key: cord-1021351-2acpx5wo
authors: Bordoni, Veronica; Matusali, Giulia; Mariotti, Davide; Antonioli, Manuela; Cimini, Eleonora; Sacchi, Alessandra; Tartaglia, Eleonora; Casetti, Rita; Grassi, Germana; Notari, Stefania; Castilletti, Concetta; Fimia, Gian Maria; Capobianchi, Maria Rosaria; Ippolito, Giuseppe; Agrati, Chiara
title: The interplay between SARS-CoV-2 infected airway epithelium and immune cells modulates the immunoregulatory/inflammatory signals
date: 2022-01-31
journal: iScience
DOI: 10.1016/j.isci.2022.103854
sha: 9be9afb044cfc922f607e6b341e325a96b340246
doc_id: 1021351
cord_uid: 2acpx5wo

To assess the cross talk between immune cells and respiratory tract during SARS-CoV-2 infection, we analysed the relationships between the inflammatory response induced by SARS-CoV-2 replication and immune cells phenotype in a reconstituted organotypic human airway epithelium (HAE). The results indicated that immune cells failed to inhibit SARS-CoV-2 replication in HAE model. In contrast, immune cells strongly affected the inflammatory profile induced by SARS-CoV-2 infection, dampening the production of several immunoregulatory/inflammatory signals (e.g., IL-35, IL-27 and IL-34). Moreover, these mediators were found inversely correlated with innate immune cell frequency (NK and γδ T cells) and directly with CD8 T cells. The enriched signals associated with NK and CD8 T cells highlighted the modulation of pathways induced by SARS-CoV-2 infected HAE. These findings are useful to depict the cell-cell communication mechanisms necessary to develop novel therapeutic strategies aimed to promote an effective immune response.

The clinical presentation of SARS-CoV-2 infection ranges from asymptomatic cases to mild upper respiratory tract infection to bilateral pneumonia with acute respiratory distress syndrome (ARDS) and multiple organ failure (Guan et al., 2020; Huang et al., 2020; Zhou et al., 2020) . Pathological examination indicates that SARS-CoV-2 targets primarily the airways tract and the lungs (Tian et al., 2020; Xu et al., 2020) . COVID-19 infection is characterized by inflammatory infiltrates in the alveolar space and by a systemic inflammatory cytokine storm, suggesting the role of an excessive immune response in damaging lung function Liao et al., 2020; Song et al., 2020) .

The inflammatory storm is able to orchestrate pleiotropic activities, resulting in tissue damage, thrombotic events, lymphocytes apoptosis and finally affecting the generation of a balanced and protective immune response Mehta et al., 2020) . Induction of interferons appears to be limited in the more severe clinical cases, suggesting an imbalance between protective antiviral and detrimental inflammatory cytokine responses (Blanco-Melo et al., 2020; Hadjadj et al., 2020) .

orchestrating and inducing antiviral responses rather than an excessive inflammation. Cytokines and chemokines produced by infected epithelial cells are able to recruit, activate, and coordinate innate and adaptive immune functions. While a majority of studies focused on the interactions between respiratory epithelial and innate myeloid immune cells, few data are available on respiratory epithelium/lymphocytes cross talk. For example, RSV-infected epithelial cells promote a T cellindependent antibody response, which is important for protection against reinfection (McNamara et al., 2013) . Furthermore, the strong interactions between epithelial and immune cells described in nasopharyngeal and bronchial samples from severe COVID-19 patients are likely to contribute to epithelial cell death (Chua et al., 2020) .

J o u r n a l P r e -p r o o f and has been used to characterize SARS-CoV-2 cell tropism, replication, and pathogenesis Ravindra et al., 2021; Zhou et al., 2020; Hao et al., 2020; Pizzorno et al., 2020) .

Here, to better understand the interplay between immune cells and the epithelial tis-sue during SARS-CoV-2 replication, we analysed the impact of SARS-CoV-2 infection on inflammatory response and immune cells phenotype in an organotypic co-culture of human bronchial epithelium and immune cells model.

To investigate the cross talk between SARS-CoV-2-infected human airway epithelium (HAE) and the immune cells, we set up a co-culture model. Specifically, we cultured primary Human Bronchial Epithelial Cells at an air-liquid interface for 21 days and then challenged the apical surface of the epithelium with SARS-CoV-2. Soon after the removal of viral inoculum, the HAE was co-cultured with peripheral blood mononuclear cells (HAE-PBMC) . The characterization of viral replication and inflammation profile, as well as the modulation of both innate and adaptive immune subsets (NK, monocytes, γδ T cells, CD4 and CD8 T cells) were performed after 6 days in SARS-CoV-2 infected or not infected HAE and HAE-PBMC. The enriched network and signaling pathways were also evaluated ( Figure 1 ).

The ability of airway epithelium to sustain SARS-CoV-2 replication, and the impact of immune cells co-culture were analysed. SARS-CoV-2 replication was measured at two and six days post infection (p.i.) comparing viral RNA levels released by or associated with epithelial cells, as well as by measuring the infective titres of the viral particles shed to the apical side of the HAE culture model.

In HAE, an increase in released and cell associated SARS-CoV-2 RNA levels was detected along the culture and no effect of PBMC on viral replication was noticed ( Figure 2A ). The same profile was observed when measuring the infectious titre (TCID50) of viral particles released by HAE or HAE-PBMC ( Figure 2A ). In this model, SARS-CoV-2 did not infect PBMC in the lower chamber (data not shown). SARS-CoV-2 infection of HAE and HAE-PBMC was also verified by immunofluorescence for the viral Spike protein, as reported in Figure 2B .

We then focused on the role of immune cells in modulating the antiviral response triggered by SARS-CoV-2 infection of the airway epithelium. The gene expression levels of IFN-α increased after SARS-CoV-2 infection both in HAE and in HAE-PBMC ( Figure 2C ). The expression of IFN-β, IFNstimulated genes (ISG15 and ISG56), as well as the truncated form of ACE2, known to have an ISG function (Onabajo et al., 2020) , showed a slight increase, although they did not reach the statistical significance ( Figure 2C ). Furthermore, SARS-CoV-2 infection did not change the gene expression levels of the two main viral entry factors ( Figure 2C ), namely ACE2 and TMPRSS2 (Hoffmann et al., 2020) .

Correlation matrix analysis showed a coordinate IFNs response in HAE culture where viral RNA directly correlates with ISG15, IFN-α and IFN-β; ISG15 correlated with IFNs and ISG56 with IFNβ ( Figure 2D ), suggesting the activation of IFN signaling by viral replication. In the presence of PBMC, these correlations seemed to be even stronger and new ones appeared ISG15 correlated with ISG56 and ACE2, ISG56 correlated with viral RNA and, finally, ACE2 correlated with IFN-β. These results suggested that viral replication in HAE model induced a Type I-IFN signaling independently from the presence of immune cells.

To evaluate the impact of immune cells on the inflammatory profile induced by SARS-CoV-2 infection, we quantified 36 key biomarkers, belonging to the TNF superfamily proteins, IFN family proteins, regulatory cytokines, and Matrix metalloproteinases (MMPs) in SARS-CoV-2 infected HAE-PBMC. PCA analysis was therefore performed in order to identify the major trends inherent to the inflammatory profile. Unsupervised PCA of 36 soluble mediators seemed more efficient at segregating HAE cultures on the basis of the presence of immune cells than on the basis of infection.

As showed in Figure 3A : not infected and SARS-CoV-2 infected HAE cultures (blue and red points respectively) are grouped on the third and fourth quadrants of score plot, while not infected and J o u r n a l P r e -p r o o f infected HAE-PBMC are grouped on the opposite (first and second) quadrants (purple and green points), suggesting that the immune cells strongly shape the inflammatory response. The quantification of all the mediators in SARS-CoV-2 infected HAE and in HAE-PBMC are shown in Table 1 . In the absence of PBMC, 33/36 cytokines screened, showed a slight increase after SARS-CoV-2 infection (Table 1) . Of note, the presence of immune cells significantly dampened the infection-induced cytokine production of 10 mediators (highlighted in bold, Table 1 ). Specifically, in infected HAE-PBMC, the immune cells reduced: i) cytokines involved in IFN responses (IFN-β and IFN-λ2); ii) matrix metalloproteinase (MMP3); iii) immunoregulatory cytokines, IL-27, IL-35, IL-34 and sTNFR1; iv) sCD30/TNFRSF8, gp130/sIL6RB, and IL-32, involved in the inflammatory response ( Figure 3B ).

J o u r n a l P r e -p r o o f J o u r n a l P r e -p r o o f

To verify if SARS-CoV-2 infected HAE may modulate PBMC phenotype we performed a multiparametric flow cytometry analysis of co-cultured PBMCs, focusing on NK cells (CD56 dim, bright and negative subsets), γδ T cells, monocytes, and adaptive CD4 and CD8 T cells. The results showed a significant decrease of innate cells (CD56+bright and γδ T cells) and a weak increase of CD8+ T cells between not infected and SARS-CoV-2 infected HAE-PBMC. Of note, the frequency of CD8 T cells was positively correlated with SARS-CoV-2 RNA (r=0.78, p=0.02). No significant differences were observed when looking at CD16+ and CD16 negative monocytes and CD4 T cells ( Figure 4 ).

To define possible associations among the immunological parameters and the inflammatory profile in not infected and SARS-CoV-2 infected HAE-PBMC, we performed a correlation matrix among the (36+8) continuous variables and build a protein-protein network using Cytoscape (STRING App). Collectively, these data showed that in SARS-CoV-2 infected HAE-PBMC co-cultures, 5 of 10 soluble mediators that were modulated by the presence of immune cells, resulted correlated to CD8

T cells, NK-bright and γδ T cells in these ways: IFN-λ as well as IL-35 were directly correlated with CD8 T cells frequency; MMP3 as well as IL-35 inversely correlated with γδ T cells frequency; IL-27 as well as IL-34 inversely correlated with NK CD56 bright frequency.

In order to explore the enrichment of biological pathways resulting from inflammatory profile and NK cells subset association, we performed STRING Enrichment analysis, grouping soluble factors based on their association with specific lymphomonocytes subset, in the presence or absence of SARS-CoV-2 infection. In SARS-CoV-2 infected HAE-PBMC, the pathways associated to NK-dim cells subsets were profoundly affected, losing most of the associations founded in the uninfected conditions ( Figure 5C ). Interestingly, during SARS-CoV-2 infection, the CD8 T cells subset was associated to RIG-I pathway.

J o u r n a l P r e -p r o o f DISCUSSION Epithelial cells that span the length of the respiratory tract respond to viral infection by producing soluble mediators that communicate with immune cells to activate and regulate the antiviral response.

Organotypic cell culture of human airway epithelial cells (HAE) has been successfully used to isolate SARS-CoV-2 and developed to widely characterize SARS-CoV-2 cell tropism, replication, and pathogenesis Ravindra et al., 2021; Hao et al., 2020; Pizzorno et al., 2020) . However, the cross-talk between immune cells and the infected HAE during SARS-CoV- has been observed in lung cancers and pulmonary infections, although its role is not well defined (Baghdadi et al., 2018; Zhou et al., 2018) . γδ T cells frequency was reduced in SARS-CoV-2-infected HAE co-cultures and negatively correlated with IL-35. Accordingly, a decrease of γδ T cell has been observed in severe COVID-19 patients (Carissimo et al., 2020; Lei et al., 2020) , and a possible protective role has also been proposed in SARS-CoV infection (Poccia et al., 2006) .

Interestingly the analysis of enrichment pathway resulting from 36 soluble mediators associated with different immune cells subsets, revealed that the RIG-I pathway (a key activator of type I interferon (IFN) antiviral signaling) was enriched in infected HAE-PBMC and correlated to CD8 T cells.

Notably, a high concentration of RIG-I is found around tight junctions, suggesting that they could act as apical foci for antiviral sensors (Mukherjee et al., 2009 ).

Altogether, we described a strict interplay between immunoregulatory/inflammatory signals (e.g., IL-

can be useful to develop novel therapeutic strategies aimed at promoting effective immune responses against respiratory viruses.

A major limitation of this work, as for all the in vitro model of infection, is that the airway epithelial and immune cell co 

The authors declare no competing interests.. 

Further information and requests for reagents may be directed to and will be fulfilled by the Lead Contact, Chiara Agrati (chiara.agrati@inmi.it).

This Study did not generate new unique reagents.

The data presented in this study are openly available at http://rawdata.inmi.it.

This paper does not report original code. All code utilized in the paper is available online and listed in the key resources table.

Any additional information required to re-analyze the data reported in this paper is available from the lead contact upon request To estimate the production of infectious SARS-CoV-2, serial dilution of HAE cell supernatants were put in contact with sub-confluent VeroE6 cells seeded in 96-well plates in MEM containing 2% FBS.

At day 5 after infection, cells were observed for cytopathic effect CPE and TCID50/ml was measured and analysed by Reed-Muench method.

The inflammation factor levels were analysed using a Bio-Plex Pro™ Human Inflammation Panel 1, 37-Plex (#171AL001M, Bio-Rad) according to the manufacturer's instructions. 0.05 mL of each collected supernatant, at six days after infection, were used for the assay. The lecture of the plate was performed with BioPlex ® MAGPIX Multiplex Reader.

Transwells after six days from the infection were fixed in PAF 4% diluted in PBS 1× for 20 min at RT. The transwells were cut with a sterile scalpel and laid down in a new 24 cell culture well plate with the side cells located face-up. Then, transwells were washed with PBS 1× three times for five min each and blocked with Blocking Buffer (2% Goat Serum from Invitrogen; 1% BSA from Sigma Aldrich; 0.1% Fish Gelatin Blocking Agent from Biotium; 0.1% Triton X-100 and 0.05% Tween20 from Sigma Aldrich in 1×PBS) for one hour at RT. Later, blocking buffer was removed, transwells were washed as before, 0.45 mL of primary antibodies (anti-ZO1 from Invitrogen; anti-SARS-CoV-2 spike antibody from GeneTex; in Primary Antibody Buffer (1% BSA and 0.1% Fish Gelatin

Blocking Agent in 1×PBS) were added to each transwell and incubated overnight at 4°C in the dark.

The following day primary antibodies were removed, transwells were washed as before, 0.45 mL of secondary antibodies (anti-Mouse IgG1 AF488 and anti-Rabbit IgG Alexa-647 from Sigma Aldrich) diluted 1:1000 in Secondary Antibody Buffer (1× PBS) were added to each transwell and incubated for two hours at RT in the dark. Afterwards, secondary antibodies were removed, transwells were washed as before and membrane cell was mounted side up on slide using one drop of SlowFade Gold

Antifade Mountant with DAPI (from Invitrogen). Therefore the images were acquired using a Leica THUNDER 3D Live Cell Imaging system (Leica Application Software (LAS) X 3.7.2; Leica Microsystems) using THUNDER Computational Clearing Settings at 63X magnification.

J o u r n a l P r e -p r o o f Cystoscape software (3.8.2 version) and STRING App were used to build protein-protein network using a 0,4 as confidence cutoff. STRING Enrichment analysis was performed to retrieve human KEGG pathways associated with different lymphomonocytes subset; redundant terms were filtered with 0,5 cutoff. RStudio software was used to build a bubble plot using ggplot2-based visualization.

Principal component analysis (PCA) was performed in order to identify the relevant information and visualize major trends inherent to the immunological profile. Data were analysed using RStudio software from http://www.rstudio.org with the libraries FactoMineR (for the analysis) and factoextra (for ggplot2-based visualization). To visualize a correlation matrix in R we used the corrplot function and generate a Heatmap object using correlation coefficients (computed using the Spearman) as input to the Heatmap. The heatmap was produced with the R package heatmap3. Quantitative variables were compared with nonparametric Wilcoxon test. Friedman along with Dunn's multiple comparisons test evaluated the statistical differences between more than two conditions. A p value lower than 0.05 was considered statistically significant. Statistical analyses were performed using GraphPad Prism v8.0 (GraphPad Software, Inc). More details of the statistical analysis are included in Figure legends . 

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