key: cord-0880706-pf799wbw
authors: de Lamballerie, Claire Nicolas; Pizzorno, Andrés; Fouret, Julien; Szpiro, Lea; Padey, Blandine; Dubois, Julia; Julien, Thomas; Traversier, Aurélien; Dulière, Victoria; Brun, Pauline; Lina, Bruno; Rosa-Calatrava, Manuel; Terrier, Olivier
title: Transcriptional profiling of immune and inflammatory responses in the context of SARS-CoV-2 fungal superinfection in a human airway epithelial model
date: 2020-05-19
journal: bioRxiv
DOI: 10.1101/2020.05.19.103630
sha: f5b0bcbcf793a5ae64cd098c88138e1896a3b324
doc_id: 880706
cord_uid: pf799wbw

Superinfections of bacterial/fungal origin are known to affect the course and severity of respiratory viral infections. An increasing number of evidence indicate a relatively high prevalence of superinfections associated with COVID-19, including invasive aspergillosis, but the underlying mechanisms remain to be characterized. In the present study, to better understand the biological impact of superinfection we sought to determine and compare the host transcriptional response to SARS-CoV-2 versus Aspergillus superinfection, using a model of reconstituted humain airway epithelium. Our analyses reveal that both simple infection and superinfection induce a strong deregulation of core components of innate immune and inflammatory responses, with a stronger response to superinfection in the bronchial epithelial model compared to its nasal counterpart. Our results also highlight unique transcriptional footprints of SARS-CoV-2 Aspergillus superinfection, such as an imbalanced type I/type III IFN, and an induction of several monocyte- and neutrophil associated chemokines, that could be useful for the understanding of Aspergillus-associated COVID-19 and but also management of severe forms of aspergillosis in this specific context.

prevalence of superinfections associated with COVID-19, including invasive aspergillosis, but 23 the underlying mechanisms remain to be characterized. In the present study, to better 24 understand the biological impact of superinfection we sought to determine and compare the 25 host transcriptional response to SARS-CoV-2 versus Aspergillus superinfection, using a model 26 of reconstituted humain airway epithelium. Our analyses reveal that both simple infection and 27 superinfection induce a strong deregulation of core components of innate immune and 28 inflammatory responses, with a stronger response to superinfection in the bronchial epithelial 29 model compared to its nasal counterpart. Our results also highlight unique transcriptional 30 footprints of SARS-CoV-2 Aspergillus superinfection, such as an imbalanced type I/type III IFN, 31 and an induction of several monocyte-and neutrophil associated chemokines, that could be 32 useful for the understanding of Aspergillus-associated COVID-19 and but also management 33 of severe forms of aspergillosis in this specific context.

The current pandemic of novel coronavirus disease 2019 , caused by severe acute 37 respiratory syndrome coronavirus 2 (SARS-CoV-2) began in Wuhan, Hubei province, China, 38 in December 2019. As of May 18, 2020, there have been more than 4,628,903 confirmed 39 COVID-19 cases in the world as reported by the WHO, including 312,009 deaths (WHO). characterized and validated in terms of viral production, impact on trans-epithelial resistance 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 149 signature that recapitulates the overall signature of a simple CoV infection, but with both a 150 particularly distinct regulation of the inflammatory response and the additional regulation of 151 many biological processes related to the physiology of epithelia. Of note, we observed a 152 relatively similar global pattern of regulation between the nasal and bronchial HAE models, 153 differing primarily in the magnitude rather than the nature of the responses to CoV and 154 CoV+Asp superinfection.

In order to explore in more depth the transcriptomic signature of the superinfection, we then signaling pathways (Fig. 2C) , which is consistent with the most upregulated DEGs shown in 179 Fig. 2A and Extended data file 2. To better visualize these observations, we applied a protein-180 protein interactions analysis using STRING network to investigate the DEGs corresponding to 181 several Reactome and GO terms (immune system process, cytokine signaling in immune system, inflammatory response, interleukin-10 signaling) enriched in the bronchial and nasal superinfection signatures along with their functional interactions ( Fig. 3A and 3B Invasive pulmonary aspergillosis (IPA), which typically occurs in an immunocompromised host,

represents an important cause of morbidity and mortality worldwide (Clancy and Nguyen, 198 2020). Superinfections were extensively documented in the case of influenza infections, with 199 the latter being usually described to "pave the way" for bacterial superinfections, but several 200 severe influenza cases have also been reported to develop invasive pulmonary aspergillosis 201 5/19/2020 9:59:00 AM. An increasing amount of evidence points towards a relatively high 202 prevalence of superinfections, including invasive aspergillosis, to be associated with COVID-203 19 (Alanio et al., 2020; Lescure et al., 2020; Zhou et al., 2020) . However, the underlying 204 mechanisms remain to be characterized. In the present study, we sought to better understand (Pizzorno et al., 2020) . 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 differences of infectivity and consecutive host responses between different cell subsets (type 225 II pneumocytes, nasal goblet secretory cells) are linked to varying ACE2/TMPRSS2 levels,

ACE2 expression being linked to the IFN response (Ziegler et al., 2020) . The discrepancies 227 we observed in the two HAE models could be explained by differences of cell type composition 228 that could be interesting to further explore using combinations of additional experimental 229 models, including ACE2/TMPRSS3 expression and single cell RNA-seq approaches. 

Our data are not entirely consistent with these findings. Whereas we also demonstrate a very 

higher this probability is, the less informative is the child term relative to its parents. This is 367 also a statistic that can be used for independent filtering to reduce the p-value adjustment 368 burden. Therefore, all terms with a p_min probability higher than 10e-4 were filtered out before 369 p-values adjustment using Bonferroni method (Dunn, 1961) . This multiple-testing correction 

Extended Data Table 2 571

Statistics of RNA-Seq fragment pseudo-alignment to the human transcriptome. 572

GO cell junction organization:GO Extracellular matrix organization:RC extracellular structure organization:GO Cell adhesion:KW biological adhesion:GO

GO reproductive process:GO regulation of cell population proliferation:GO regulation of cell death:GO cell surface receptor signaling pathway:GO Signal Transduction:RC regulation of developmental process:GO multicellular organismal process:GO regulation of multicellular organismal process:GO regulation of cell communication:GO regulation of signaling:GO signal transduction:GO regulation of response to stimulus:GO developmental process:GO response to stimulus:GO negative regulation of biological process:GO positive regulation of biological process:GO biological regulation:GO Epidermolysis bullosa:KW endodermal cell differentiation:GO Binding and Uptake of Ligands by Scavenger Recepto

Immune System:RC protein localization to cilium:GO Ciliopathy:KW cytoskeleton−dependent intracellular transport:GO Cilium biogenesis/degradation:KW Organelle biogenesis and maintenance:RC microtubule−based process:GO cell projection organization:GO locomotion:GO regulation of locomotion:GO regulation of cellular component movement:GO movement of cell or subcellular component:GO regulation of localization:GO signaling:GO