19 could be reduced by drugs combining anti-inflammatory and antiviral effects [START_REF] Stebbing | COVID-19: combining antiviral and anti-inflammatory treatments[END_REF] .

Several current therapeutic approaches involve drug repositioning, in light of what has been achieved for other viruses, particularly influenza viruses 5 . Our previous work showed that naproxen, an approved antiinflammatory drug, is an inhibitor of both cyclooxygenase (COX) and of Influenza A virus nucleoprotein [START_REF] Lejal | Structure-based discovery of the novel antiviral properties of naproxen against the nucleoprotein of influenza A virus[END_REF] .

Although NSAIDs were the subject of a precautionary measure by the French Ministry of Health in mid-March 2020 regarding the use of these drugs in the event of COVID-19 infection, there is currently no evidence of any particular toxicity of this family of drugs [7][START_REF] Torjesen | Covid-19: ibuprofen can be used for symptoms, says UK agency, but reasons for change in advice are unclear[END_REF] . Moreover, the ability of naproxen to decrease viral load and inflammation through the inhibition of cyclooxygenase activity could be beneficial for limiting the cytokine storm that may happen in severe forms of COVID19. Importantly, naproxen has the advantage of being a generic drug, readily available and often used for other indications than COVID-19 by fragile populations.

Naproxen binding to the Influenza A virus nucleoprotein blocked viral RNA association with the nucleoprotein and impeded its self-association [START_REF] Lejal | Structure-based discovery of the novel antiviral properties of naproxen against the nucleoprotein of influenza A virus[END_REF][START_REF] Tarus | Structure-based design of novel naproxen derivatives targeting monomeric nucleoprotein of Influenza A virus[END_REF] ; consequently, naproxen strongly reduced viral transcription/ replication in infected cells and protected mice against an infection with Influenza A virus [START_REF] Lejal | Structure-based discovery of the novel antiviral properties of naproxen against the nucleoprotein of influenza A virus[END_REF][START_REF] Dilly | From Naproxen Repurposing to Naproxen Analogues and Their Antiviral Activity against Influenza A Virus[END_REF] . Viral nucleoproteins are unique to the virus and no equivalent of these proteins are found in the host cell, which makes them attractive targets for potential antivirals [START_REF] Chenavas | Influenza virus nucleoprotein: structure, RNA binding, oligomerization and antiviral drug target[END_REF][START_REF] Lin | Structure-Based Stabilization of Non-native Protein-Protein Interactions of Coronavirus Nucleocapsid Proteins in Antiviral Drug Design[END_REF] . Moreover, the viral nucleoprotein is an important diagnostic marker of infection with SARS-COV and /or Influenza A [START_REF] Su | Detection of Influenza a Virus in Swine Nasal Swab Samples With a Wash-Free Magnetic Bioassay and a Handheld Giant Magnetoresistance Sensing System[END_REF][START_REF] Che | Nucleocapsid protein as early diagnostic marker for SARS[END_REF] . SARS-CoV-1 virions contains multiple copies of the N protein (ca 1000 copies) located inside the particle, thus N is one of the most abundant structural proteins in CoVs [START_REF] Bar-On | SARS-CoV-2 (COVID-19) by the numbers[END_REF] . Through its interaction with the coronavirus membrane (M) protein, the N protein drives virus assembly and budding. N binds to the long viral RNA genome to form a virion core comprising a ribonucleoprotein (RNP) complex that assumes a long helical structure. The RNP is important for replication and transcription in which the N protein also plays a critical role. Interactions between N and the non-structural protein-3 are also important for replication. In addition to its role in different aspects of the viral cycle, the N protein not only highjacks cellular processes, including the progression of the host cell cycle and apoptosis, but also modulates the immune response, by for example inhibiting the interferon response [START_REF] Hu | The Severe Acute Respiratory Syndrome Coronavirus Nucleocapsid Inhibits Type I Interferon Production by Interfering with TRIM25-Mediated RIG-I Ubiquitination[END_REF] .

In this study, we combined structure-based modeling of naproxen binding to the nucleoprotein of SARS-CoV-2 with experimental approaches to explore and validate whether naproxen harbors antiviral activity against SARS-CoV-2 pandemic virus, as previously implemented for Influenza A virus [START_REF] Tarus | Structure-based design of novel naproxen derivatives targeting monomeric nucleoprotein of Influenza A virus[END_REF][START_REF] Dilly | From Naproxen Repurposing to Naproxen Analogues and Their Antiviral Activity against Influenza A Virus[END_REF][START_REF] Lejal | Turning off NADPH oxidase-2 by impeding p67(phox) activation in infected mouse macrophages reduced viral entry and inflammation[END_REF] .

Structure-based modeling of naproxen binding to the nucleoprotein of SARS-CoV-2: The nucleoproteins N of enveloped, positive-sense, single-stranded viruses Coronavirus (CoV) share with negative-sense singlestranded viruses such as Influenza A virus the ability to bind to-and protect genomic viral RNA without sequence specificity and to form self-associated oligomers [START_REF] Chenavas | Influenza virus nucleoprotein: structure, RNA binding, oligomerization and antiviral drug target[END_REF][START_REF] Lin | Structure-Based Stabilization of Non-native Protein-Protein Interactions of Coronavirus Nucleocapsid Proteins in Antiviral Drug Design[END_REF][START_REF] Lejal | Turning off NADPH oxidase-2 by impeding p67(phox) activation in infected mouse macrophages reduced viral entry and inflammation[END_REF][START_REF] Tarus | Oligomerization paths of the nucleoprotein of influenza A virus[END_REF] . Despite the limited sequence similarity between them, the N-terminal domains of three coronaviruses SARS-Cov-1, MERS-CoV, SARS-CoV-2 and Influenza A virus all presented a wide, positively-charged groove in which the viral negatively-charged RNA binds, Figure 1 (A-D) [START_REF] Chang | Crystal structure of RNA binding domain of nucleocapsid phosphoprotein from SARS coronavirus 2 to be[END_REF][START_REF] Huang | Structure of the N-terminal RNA-binding domain of the SARS CoV protein[END_REF][START_REF] Lin | Structure-Based Stabilization of Non-native Protein-Protein Interactions of Coronavirus Nucleocapsid Proteins in Antiviral Drug Design[END_REF] . The coronavirus N proteins present a central antiparallel sheet of 3-5 β-strands structure and flexible linkers and loops, with emphasis on the large extrusion carrying many basic residues, which likely helps adjust binding to the RNA. Such flexibility [START_REF] Tarus | Molecular dynamics studies of the nucleoprotein of influenza A virus: role of the protein flexibility in RNA binding[END_REF] was evaluated here from the crystallographic b factor (Figure 1 E-H). The central parts of the  -sheet of these CoV proteins concentrate most of the aromatic residues (Figure 1I). For instance, SARS-CoV-2 nucleoprotein amino-terminal domain counts 14 aromatic residues among a total of 124 modeled in 6VYO.pdb. The consecutive, 5-aromatics pentapeptide 108 WYFYY 112 is located at the center of the electropositive groove (Figure 1I, red arrow). This contrast with the nucleoprotein of Influenza A virus which has a single aromatic residue (Y148) within its RNA binding groove in which naproxen (Figure 1J) was shown to bind, making electrostatic (blue arrow) and hydrophobic interactions (red arrow) with conserved residues of the RNA binding groove and C-terminal domain (Sup.

Figure 1) [START_REF] Lejal | Structure-based discovery of the novel antiviral properties of naproxen against the nucleoprotein of influenza A virus[END_REF] . From these observations, we could expect that the N-terminal domain of CoVs N may offer more than a single binding site for naproxen, which could concentrate within the cluster of aromatic residues found in the middle of the -sheet. This is exactly what is observed in SARS-CoV-1 (Figure 1K, N and supplementary Figure 1C-K). The recently published structure of MERS-CoV N terminal domain and the SARS-Cov-2 N-terminal domain were crystallized as a dimer of dimer, which offers a potential site for naproxen at the interface of the dimer [START_REF] Lin | Structure-Based Stabilization of Non-native Protein-Protein Interactions of Coronavirus Nucleocapsid Proteins in Antiviral Drug Design[END_REF] in addition to a binding site on one monomer of the protein. These two sites are shown in Sup. Figure 1E, F. Naproxen stabilized this "non -natural" interface as found in the crystal structure for the designed ligands by Hou et al [START_REF] Lin | Structure-Based Stabilization of Non-native Protein-Protein Interactions of Coronavirus Nucleocapsid Proteins in Antiviral Drug Design[END_REF] . Figures 1L, O andM, P and Sup. Figure 1H,E indeed show that naproxen can also bind at this interface in both MERS-CoV and SARS-Cov-2, respectively, but an additional binding site in SARS-CoV-2 monomer was also identified (Sup. Figure 1F). Supplementary Figure 1A -D shows that naproxen binds at the interface between hydrophobic and hydrophilic parts of N proteins, to account for the hydrophobic interactions of its aromatic core with usually aromatic residues ( -stacking) and methyl group of the methoxy group with hydrophobic apolar residues and polar and/or electrostatic interactions via its carboxylate moiety. Accordingly, the following order of free binding energies of naproxen binding to the N-terminal domain of N were calculated: SARS-CoV-1 (G= -30 Kcal/mol) ≈MERS-CoV (G= -29 Kcal/mol) ≈ SARS-Cov-2 (G= -26 to -32) Kcal/mol for site 1 and 2, respectively. This suggests a similar affinity of naproxen for SARS-CoV-1 and MERS-CoV, similar or slightly lower affinities of naproxen for SARS-CoV-2 than naproxen affinity for Influenza A virus based on a free energy calculation of (G= -30 We further evaluated the antiviral effect of naproxen using a more biologically relevant experimental model, notably the nasal and bronchial MucilAir TM reconstituted human airway epithelia (HAE) [START_REF] Pizzorno | Characterization and treatment of SARS-CoV-2 in nasal and bronchial human airway épithélium[END_REF] .

6

Developed from biopsies of nasal or bronchial cells differentiated in the air/liquid interphase, these models reproduce with high fidelity most of the main structural, functional and innate immune features of the human respiratory epithelium that play a central role in the early stages of infection and constitute robust surrogates to study airway disease mechanisms and for drug discovery [START_REF] Pizzorno | Characterization and treatment of SARS-CoV-2 in nasal and bronchial human airway épithélium[END_REF] . Post-infection treatment of nasal HAE with 90 or 300 µM naproxen did not show an antiviral effect at 48 hpi compared to the mock-treated control (Fig. 2C, upper panel).

Conversely, significant reductions in intracellular SARS-CoV-2 viral titers were observed for the two treatment conditions in bronchial HAE (73 and 82% reduction vs mock-treated control, respectively). This reduction in viral titers correlated with naproxen inducing a protective effect of the bronchial epithelium integrity, as shown by Trans-epithelial electrical resistance (TEER), considered as a surrogate of epithelium integrity. TEER values in both naproxen groups were comparable to those of the uninfected and untreated control (Fig. 2C, lower panel) and significantly higher than those of the untreated control (Fig. 2C, lower panel). The antiviral effects of naproxen in reconstituted human bronchial epithelium are consistent with the IC50 value determined in Vero E6 cells. However, the lack of antiviral effect of naproxen on nasal epithelium is puzzling. The viral load is usually lower in the nasal cavity as compared to the lungs [START_REF] Bar-On | SARS-CoV-2 (COVID-19) by the numbers[END_REF][START_REF] Rockx | Comparative pathogenesis of COVID-19, MERS, and SARS in a nonhuman primate model[END_REF] . The nasal epithelium is an important portal for initial infection, and may serve as a key reservoir for viral spread across the respiratory mucosa and an important locus mediating viral transmission. The number of host cells as pneumocytes and alveolar macrophages permissive to viral entry is higher in the lungs than the mucous cells in the nasal cavity. Moreover, recent interactome analysis support the hypothesis that SARS-CoV-2 preferentially hijacks host proteins available in lung tissue, and N targets stress granule protein G3BP1, an essential antiviral protein which is known to induce innate immune response 7 .

Interestingly, we previously determined that the peak of viral replication was reached earlier in bronchial (48-72 hpi) than in nasal HAE, in which a progressive increase in infectious viral titers was observed until at least 96 hpi [START_REF] Pizzorno | Characterization and treatment of SARS-CoV-2 in nasal and bronchial human airway épithélium[END_REF] . Using this model, this differential antiviral effect between airway sites was observed for naproxen in this work and for remdesivir in a recent report that indicates an antiviral effect mainly observed in the lower respiratory tract of non-Human primates [START_REF] Pizzorno | Characterization and treatment of SARS-CoV-2 in nasal and bronchial human airway épithélium[END_REF] . It is interesting to note that naproxen is found very protective against viral-induced damages at 48 h post-infection in the reconstituted bronchial epithelium (Figure 2C and Figure 4 in [START_REF] Pizzorno | Characterization and treatment of SARS-CoV-2 in nasal and bronchial human airway épithélium[END_REF] ). Therefore, we speculate that the lack of naproxen effect in nasal epithelium as opposed to its antiviral effect in pulmonary epithelium could be associated with differential expression or activity of host factor(s), and/or with slower replication kinetics in the nasal vs pulmonary epithelia. FAD-approved drugs including nitazoxanide, chloroquine and remdesivir exhibited IC50s in the low micromolar range against a clinical isolate of COVID-2019 in vitro [START_REF] Wang | Remdesivir and chloroquine effectively inhibit the recently emerged novel coronavirus (2019-nCoV) in vitro[END_REF] . Their corresponding selectivity index were 17, 129 and 89, respectively and CC50 values found to be 36 µM and in the > 100 µM range. Here, the selectivity index determined for naproxen for SARS-CoV-2 in VeroE6 cells is SI > (19 ± 3) µM.

The IC50 of naproxen for SARS-CoV2 compares well to IC50 = (25 ± 7) µM and SI = 64 of naproxen determined for Influenza A at the same MOI = 10 -2 . These values are consistent with the determination of the free energies of naproxen binding to N. We cannot exclude additional putative naproxen binding sites of naproxen in the C-terminal domain of SARS-CoV2 or in the unstructured serine-rich domain that could enable naproxen to further interfere with N functions. In addition to the antiviral activity against SARS-CoV-2 virus demonstrated here, naproxen also exhibited antiviral properties in cellular and rodent models of Influenza A and B [START_REF] Lejal | Structure-based discovery of the novel antiviral properties of naproxen against the nucleoprotein of influenza A virus[END_REF][START_REF] Dilly | From Naproxen Repurposing to Naproxen Analogues and Their Antiviral Activity against Influenza A Virus[END_REF][START_REF] Zheng | Naproxen Exhibits Broad Anti-influenza Virus Activity in Mice by Impeding Viral Nucleoprotein Nuclear Export[END_REF] . Naproxen also inhibited the replication of the unrelated Zika virus [START_REF] Pan | Nonsteroidal Anti-inflammatory Drugs Potently Inhibit the Replication of Zika Viruses by Inducing the Degradation of AXL[END_REF] . Therefore, from three unrelated single-stranded RNA virus, we suggest that naproxen could present broad-spectrum antiviral properties. Moreover, the combination of clarithromycin, naproxen and oseltamivir reduced mortality of patients hospitalized for H3N2 Influenza infection when compared to oseltamivir alone in a phase IIB/III clinical trial [START_REF] Hung | Efficacy of Clarithromycin-Naproxen-Oseltamivir Combination in the Treatment of Patients Hospitalized for Influenza A(H3N2) Infection: An Open-label Randomized, Controlled, Phase IIb/III Trial[END_REF] .

Conclusion:

We suggest in this study that one of the NSAIDs, naproxen, has a direct antiviral activity on SARS-CoV2 by its effect on the nucleoprotein-mediated replication and possibly via other functions of N, as inhibition of interferon [START_REF] Hu | The Severe Acute Respiratory Syndrome Coronavirus Nucleocapsid Inhibits Type I Interferon Production by Interfering with TRIM25-Mediated RIG-I Ubiquitination[END_REF] . Indeed, the cellular assays demonstrated subsequent inhibition of viral replication by naproxen, with recovery of intact pulmonary epithelium protected from the pandemic virus. These very encouraging results prompted us to test in a clinical trial, named ENACOVID, the effect of addition of naproxen on the standard of care in severely-ill patients. This trial is supported by APHP, Assistance Publique, Hôpitaux de Paris areas and deposited on March 26, https://www.clinicaltrialsregister.eu/ctrsearch/search?query=eudract_number:2020-001301-23.

Experimental:

Modeling: The following X-ray structures extracted from the Protein Data Bank (PDB) have been used: N-terminal domain of SARS-CoV-2 : 6VYO 20 , SARS-CoV-1: 1SSK [START_REF] Huang | Structure of the N-terminal RNA-binding domain of the SARS CoV protein[END_REF] , MERS-CoV: 6KL2/ 6KL5 13 ; The Influenza A nucleoprotein PDB 2IQH was used as a comparison [START_REF] Ye | The mechanism by which influenza A virus nucleoprotein forms oligomers and binds RNA[END_REF] . The binding protocol and subsequent MD are described in the Supplementary data.

Material: Naproxen was purchased from Sigma (reference N5160) Virus: All experiments involving the manipulation of infectious SARS-CoV-2 were performed in biosafety level 3 (BSL-3) facilities, using appropriate protocols. The BetaCoV/France/IDF0571/2020 SARS-CoV-2 strain used in this study was isolated directly from a patient sample as described elsewhere [START_REF] Pizzorno | Characterization and treatment of SARS-CoV-2 in nasal and bronchial human airway épithélium[END_REF] .

(https://www.biorxiv.org/content/10.1101/2020.03.31.017889v1). The dose-response antiviral evaluation of naproxen in Vero E6 cells and in reconstituted human airway epithelia (HAE) were performed as described before https://www.biorxiv.org/content/10.1101/2020.03.31.017889v1 and further detailed in the Supplementary data.

Ackowledgments:

Legends to Figures: The ligand crystallized in this structure is depicted in green for comparison. Panel M shows naproxen binding to Influenza A N that involved electrostatic interactions of the carboxylate with R361, a cation - interaction with R355 and hydrophobic interaction with Y148 and interactions of the last C-terminal F (not shown) with the methyl of the methoxy group.

2 :

 2 Sorbonne University, INSERM, UMR_S 938, Centre de Recherche Saint-Antoine, Cancer biology and therapeutics Team, 75012, Paris, France 3: Sorbonne University, Institut de Biologie Physico-Chimique, Unité Mixte de Recherches 8226 CNRS Laboratoire de Biologie Moléculaire et Cellulaire des Eucaryotes, F-75005, Paris, France 4: Sorbonne University, INSERM, UMR_S 938, Centre de Recherche Saint-Antoine, Metabolism and agerelated joint diseases Team, and Department of Rheumatology, AP-HP Saint-Antoine Hospital, F-75012, Paris, France 5: Sorbonne University, INSERM, UMR_S 938, Centre de Recherche Saint-Antoine, Genetic and acquired lipodystrophies Team, and Institut Hospitalo-Universitaire de Cardio-métabolisme et Nutrition (ICAN), Department of Endocrinology, AP-HP Saint-Antoine Hospital, F-75012, Paris, France 6 : AP-HP, Service d'Urgences -SAMU -SMUR, Hôpital Avicenne, Bobigny, France

  Kcal/mol). Taking together, this work suggests naproxen binding in the N-terminal domains of SARS-CoV-2 N protein and likely to the SARS-CoV-1 and MERS-CoV N in similar binding modes. It is likely that this binding would impede viral RNA binding to N as naproxen binding sites on the N-terminal domain of N are found in the RNA binding groove. Competing with N binding to RNA would repress replication and decrease the level of RNA produced by the virus. The experimental quantification of the viral RNA by RT-qPCR tests this hypothesis in the below antiviral assays. Naproxen inhibits SARS-CoV-2 infection in VeroE6 cells and in reconstituted human airway epithelia (HAE): Based on the docking analysis described above, we then evaluated the potential anti SARS-CoV-2 activity of naproxen in vitro. As shown in Figure 2A-B, naproxen efficiently reduces viral replication in African green monkey VeroE6 cells in a dose-response manner. A unique treatment at 1hour post-infection (hpi) resulted in 50% inhibitory concentration (IC50) values of 46.07 µM at 48 hpi and 59.53 µM at 72 hpi. We determined a cytotoxic concentration CC50 > 1000 µM. This value compares well with the CC50 previously determined in MDCK and A549 cells to be 1605 ± 300 µM 6 . The selectivity index (SI) was SI= CC50/IC50, SI > 21.71 and 19.79, respectively.

Figure 1 :

 1 Figure 1: Structural comparison of viral nucleoproteins N of (A.) influenza A 2IQH.pdb and of aminoterminal domains of (B.) SARS-CoV-1 1SSK.pdb (C.) MERS-CoV 6KL2.pdb (D.) SARS-CoV-2 6VYO.pdb. Electrostatic surface potentials are computed according to Adaptive Poisson-Boltzmann Solver Electrostatics plugin from PyMOL in standard parameters. Electronegative potentials are colored red while electropositive potentials are colored blue. A large electropositive cleft is visible on each protein, pointed with a green arrow; it maps the putative RNA interaction sites. (E.F.G.H.) Main chains flexibilities are approximated as crystallographic b-factors, with main chain ribbon diameter proportional to b-factor value; red is highest b-factor while blue is lowest b-factor. Most mobile elements are identified by a red asterisk (*); they group on one external side of the protein, facing solvent. Mobile elements of highest bfactors are proximal to electropositive surfaces, supporting an interaction scenario where (1) RNA docks onto an electro-complementary surface before (2) the protein conformation is induced into the N-RNA complex of highest stability. (I.) Pairwise structural alignment of equivalent Carbons α of SARS-CoV-1 (blue), MERS-CoV (green) and SARS-CoV-2 (orange) nucleoproteins. The red arrow emphasizes the conserved RWYFYY sequence (J.) Naproxen tridimensional structure in sticks. Frequent residue types in bonding distances in all docking poses are highlighted with arrows: blue, charged electrostatic; red, aromatic; purple, uncharged polar; teal, van der Waals contact. (K.L.M.) Naproxen modelled binding site on amino-terminal RNA interacting domains of (K.) SARS-CoV-1 SSK1.pdb, (L.) MERS-CoV 6KL5.pdb, (M.) SARS-CoV-2 6VYO.pdb. Connoly solvent excluded surfaces are colored white. Residues at a 4 Å distance to naproxen are colored orange. (N.O.P.) Close-up view of docking pockets with residues at a 4 Å distance to naproxen represented in lines. Nitrogen is colored blue, oxygen is colored red. Images were ray traced with PyMOL version 2.0.6. Orientations were selected to highlight discussed properties.

Figure 2 :

 2 Figure 2: Naproxen inhibits SARS-CoV-2 infection in VeroE6 cells and in HAE. (A-B) Dose-response curves of naproxen at 48 and 72 hpi in infected VeroE6 cells. (C) Relative intracellular viral genome quantification and trans-epithelial resistance (TEER in Ohms/cm2) between the apical and basal poles in nasal and bronchial HAE at 48 hpi. Results are expressed in relative viral production compared to the infected untreated control and relative TEER compared to t=0 (before infection). ***P <0.001 and *P <0.05 compared to the infected untreated (viral titers) or uninfected (TEER) groups by Student t-test. Data are representative of three independent experiments.

Funding: This Consortium is grateful to Sorbonne University for granting our project CovNucleovir on a Flash COVID funding of the Medicine Faculty. Professor Frederic Adnet is gratefully acknowledging Assistance Publique-Hôpitaux de APHP for granting the ENACOVID clinical trial. Part of this work was funded by INSERM REACTing (REsearch & ACtion emergING infectious diseases), CNRS, and Mérieux research grants (TO and MRC, VirPath team). We are grateful to Drs HO Bertrand and K. Luckman from Biovia for the gift a temporary licence for Discovery Studio (Dassault Systemes) to SD. # these second authors contributed equally; & MRC and VM contributed equally Corresponding author: Dr Anny Slama-Schwok, CRSA, INSERM U938, Anny.Slama-Schwok@inserm.fr Supporting Informations: contains: supplementary methods and supplementary Figure 1.

Supplementary Material to "Broad-spectrum antiviral activity of naproxen: from Influenza A to SARS-CoV-2 Coronavirus" by Terrier et al.

Content:

Supplementary methods

Supplementary Figure 1 Supplementary methods Modeling: The binding mode of naproxen for the protein structures was assessed in a two-step protocol. First, the binding site was defined by blind docking to the whole protein using the docking program AutoDock Vina 34 . The docking space was defined visually in order to encompass the proteic model with a 2 Å margin at least as a tetragonal box. Three replicas with the default scoring function Vina [START_REF] Trott | AutoDock Vina: Improving the Speed and Accuracy of Docking with a New Scoring Function, Efficient Optimization, and Multithreading[END_REF] were performed to check the convergence of the experiment. The relevance of the binding sites identified by blind docking was confirmed by the cavity detection algorithm of Discovery Studio version 2020, which detected similar sites. A site-docking was then performed using Libdock with energy minimization using smart minimizer (Discovery Studio version 2020) to more precisely identify the naproxen binding mode. In this docking, three replicas were also applied but on a restricted space, encompassing the cavity where the ligand was blind docked. For each protein, the most representative pose of naproxen (i.e. the pose more often found) was selected. The resulting protein-NP complexes were finally refined by a molecular dynamics simulation using the CHARMm force field 35 Dose-response antiviral evaluation in Vero E6 cells Multi-well 6 plates were seeded with VeroE6 cells 24 h before infection (day -1). On day 0, 90%-confluent wells were washed twice with PBS and infected with SARS-CoV-2 at a multiplicity of infection (MOI) of 0.01. One hour after infection, the inoculum was removed and cells were subsequently treated with the indicated dilutions of naproxen in DMEM. DMEM was used as mock-treatment control. Plates were then incubated at 37 °C and 5% CO2. Supernatants were collected at 48 and 72 hpi and stored at -80 °C for viral RNA extraction with the QiAmp viral RNA Kit (Qiagen) and titration by RT-qPCR as described elsewhere [START_REF] Huang | Structure of the N-terminal RNA-binding domain of the SARS CoV protein[END_REF] . Drug 50% cytotoxic concentration (CC50), 50% inhibitory concentration (IC50) and selectivity index (SI) values were calculated using the Quest Graph IC50 calculator (AAT Bioquest).

Evaluation of antiviral activity in reconstituted human airway epithelia (HAE) MucilAir TM reconstituted HAE were obtained from Epithelix SARL (Geneva, Switzerland) and maintained in air-liquid interphase with specific culture medium in Costar Transwell inserts (Corning, NY, USA) according to the manufacturer's instructions. After two washes with warm OptiMEM medium (Gibco, ThermoFisher Scientific), apical poles were inoculated with a 150 μl dilution of virus in OptiMEM medium at a MOI of 0.1. OptiMEM was used as mock-infection control. One hour after viral infection, MucilAir TM culture medium containing or not (untreated) specific dilutions of naproxen in were applied through the basolateral poles. Treatments were repeated at 24 hpi. At 48 hpi, HAE cells were harvested in RLT buffer (Qiagen) and total ARN was extracted using the RNeasy Mini Kit (Qiagen) and stored at -80 °C for subsequent titration by RT-qPCR. Variations in transepithelial electrical resistance (Δ TEER) were measured using a dedicated volt-ohm meter (EVOM2, Epithelial Volt/Ohm Meter for TEER) and expressed as Ohm/cm 2 . Supplementary Figure 1: Each structure is represented in hydrophobic (brown) to hydrophilic (blue) surface (first column, in ribbon (second column), a close-up is shown in the third column. Binding of naproxen to the N-terminal domain of SARS-CoV2 site 1 (most frequent site): panels A, E and I: the carboxylate interacted by polar interactions with S105 and electrostatic interactions with R92, R107, the latter belonging to the sequence R 107 WYFYY 112 of monomer D, L104 of monomer D made hydrophobic contacts with the methyl of the methoxy group, the aromatic core of naproxen made hydrophobic contacts with Y112 (of the sequence R 107 WYFYY 112 ) and stacked on W52. Binding of naproxen to the N-terminal domain of SARS-CoV2 site 2: panels B, F and J: the carboxylate interacted by polar and electrostatic interactions with N150 (monomer A) and R92 (monomer D), respectively, T148 of monomer A made hydrophobic contacts with the methyl of the methoxy group, the aromatic core of naproxen made hydrophobic contacts with I94 (monomer D). Binding of naproxen to the N-terminal domain of SARS-CoV1: panels C, G and K: the carboxylate interacted by electrostatic interactions with R66, the oxygen of the methoxy group made polar interaction with S29, the aromatic core of naproxen was in hydrophobic/ stacking interactions with surrounding Y87 and Y89 of the sequence R 85 WYFYY 90 . Binding of naproxen to the N-terminal domain of MERS-CoV: panels D,H and L: the carboxylate interacted by polar interactions with N66 (monomer D), the methyl group formed hydrophobic contacts with V41 (monomer A), the aromatic core of naproxen stacked on W43 and F135 (monomer D), Y44 and Y102 of the sequence R 97 WYFYY 102 (monomer D) made both hydrophobic interactions and polar interactions (via its OH group) with the methoxy group of naproxen.