key: cord-0947234-1y2d6k87
authors: Marongiu, Laura; Protti, Giulia; Facchini, Fabio A.; Valache, Mihai; Mingozzi, Francesca; Ranzani, Valeria; Putignano, Anna Rita; Salviati, Lorenzo; Bevilacqua, Valeria; Curti, Serena; Crosti, Mariacristina; D’Angiò, Mariella; Bettini, Laura Rachele; Biondi, Andrea; Nespoli, Luca; Tamini, Nicolò; Mancini, Nicasio; Clementi, Nicola; Abrignani, Sergio; Spreafico, Roberto; Granucci, Francesca
title: Maturation signatures of conventional dendritic cells in COVID-19 reflect direct viral sensing
date: 2021-03-03
journal: bioRxiv
DOI: 10.1101/2021.03.03.433597
sha: 258ad7c6620d2e42aa341ed621903353ffdc3af4
doc_id: 947234
cord_uid: 1y2d6k87

Growing evidence suggests that conventional dendritic cells (cDCs) undergo aberrant maturation in COVID-19, and this adversely affects T cell activation. Here, we find that cDC2 subtypes show similar infection-induced gene signatures with an increasing gradient of expression of interferon-stimulated genes from mild to severe patients and a down-regulation of major histocompatibility complex class II (MHC class II) molecules and some inflammatory cytokines compared to the baseline level of healthy donors. In vitro, the direct exposure of cDC2s to the virus recapitulates the type of activation observed in vivo. Our findings provide evidence that SARS-CoV-2 can directly interact with cDC2s and, by down-regulating crucial molecules required for T cell activation, implements an efficient immune escape mechanism. One-Sentence Summary Type 2 conventional dendritic cell functions are altered by direct interaction with SARS-CoV-2

). The low numbers of cDC1s allowed their analysis only in dataset 1. In all DC subsets 147 from the three datasets, when comparing expression profiles from COVID-19 patients with 148 those from HDs, most of the genes up-regulated in COVID-19 were interferon (IFN) stimulated 149 genes (ISGs) (Fig. 1C and Supplementary Fig. 4A,B) . 150 To understand more deeply which biological signaling pathways were differentially regulated Table 3 ) (24) . As it could be predicted by the identified DEGs, in all DC 155 subtypes from the three datasets, maturation was dominated by ISGs while we could not detect 156 the up-regulation of inflammatory signatures containing classical activation markers and 157 cytokines ( Fig. 1D and Supplementary Fig. 5A ). The enrichment of the inflammatory 158 response pathway from the Hallmark collection in DC subsets of COVID-19 patients from 159 datasets 1 and 2 ( Fig. 1D ) was due to an overlap between the genes in this pathway and those 160 from IFN pathways. Indeed, many genes in the leading edge of the inflammatory response 161 pathway were actually ISGs, such as IFITM1, IRF7, LY6E and AXL. (2) . Genes encoding inflammatory molecules, like 173 IL1B and CXCL8, showed a baseline level of expression in HDs that progressively decreased 174 with disease severity (Fig. 1E, lower left panel) . Therefore, ISGs expression distribution 175 showed a strong anti-correlation with that of specific inflammatory cytokines. Furthermore, 176 analysis of the PCA loadings showed that the ISG response was orthogonal to the lineage 177 differentiation markers of DC2s and DC3s (Fig. 1E, right panel) , indicating that both DC2s 178 and DC3s were equally affected by type I IFNs.

This pattern of anti-correlation between ISGs and inflammatory molecules was also evident in 180 the second dataset analyzed (Supplementary Fig. 6B,C,D) and highlights again the unique 181 ISGs response in DC subsets during SARS-CoV-2 infection. 182 The lack of a classical maturation signature in circulating DCs prompted us to ask whether it 183 was possible to identify activated DCs in the blood. It could not be excluded that mature DCs 184 reach circulation too late after activation when they are exhausted. 185 We, therefore, investigated the transcriptional responses of circulating DC2 and DC3 subsets 186 in two distinct publicly available datasets: the dataset from Reyes et al. (25) CITE-seq data of PBMCs from healthy volunteers that received an adenovirus-based vaccine. 191 As previously described, we performed dimensionality reduction and unsupervised clustering 192 to identify DC subpopulations. Our approach clearly identified DC subsets in both datasets 193 ( Fig. 2A and Supplementary Fig. 7) . We then determined DEGs in infected or vaccinated 194 donors with respect to the corresponding HDs (Supplementary Table 4 In addition to IFN-induced pathways, the PI3K-AKT-mTOR pathway was consistently up-211 regulated in all three COVID-19 datasets (Fig. 1D) and a down-regulation of genes encoding 212 MHC class II molecules was also evident (Fig. 3A) . The PI3K-AKT-mTOR pathway is 213 interesting in the context of SARS-CoV-2 infections since it is induced by IL-6 (27), a cytokine 214 particularly relevant in COVID-19 pathogenesis. 215 Therefore, COVID-19 cDCs showed three major features: i) the up-regulation of ISGs and 216 PI3K-AKT-mTOR pathway, ii) the lack of a clear inflammatory signature, iii) the down-217 regulation of MHC class II molecules. We wondered whether these features could be due to 218 the presence of mediators released during SARS-CoV-2 infection or to the direct interaction 219 with the virus. The first cDC characteristic is compatible with both the direct interaction with 220 the virus and the exposure to paracrine cytokines, i.e IFNs and IL-6 produced by bystander 221 cells. By contrast, the systematic down-regulation of genes encoding MHC class II molecules 222 is more likely explained by a direct interaction of cDCs with the virus. This prediction was also 223 supported by evidence that the virus can infect monocyte derived DCs (28) . 224 Therefore, we investigated whether the direct interaction of cDC2s with the virus could induce 225 a similar response to that found in the scRNA-seq datasets. 226 By using IL-6 and MHC class II as readouts, we measured the response to the virus of cDC2s 227 (CD1c + CD19cells) freshly isolated from HDs. As predicted, we found that SARS-CoV-2 228 directly induced a significant down-regulation of MHC class II surface expression and the up-229 regulation of IL-6 in both DC2s and DC3s (Fig. 3B,C) . This suggests that the peculiar cDC (29) . In order to further investigate a potential specific role for 236 DC3s with respect to DC2s, we determined the genes differentially induced/downmodulated 237 by these two subpopulations in response to SARS-CoV-2 infection and bacterial sepsis. 238 Only 52 genes were identified as differentially expressed in DC3s compared with DC2s in were up-regulated and 118 were down-regulated (Fig. 4B) . 243 These findings highlight that the peculiar differences between DC2s and DC3s, which are Despite these differences between SARS-CoV-2 and bacterial infections, a common feature 256 was observed for DC3s in these two clinical conditions. In both cases, genes associated with 257 cell cycle progression (SH3RF1, FSCN1 for COVID-19; GAS2L3, RGCC, TCTN1 for sepsis) 258 were specifically up-regulated in DC3s (Fig. 4A,B) . Moreover, pathway analysis emphasized 259 the up-regulation of the G2M checkpoint and the mitotic spindle pathways in DC3s compared 260 with DC2s in the COVID-19 dataset (Fig. 4C) . Also, a down-regulation in the pathway of the 261 apoptosis was found in both datasets, although in the COVID-19 dataset the apoptosis pathway 262 is not indicated in Figure 4C CoV-2 infection will help identify ad hoc interventions to achieve optimal adaptive responses, 270 a prerequisite for a good prognosis (32) (21) . 

In this study, three different scRNA-seq datasets from COVID-19 patients and healthy controls were analyzed. Supplementary Fig. 1B) . After sorting, cell number and viability were evaluated using an automated cell counter. Viability for each sample was ≥75%. 10,000 cells per sample were loaded on a Chromium Next GEM Chip G (10x Genomics 

Data processing and analysis for all single-cell datasets was performed using the Seurat First, filters were applied to remove low-quality cells. These were based on the number of genes and UMIs detected in each cell and on the percentage of reads mapping to mitochondrial genes. Counts were then normalized and log-transformed using sctransform (33) , while regressing out UMI counts and percentage of mitochondrial counts.

For dimensionality reduction, PCA was performed. Principal components (PCs) were fed to Harmony (34) for batch correction and/or integration of datasets from both disease and healthy conditions. UMAP was used for 2D visualization. Clusters were identified with the shared nearest neighbour (SNN) modularity optimization-based clustering algorithm followed by Louvain or Leiden (check code) community detection. Cell type assignment was manually performed using marker genes, as detailed in figures. cDCs were retained and re-clustered again to identify subsets.

After the identification of cDC subsets, we aggregated cell-level counts into sample-level pseudobulk counts. For each DC subset, only donors with at least 10 cells were retained.

For the dataset from Reyes et al., only samples from the primary cohort were considered for differential analysis due to the low number of DCs obtained from subjects from the secondary cohort.

Differential expression analysis was performed using the quasi-likelihood framework of the edgeR package (35) , using each donor as the unit of independent replication.

Pre-ranked GSEA (36) was performed on the differentially expressed genes (DEGs) using the fgsea package (37) . The Hallmark gene sets (38) and the Blood Transcription Modules (BTM) (24) were used. BTM families analyzed in this study are reported in Supplementary Table 3 .

Code used for data analysis will be made available upon publication.

(A) Gating strategy to identify DCs subsets from PBMCs. Total DCs (cDCs TOT) were detected among the CD11c + MHC-II + and LIN -(CD88, CD89, CD3 and CD19) population. cDC1s were identified as CLEC9A + from the CD14fraction of total DCs. cDC2s (FcεRIα + ) include CD14 + and CD14cells. DC2s and DC3s were identified as CD5 + CD1c + and CD5 -CD1c + respectively. Inflammatory DC3s were recognized as CD14 + CD163 + cells.

marker genes used to identify cDC clusters. Black arrows indicate cDC clusters (cluster 6 is cDC2 and cluster 32 is cDC1). (C) Re-clustering of clusters 6 and 32 corresponding to cDCs.

Clusters 5, 6, 7 and 8 were identified as contaminants. Clusters 0, 1, 2, 3 and 4 were re-clustered in a final iteration to clearly delineate cDC1, DC2 and DC3 subsets as shown in Figure 1B 

GSEA with Blood Transcription Modules (BTM) for datasets 1, 2 and 3. (A) GSEA of DEGs using the BTM collection: dataset 1 (upper panel), dataset 2 (middle panel) and dataset 3 (lower panel)

Dot size encodes the number of genes in the leading edge. NES, normalized enrichment score

Violin plots referred to clusters in (C) showing expression levels of selected marker genes. (E) Violin plots referred to clusters in (C) showing the number of genes and the number of molecules detected in each cluster

Asterisk indicates genes associated with pro-inflammatory functions. Ribosomal protein (RP) genes were removed from the top 100 DEGs. Int-URO, intermediate urosepsis

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