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1

Heightened innate immune responses in the respiratory tract of COVID-19 1

patients 2

Zhuo Zhou1,2,14, Lili Ren2,3,14, Li Zhang4,14, Jiaxin Zhong4,5,14, Yan Xiao2,14, Zhilong Jia6, 3

Li Guo2, Jing Yang4,5, Chun Wang4,5, Shuai Jiang4, Donghong Yang7, Guoliang Zhang8, 4

Hongru Li9, Fuhui Chen10, Yu Xu7, Mingwei Chen11, Zhancheng Gao7, Jian Yang2, Jie 5

Dong2, Bo Liu2, Xiannian Zhang12, Weidong Wang6, Kunlun He6, Qi Jin2, Mingkun 6

Li4,13,2*, Jianwei Wang2,3,15* 7

1. Biomedical Pioneering Innovation Center, Beijing Advanced Innovation Center 8

for Genomics, Peking University Genome Editing Research Center, School of 9

Life Sciences, Peking University, Beijing 100871, China 10

2. National Health Commission of the People’s Republic of China Key Laboratory 11

of Systems Biology of Pathogens and Christophe Mérieux Laboratory, Institute of 12

Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union 13

Medical College, Beijing 100730, China 14

3. Key Laboratory of Respiratory Disease Pathogenomics, Chinese Academy of 15

Medical Sciences & Peking Union Medical College, Beijing, 100730, China 16

4. Beijing Institute of Genomics, Chinese Academy of Sciences, and China National 17

Center for Bioinformation, Beijing, 100101, China 18

5. University of Chinese Academy of Sciences, Beijing 100049, China 19

6. Key Laboratory of Biomedical Engineering and Translational Medicine, Ministry 20

of Industry and Information Technology, Beijing Key Laboratory for Precision 21

Medicine of Chronic Heart Failure, Chinese PLA General Hospital, Beijing, 22

100853, China 23

7. Department of Respiratory and Critical Care Medicine, Peking University 24

People's Hospital, Beijing 100044, China 25

8. National Clinical Research Center for Infectious Diseases, Guangdong Key 26

Laboratory for Emerging Infectious Diseases, Shenzhen Third People's Hospital, 27

Southern University of Science and Technology, Shenzhen 518112, China 28

Manuscript

2

9. Fujian Provincial Hospital, Fujian 350000, China 29

10. Department of respiratory, the Second Affiliated Hospital of Harbin Medical 30

University, Harbin, 150001, China 31

11. Department of Respiratory and Critical Care Medicine, the First Affiliated 32

Hospital of Xi'an Jiaotong University, Shaanxi Province 710061, China 33

12. School of Basic Medical Sciences, Beijing Advanced Innovation Center for 34

Human Brain Protection, Capital Medical University, Beijing 100069, China 35

13. Center for Excellence in Animal Evolution and Genetics, Chinese Academy of 36

Sciences, Kunming, 650223, China 37

14These authors contributed equally 38

15Lead Contact 39

* Correspondence: E-mail: wangjw28@163.com (J. W), limk@big.ac.cn (M. L) 40

41

SUMMARY 42

The outbreaks of 2019 novel coronavirus disease (COVID-19) caused by SARS-CoV-43

2 infection has posed a severe threat to global public health. It is unclear how the human 44

immune system responds to this infection. Here, we used metatranscriptomic 45

sequencing to profile immune signatures in the bronchoalveolar lavage fluid of eight 46

COVID-19 cases. The expression of proinflammatory genes, especially chemokines, 47

was markedly elevated in COVID-19 cases compared to community-acquired 48

pneumonia patients and healthy controls, suggesting that SARS-CoV-2 infection causes 49

hypercytokinemia. Compared to SARS-CoV, which is thought to induce inadequate 50

interferon (IFN) responses, SARS-CoV-2 robustly triggered expression of numerous 51

IFN-inducible genes (ISGs). These ISGs exhibit immunopathogenic potential, with 52

overrepresentation of genes involved in inflammation. The transcriptome data was also 53

used to estimate immune cell populations, revealing increases in activated dendritic 54

cells and neutrophils. Collectively, these host responses to SARS-CoV-2 infection 55

3

could further our understanding of disease pathogenesis and point towards antiviral 56

strategies. 57

INTRODUCTION 58

The COVID-19 outbreak caused by SARS-CoV-2 infection (Lu et al., 2020; Ren et al., 59

2020; Zhou et al., 2020; Zhu et al., 2020) has been declared as a global pandemic. 60

Typical clinical symptoms of COVID-19 are fever, cough, myalgia, and shortness of 61

breath (Huang et al., 2020; Wang et al., 2020). Severe cases often developed acute lung 62

injury, or the fatal form, acute respiratory distress syndrome (ARDS) (Chen et al., 2020; 63

Huang et al., 2020; Wang et al., 2020). To date, no specific antiviral treatment is 64

available for COVID-19. 65

The infection of certain respiratory viruses often resulted in a robust inflammatory 66

response (de Jong et al., 2006; Huang et al., 2005). This immune dysregulation, termed 67

as hypercytokinemia or “cytokine storm”, is often associated with detrimental 68

outcomes, such as ARDS (de Jong et al., 2006). To date, it remains elusive how 69

pathogen infections disrupt the host immune homeostasis and stimulate a hyperactive 70

proinflammatory response. A proposed model suggested that viral evasion of the innate 71

immune response plays a critical role (D'Elia et al., 2013): the virus can effectively 72

blind the immune system, causing an inadequate or delayed response; this immune 73

escape gives rise to the unrestrained virus replication, which eventually results in a 74

hyperactivated proinflammatory response. 75

Type-I IFN (IFN-I) response plays a critical role in combating virus infection mainly 76

by inducing the expression of interferon-stimulated genes (ISGs) that exert antiviral 77

functions (Samuel, 2001). Meanwhile, viruses have developed strategies to counteract 78

IFNs. For example, SARS-CoV can efficiently suppress IFN induction and antagonize 79

IFN’s effect by harnessing its structural and non-structural proteins (de Wit et al., 2016). 80

SARS-CoV infection failed to induce competent IFN response in cultured cells 81

(Cheung et al., 2005; Spiegel and Weber, 2006; Ziegler et al., 2005) and SARS patients 82

(Reghunathan et al., 2005). The inadequate IFN response may account for the 83

4

progressive increase of viral load in SARS patients, the concomitant hypercytokinemia, 84

and the ultimate fatal outcomes. 85

Clinical study on COVID-19 suggested the hypercytokinemia in severe cases (Huang 86

et al., 2020). However, a more comprehensive investigation of the host immune 87

response is needed. Here, we collected the bronchoalveolar lavage fluid (BALF) of 88

eight COVID-19 patients, 146 community-acquired pneumonia patients, and 20 healthy 89

controls, followed by metatranscriptome sequencing and functional analysis. The 90

molecular signatures of the host response, especially the innate immune response to 91

SARS-CoV-2 infection, were explicitly depicted, showing distinct patterns from those 92

seen in SARS and pneumonia caused by other pathogens. 93

94

RESULTS 95

Sample collection, metatranscriptome sequencing, differential expression gene 96

analysis, 97

BALFs were obtained from laboratory-confirmed COVID-19 patients (SARS2) (n = 8), 98

community-acquired pneumonia patients (CAP) (n = 146), and healthy controls without 99

known respiratory diseases (Healthy) (n = 20). The demographic and clinical 100

characteristics of the cohorts were detailed in Table S1. By metatranscriptome 101

sequencing, more than 20 million reads were generated for each BALF sample from 102

SARS2, CAP, and Healthy. Among them, 56% of the reads could be mapped to the 103

human genome and were used for further analysis. As metatranscriptome data enabled 104

us to capture the transcriptionally active microbes, we further classified CAP into two 105

categories: Virus-like CAP (determined by at least 100 viral reads and 10-fold higher 106

than those in the negative controls) and Non-viral CAP. The read counts for SARS-107

CoV-2 in each COVID-19 case were listed in Table S2. 108

5

Differentially expressed genes (DEGs) were then identified by comparing pneumonia 109

transcriptomes with the healthy control transcriptomes (SARS2 versus Healthy 110

(SARS2-H), Virus-like CAP versus Healthy (Vir-H), and Non-viral CAP versus 111

Healthy (NonVir-H)). An adjusted p-value (q-value < 0.05) and fold change (FC) ratio 112

(|log2FC| ≥ 2) were used to determine the DEGs (Tables S3A-3C). The volcano plot 113

showed that the number of DEGs in SARS2-H was markedly higher than that in Vir-H 114

and NonVir-H, suggesting that SARS-CoV-2 infection strongly perturbed 115

transcriptome homeostasis of cells in the lung (Figure 1A). The principal component 116

analysis revealed that SARS2 formed distinct clusters, while Virus-like CAP, and Non-117

viral CAP clusters were more widely dispersed and overlapped (Figure 1B), showing 118

the disparate characteristics of DEGs in SARS2 from others. 119

We next collated genes whose expression is mostly regulated (|log2FC| ≥ 5) in SARS2-120

H (Figure S1A). Expression levels of proinflammatory cytokine and chemokine genes 121

(IL-1B, CXCL17, CXCL8, and CCL2), typical antiviral ISGs (IFIT and IFITM family 122

genes), and calgranulin genes that exert pleiotropic functions in inflammatory disorders 123

(S100A8, S100A9, and S100A12) were upregulated in SARS2 as compared to Healthy 124

(Figure S1A). Meanwhile, we observed marked upregulation of IL1RN and SOCS3 125

(Figure S1A), both of which encode cytokine signaling antagonists, suggesting that the 126

negative feedback loops were elicited. Genes involved in morphogenesis and migration 127

of immune cells (NCKAP1L, DOCK2, SPN, and DOCK10) were underexpressed 128

(Figure S1A). A host of DEGs was also observed when comparing CAPs to Healthy, 129

albeit with much lower fold changes (Figure S1A). 130

131

Global functional analyses 132

We then performed global functional analyses. For pathway analysis, we applied the 133

Ingenuity Pathway Analysis (IPA) and KEGG analyses on DEGs. In IPA, “Interferon 134

Signaling” ranked first in the upregulated pathways in SARS2 (z-score = 3.3, Figure 135

1C). This pathway is also upregulated, though to a lesser extent, in Virus-like CAP (z-136

score = 1), whereas not in the Non-viral CAP (Figure 1C), indicating robust IFN 137

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response in SARS-CoV-2 infection. The pathways involved in the inflammatory 138

response in SARS2 was revealed by both analyses, with upregulated "Role of IL-17F 139

in Allergic Inflammatory Airway Diseases" (z-score = 3.0) and "Chemokine Signaling" 140

(z-score = 1.6) in IPA (Figure 1C), along with “Chemokine signaling pathway”, “IL-17 141

signaling pathway”, “TNF signaling pathway”, and “NF-κB signaling pathway” in 142

KEGG (q < 0.05, Figure S1B). Moreover, both IPA and KEGG analyses showed that 143

DEGs from SARS2-H were strongly and specifically enriched in mRNA translation-144

related categories, with up-regulated “EIF2 Signaling” (z-score = 3.3, Figure 1C) in IPA 145

and “Ribosome” (q < 0.05, Figure S1B) in KEGG. Intriguingly, several neuron 146

function-related pathways were specifically downregulated in SARS2 by IPA analysis, 147

including "CREB Signaling in Neurons" (z-score = -1.1), "Synaptogenesis Signaling 148

Pathway" (z-score = -1.4), and "Endocannabinoid Neuronal Synapse Pathway" (z-score 149

= -2.1, Figure 1C). This unexpected observation awaits further investigation. 150

151

Next, we build protein-protein interaction (PPI) networks with the upregulated DEGs 152

in SARS2 by STRING (Figures 1D and S2). A dominant network comprising several 153

subnetworks were generated using the confidence score of 0.9. Genes in the top two 154

enriched KEGG categories (Figure S1B), namely “Ribosome” and “Chemokine 155

signaling pathway”, formed two most densely connected subnetworks, which are 156

mainly composed of ribosomal proteins and chemokines, respectively (Figures 1D). 157

Overall, our global functional analysis revealed a highly responsive state against 158

noxious stimuli in COVID-19 cases, characterized by the potent defense responses and 159

hyperactive ribosome biogenesis. 160

161

Cytokine profiles 162

To understand the cytokine profile in BALF from the COVID-19 patients, we assigned 163

DEGs into seven categories of cytokine-related genes (Figure 2A). In categories of 164

“Chemokine” and “Receptor”, multiple genes were more remarkably upregulated in 165

7

SARS2 as compared to those in CAPs. Several genes were regulated in the “Interleukin” 166

and “Tumor necrosis factor” categories, and few were modulated in other categories 167

(Figure 2A). To examine potential cytokine expression dynamics, we aligned individual 168

cases according to the increasing days between sampling and symptom onset. 169

Expression levels of cytokine-related genes seem to decrease over time, with one 170

patient (C4) who eventually deceased being the outlier (Figure 2A). These observations 171

suggested that the exuberant inflammation in COVID-19 could be progressively 172

resolved, and unquenched inflammation may result in detrimental outcomes. 173

174

Chemokines are predominant among upregulated cytokine-related genes in SARS2 175

(Figure 2A). CXCL17 ranked first in the upregulated chemokines. CXCL17 176

upregulation is specific to SARS2 and is observed in all eight cases (Figure 2B), 177

suggesting a role of CXCL17 in COVID-19 pathogenesis. CXCL8, the archetypal 178

neutrophil chemoattractant, together with CXCL and CXCL2, are upregulated (Figure 179

2B). These chemokines are thought to be critical for recruiting neutrophils into the 180

flamed lung (Donnelly et al., 1993; Frevert et al., 1995; Miller et al., 1992). CXCR2, 181

the receptor for CXCL8, CXCL1, and CXCL2, is also markedly upregulated (Figure 2B). 182

Moreover, we observed the upregulation of CCL2 and CCL7, both of which are vital 183

for monocytes recruitment (Shi and Pamer, 2011) (Figure 2B). By presenting individual 184

cases in the heatmap, one case (C1) with ultra-high viral reads (Table S2) exhibited the 185

most pronounced chemokine upregulation (Figure 2B), suggesting that higher virus 186

replication resulted in a more robust proinflammatory response. Corroborating this, in 187

three cases (C1, C2, and C5) sampled at the same time after symptom onset (day 8), the 188

increases of viral load corresponded with the upregulations of critical chemokines, 189

including CXCL8, CCL2, CCL7, etc. (Figure 2B). Both CAPs showed less DEGs and 190

lower expression of cytokine genes as compared to SARS2 (Figure 2B). 191

192

IL1RN and ILB are interleukin genes significantly upregulated in SARS2 (Figure 2B), 193

and these upregulations are mirrored by elevated protein levels of IL-1Ra and IL-1β in 194

plasma of COVID-19 patients (Huang et al., 2020). Because IL-1β was recognized as 195

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the key cytokine driving proinflammatory response in BALF from patients with ARDS 196

(Pugin et al., 1996), it is possible that the ratio of IL-1β to its inhibitor, IL-1Ra, may 197

correlate to inflammation intensity in COVID-19 patients; however, we did not observe 198

clear correlation in the current study (Figure 2B). 199

Signatures of IFN response 200

We then examined the IFN response in COVID-19 patients. By intersecting our data 201

with a list comprising 628 ISGs (Mostafavi et al., 2016), we found that SARS2 showed 202

more markedly elevated expression of ISGs as compared to CAPs, and the expression 203

seems decreased over time, again with the fatal case (C4) being the outlier (Figure 3A). 204

Eighty-three ISGs were significantly elevated in SARS2, suggesting the robust IFN 205

response (Table S4). ISGs known to exert direct antiviral activity were upregulated, 206

including IFIT and IFITM genes that exert broad-spectrum antiviral functions (IFIT1, 207

-2, -3, and IFITM1, -2, -3) and others (ISG15 and RSAD2) (Diamond and Farzan, 2013; 208

Helbig and Beard, 2014; Perng and Lenschow, 2018) (Figure 3B). Among those, 209

IFITMs have been shown to inhibit cellular entry of SARS-CoV and MERS-CoV 210

(Huang et al., 2011; Wrensch et al., 2014). Moreover, we observed the increased 211

expression of IFIH1, TANK, IRF7, and STAT1, which may further potentiate IFN 212

signaling (Figure 3B). 213

Other than antiviral, ISGs may exert diverse functions. We assigned these ISGs to five 214

previously established functional clusters (Mostafavi et al., 2016), including RNA 215

processing (C1 and C2), IFN regulation/antiviral function (C3), metabolic regulation 216

(C4), and inflammation regulation (C5) (Table S4). Surprisingly, ISGs are substantially 217

enriched in the cluster of C5 (29 out of 83) (Figure 3B), which mainly comprises 218

inflammation mediators or regulators. CCL2 and CXCL10, two IFN-inducible 219

chemokine genes found in C5 (Figure 3B), were also elevated in peripheral blood of 220

patients with SARS (Cameron et al., 2007). These results pointed to the 221

immunopathological role of IFN response in COVID-19 patients. Further, using a 222

curated ISG list from patient blood transcriptomes (Mostafavi et al., 2016) and data 223

from peripheral blood of SARS patients (Cameron et al., 2007; Reghunathan et al., 224

9

2005), we compared the ISG distributions in the five clusters between SARS-CoV-2 225

and other viruses. ISGs identified in infections of yellow fever virus (live attenuated 226

vaccine), HCV, EBV, and dengue virus were primarily assigned to C3 with less 227

involvement of C5, while SARS-CoV-2 exhibited overly high C5 distribution (Figure 228

3C, Table S4). Compared to other viruses, SARS-CoV infections induced much lesser 229

ISGs (Figure 3C, Table S4). Altogether, our results unveiled the unique signatures of 230

IFN response in SARS-CoV-2 infection. 231

232

Cell composition analyses 233

The compositions of BALF cells could reflect immune cell profiles in the affected lung 234

(Costabel and Guzman, 2001). Using the transcriptome data, we estimated immune cell 235

types and their proportions by CIBERSORT (Newman et al., 2015). Similar to findings 236

in BALF from healthy adults (Meyer et al., 2012), the BALF cell components in 237

Healthy were mainly macrophages and lymphocytes (Figure 4A). This result supported 238

the accuracy of the BALF cell sequencing and the effectiveness of our analytical 239

approach. Among innate immune cells, activated dendritic cells, activated mast cells, 240

and neutrophils were more abundant in viral pneumonia groups than those in Healthy 241

(Figure 4B). SARS2 showed a more pronounced increase in neutrophils than other 242

pneumonia (Figure 4B). Compared to innate immune cells, the composition of T cells 243

and B cells was less varied among pneumonia groups and Healthy (Figure S3A). A 244

recent study suggested that the neutrophil-to-lymphocyte (NLR) ratio was associated 245

with the disease severity of COVID-19 (Liu et al., 2020). Our analysis also showed a 246

significantly higher NLR in COVID-19 cases (Figure S3A). The highest NLR was 247

observed in the case with ultra-high viral reads and robust cytokine/ISG expression 248

(Figures S3B and S3C). 249

250

DISCUSSION 251

10

Many of the transcriptomic profiling studies have been conducted by using intermittent 252

samples such as peripheral blood (Chaussabel, 2015). However, some infections, such 253

as avian influenza or SARS, occur in the lower respiratory tract (Gu and Korteweg, 254

2007; van Riel et al., 2006), where the immune response was triggered and regulated. 255

Thus, blood transcriptome may not be optimal for profiling the immunopathological 256

features in those scenarios. In the current study, we sampled COVID-19 patients with 257

bronchoalveolar lavage (BAL), a method for retrieving cells and solutes from different 258

areas of the lung (Meyer et al., 2012). By sequencing and analyzing the BAL fluid cells 259

from eight COVID-19 patients and control cohorts, we obtained gene expression 260

profiles of both host and virus, which directly reflected the in situ host response against 261

SARS-CoV-2 infection. We thus provided a valuable opportunity to garner insights into 262

COVID-19 pathogenesis. 263

264

In COVID-19 patients, we observed the upregulation of a plethora of proinflammatory 265

cytokines, suggesting the pathogenic role of hypercytokinemia. The most salient feature 266

of the cytokine profile is the chemokine response, as seen from the elevated expression 267

of multiple chemokines and their receptors. Among those, various neutrophil 268

chemoattractants were upregulated. Consonant with this, the higher neutrophil amount 269

was observed in SARS2 by cell composition analysis. Also, chemoattractants for 270

monocytes and other immune cells were significantly upregulated. In patients with 271

ARDS, the alveolar spaces are occupied by the infiltrating neutrophils and monocytes 272

(Matute-Bello et al., 2008), suggesting the pathogenic role of those cells. Thus, our data 273

unraveled a possible chemokine-dominant hyperactive cytokine response in COVID-274

19 cases. 275

276

In contrast to SARS-CoV, SARS-CoV-2 triggered a robust IFN response, hallmarked 277

by the expression of numerous ISGs, including IFITMs shown to counteract SARS-278

CoV and MERS-CoV (Huang et al., 2011; Wrensch et al., 2014). This protective 279

potential of ISGs may account for the lower proportion of severe cases and the case-280

fatality rate in COVID-19 as compared to SARS (Wu and McGoogan, 2020). 281

11

Nevertheless, high SARS-CoV-2 loads were detected very early after symptom onset 282

(Zou et al., 2020), suggesting that the virus may have developed countermeasures 283

against the IFN system, such as delaying the IFN response by inhibiting innate immune 284

signaling. Moreover, although robust ISG induction was observed, we failed to detect 285

significant upregulation of IFNs. This discrepancy needs further investigation. 286

IFN-I could also be pathogenic. In the mouse model, depletion of IFN-/ receptor 287

protects mice from lethal infection of SARS-CoV (Channappanavar et al., 2016). 288

Moreover, delayed IFN- treatment failed to inhibit virus replication and aggravated 289

pulmonary inflammation in the mice infected with MERS-CoV (Channappanavar et al., 290

2019). These data suggested that the timing of IFN therapy against SARS and MERS 291

is critical. In the ISGs identified in COVID-19 cases, we found a subset of genes with 292

proinflammatory activities, which is relatively underrepresented in other viral 293

infections. These “proinflammatory ISGs” may assist the antiviral protection by 294

amplifying inflammatory signals to the environment. However, they could also be 295

deleterious in infections, especially when hypercytokinemia has already been triggered. 296

As IFN treatment has been adopted as an antiviral therapy against SARS-CoV-2 297

infection (ChiCTR2000029600), the timing and dose should be carefully considered. 298

299

ACKNOWLEDGMENTS 300

We thank Dr. Xue Yongbiao (China National Center for Bioinformation) and Dr. Wang 301

Jianbin (Tsinghua University, Beijing, China), Dr. Yanyi Huang (Peking University) for 302

technical assistance and helpful discussions. This work was supported by grants from 303

the National Major Science & Technology Project for Control and Prevention of Major 304

Infectious Diseases in China (2017ZX10204401, 2018ZX10301401, 305

2017ZX10103004, 2018ZX10305409), Innovation Fund for Medical Sciences (2016-306

I2M-1-014), National Natural Science Foundation of China (31670169, 31470267, 307

31701155), National Key Research and Development Program of China 308

(2017YFC0908403), Non-profit Central Research Institute Fund of Chinese Academy 309

of Medical Sciences (2019PT310029), the Open Project of Key Laboratory of Genomic 310

and Precision Medicine, Chinese Academy of Sciences, and Beijing Advanced 311

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Innovation Center for Genomics (ICG). 312

313

AUTHOR CONTRIBUTIONS 314

Conceptualization, J.W., M. L., L. R., and Q. J.; Lab work, L.R., Y.X., L.G., and J.D.; 315

Data QC, J.Z., M.L., Jian Yang, and B.L.; High-throughput sequencing, X.Z.; 316

Transcriptomic analysis, Z.Z., L.Z., J.Z., Z.J., Jing Yang, C.W., S.J., W.W., and K.H.; 317

Clinical sample management, L.R., Y.X., D.Y., G.Z., H.L., F.C., Y.X., M.C., and Z.G.; 318

Writing - Original Draft, Z.Z., L.Z., J.Z., M.L., and J.W.; Writing - Review & Editing, 319

J.W., M.L., Z.Z., L.Z., and L.R. 320

321

DECLARATION OF INTERESTS 322

The authors declare no competing interests. 323

324

FIGURE LEGENDS 325

Figure 1. Analysis of DEGs in BALF of COVID-19 and CAP patients comparing 326

to healthy controls. (A) Volcano plot of DEGs comparing SARS2 versus Healthy 327

(SARS2-H), Virus-like CAP versus Healthy (Vir-H), and Non-viral CAP versus 328

Healthy (NonVir-H). The names of DEGs with the top 20 absolute FC are shown. (B) 329

PCA loading plot based on all DEGs. Autoscaling of data was performed. (C) 330

Functional enrichment analysis of DEGs with IPA. Asterisks (*) indicate q-values < 331

0.05 and z-score ≥ 1. (D) PPI network of up-regulated DEGs in SARS2 comparing to 332

Healthy. Each node represents a protein, and interactions with confidence score > 0.9 333

are presented. See also Figure S2. 334

335

Figure 2. Cytokine-related gene expressions in COVID-19 and CAP patients. (A) 336

Heatmap of 218 genes encoding cytokines and receptors. (B) Heatmap of DEGs 337

encoding cytokines and receptors. SARS2 samples (n = 8) were ordered by days after 338

symptom onset (DSO) in the right panel of A and B. Asterisks (*) indicate significant 339

DEGs (absolute log2FC ≥ 2, q-value < 0.05). Relative viral reads (calculated by the 340

ratio of SARS-CoV-2 reads to human reads) and the ratios of IL1B to IL1RN were 341

13

shown in (A) and (B). 342

343

Figure 3. Expression of ISGs in COVID-19 and CAP patients. (A) Heatmap of 628 344

ISGs. SARS2 samples (n = 8) were ordered by days after symptom onset (DSO) in the 345

right panel. (B) Heatmap of 83 up-regulated ISGs in SARS2 comparing to Healthy. 346

Asterisks (*) indicate significant DEGs (absolute log2FC ≥ 2, q-value < 0.05). (C) 347

Upregulated ISGs in SARS-CoV-2 infection identified in this study, as well as in SARS-348

CoV and other viral infections (see Table S4). In (B) and (C), ISGs were assigned into 349

five biclusters. 350

351

Figure 4. Composition of immune cells in BALF predicted from transcriptome 352

data. (A) The proportion of nine major immune cell types. (B) The proportion of 12 353

innate immunity-related cell subtypes. Asterisks represent significant differences 354

between groups (*q-value < 0.05, **q-value < 0.01, ***q-value < 0.001, Mann-355

Whitney test). See also Figure S3. 356

357

STAR METHODS 358

RESOURCE AVAILABILITY 359

Lead Contact 360

Further information and requests for resources and reagents should be directed to and 361

will be fulfilled by the Lead Contact, Jianwei Wang (wangjw28@163.com). 362

363

Materials Availability 364

This study did not generate new unique reagents. 365

366

Data and Code Availability 367

Reads mapped to human genome GRCh38 have been extracted from the raw 368

sequencing data and deposited in the Genome Warehouse in National Genomics Data 369

Center (National Genomics Data Center and Partners, 2020), under project number 370

PRJCA002273 that is publicly accessible at https://bigd.big.ac.cn/gsa. 371

14

EXPERIMENTAL MODEL AND SUBJECT DETAILS 372

BALF samples from eight laboratory-confirmed COVID-19 patients (SARS2) were 373

collected from hospitals in Wuhan in January 2020. BALFs from 146 community-374

acquired pneumonia patients (CAP) and 20 healthy controls from volunteers without 375

any known pulmonary diseases (Healthy) were collected from Peking University 376

People's Hospital, Shenzhen Third People's Hospital, Fujian Provincial Hospital, the 377

Second Affiliated Hospital of Harbin Medical University, and the First Affiliated 378

Hospital of Xi'an Jiaotong University between 2014 and 2018. CAP was diagnosed 379

following guidelines of the Infectious Diseases Society of America and the American 380

Thoracic Society (Mandell et al., 2007). The known respiratory pathogens in CAP 381

patients were screened by using FTD Respiratory pathogens kit (Fast Track Diagnostics, 382

Luxembourg). The study was approved by the Institutional Review Board of hospitals 383

where sampling was carried out. The data collection for the COVID-19 patients were 384

deemed by the National Health Commission of the People's Republic of China as the 385

contents of the public health outbreak investigation. 386

The written informed consent was obtained from all subjects before inclusion. Of the 387

COVID-19 cases, five were male and three were female. Of the CAP cases, 75 were 388

male and 54 were female. Eight COVID-19 cases had an average age of 49 years (range 389

40-61), and 112 CAP cases had an average of 56.4 years (range 22-91). The healthy 390

subjects were pre-screened, and 20 had no known respiratory diseases were included as 391

healthy control. Currently or potentially immunocompromised subjects with active 392

cancer, primary immunodeficiency disorders, HIV infection, or tuberculosis infection 393

were excluded from the study. Subjects with a recent medical record of taking 394

immunosuppressive medications were excluded from the study. The detailed 395

information of the subjects could be found in the demographic and clinical Table (Table 396

S1). 397

398

METHODS DETAILS 399

Sample Preparation and Sequencing 400

BALF samples were obtained during bronchoscopies done as part of clinical 401

15

management. In the biosafety level (BSL)-III laboratory, a 200 ul aliquot of each BALF 402

sample from COVID-19 patients were lysed in Trizol LS (Thermo Fisher Scientific, 403

Carlsbad, CA, USA), followed by RNA extraction using a Direct-zol RNA Miniprep 404

kit (Zymo Research, Irvine, CA, USA) according to the manufacturer's instructions. 405

RNA extractions from other samples were conducted following the same protocol in 406

the BSL-II laboratory. 10 ul of purified RNA was used for cDNA generation and library 407

preparation using an Ovation Trio RNA-Seq Library Preparation Kit (NuGEN, CA, 408

USA) according to the manufacturer's instructions. Three samples, including two saline 409

solutions passing through the bronchoscope and one nuclease-free water, were included 410

as the negative controls. Metatranscriptome sequencing was performed on an Illumina 411

HiSeq 2500/4000 platform (Illumina, United Kingdom). 412

413

Data Processing and Analysis 414

Raw sequencing reads were quality controlled as previously described (Shen et al., 415

2020). Briefly, the FASTQ files were subjected to adapter trimming, low quality reads 416

removal, and short reads removal using Fastp 0.20.0 (Chen et al., 2018). All clean data 417

were mapped to the human genome GRCh38 using HISAT2 v2.1.0 (Kim et al., 2019) 418

with default parameters. Bam files were sorted by Samtools 1.9 (Li et al., 2009). 419

Gene counts were summarized using the featureCounts program (Liao et al., 2014) as 420

part of the Subread package release 2.0.0 (http://subread.sourceforge.net/). To identify 421

differentially expressed genes (DEGs) between two groups, genes that were present in 422

less than 50% of samples in both groups and genes with average counts per million 423

(CPM) lower than five in both groups were removed. Remained gene counts were then 424

normalized using the quantile method and the voom method. DEGs were determined 425

with the threshold adjusted p-value < 0.05 and absolute logged fold-change (Log2FC) 426

≥ 2 using the Bioconductor limma package v3.42.2 (Ritchie et al., 2015). In presenting 427

the individual case of SARS2, the fold change of each gene was calculated as the ratio 428

of normalized gene expression of each SARS2 individual to the mean expression of 429

Healthy. 430

For the functional enrichment of DEGs, Ingenuity Pathway Analysis (IPA, Ingenuity 431

16

Systems, Inc.) (Kramer et al., 2014) and Kyoto Encyclopedia of Genes and Genomes 432

(KEGG) pathway analysis (Kanehisa and Goto, 2000) were applied via IPA December 433

2019 release (v499329) and the Bioconductor clusterProfiler package v3.14.3 (Yu et al., 434

2012), respectively. Protein-protein interaction (PPI) networks of upregulated DEGs 435

were built using STRING v11 (Szklarczyk et al., 2019) with a confidence score 436

threshold of 0.9 and plotted with Cytoscape v3.7.1 (Shannon et al., 2003). 437

To infer the composition of immune cells in BALF, raw gene counts were normalized 438

as transcripts per million (TPM) and processed using the CIBERSORT algorithm v1.06 439

(Newman et al., 2015) with the original CIBERSORT gene signature file LM22 and 440

100 permutations. Twenty-two immune cell subtypes were obtained and further 441

summarized into nine major immune cell types. Samples with p‐ value < 0.05, which 442

reflects the statistical significance of the deconvolution results across all cell subsets, 443

were included. 444

445

QUANTIFICATION AND STATISTICAL ANALYSIS 446

Fisher’s exact test was used for categorical variables, and the Mann-Whitney test was 447

used for continuous variables that do not follow a normal distribution. P-values from 448

multiple testing were adjusted (q-value) using the Benjamini-Hochberg false discovery 449

rate (FDR) with a significance level of 0.05 (Benjamini and Hochberg, 1995). 450

451

SUPPLEMENTAL EXCEL TABLE TITLE AND LEGENDS 452

Table S1. Demographic and clinical information, Related to Figure 1 and STAR 453

Methods 454

Demographic and clinical information of all subjects were shown. The criterion for 455

virus infection (Virus) is at least supported by 100 viral reads and should be 10 times 456

higher than the abundance in the negative control by sequencing. 457

458

Table S3. DEGs in BALF of COVID-19 and CAP patients comparing to healthy 459

controls, Related to Figure 1 460

17

DEGs identified by comparing SARS2 to Healthy (Table S3A), Virus-like CAP to 461

Healthy (Table S3B), and Non-viral CAP to Healthy (Table S3C) were presented. An 462

adjusted p-value (q-value < 0.05) and fold change (FC) ratio (|log2FC| ≥ 2) were used 463

to determine the DEGs. 464

465

Table S4. ISGs in SARS-CoV-2 and other viral infections, Related to Figure 3 466

ISGs identified in viral infections were assigned to five previously established 467

functional clusters, including RNA processing (C1 and C2), IFN regulation/antiviral 468

function (C3), metabolic regulation (C4), and inflammation regulation (C5). 469

470

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KEY RESOURCES TABLE

The table highlights the genetically modified organisms and strains, cell lines, reagents, software, and source data essential to reproduce results presented in the manuscript. Depending on the nature of the study, this may include standard laboratory materials (i.e., food chow for metabolism studies), but the Table is not meant to be comprehensive list of all materials and resources used (e.g., essential chemicals such as SDS, sucrose, or standard culture media don’t need to be listed in the Table). Items in the Table must also be reported in the Method Details section within the context of their use. The number of primers and RNA sequences that may be listed in the Table is restricted to no more than ten each. If there are more than ten primers or RNA sequences to report, please provide this information as a supplementary document and reference this file (e.g., See Table S1 for XX) in the Key Resources Table.

Please note that ALL references cited in the Key Resources Table must be included in the References list. Please report the information as follows:

REAGENT or RESOURCE: Provide full descriptive name of the item so that it can be identified and linked with its description in the manuscript (e.g., provide version number for software, host source for antibody, strain name). In the Experimental Models section, please include all models used in the paper and describe each line/strain as: model organism: name used for strain/line in paper: genotype. (i.e., Mouse: OXTRfl/fl: B6.129(SJL)-Oxtrtm1.1Wsy/J). In the Biological Samples section, please list all samples obtained from commercial sources or biological repositories. Please note that software mentioned in the Methods Details or Data and Software Availability section needs to be also included in the table. See the sample Table at the end of this document for examples of how to report reagents.

SOURCE: Report the company, manufacturer, or individual that provided the item or where the item can obtained (e.g., stock center or repository). For materials distributed by Addgene, please cite the article describing the plasmid and include “Addgene” as part of the identifier. If an item is from another lab, please include the name of the principal investigator and a citation if it has been previously published. If the material is being reported for the first time in the current paper, please indicate as “this paper.” For software, please provide the company name if it is commercially available or cite the paper in which it has been initially described.

IDENTIFIER: Include catalog numbers (entered in the column as “Cat#” followed by the number, e.g., Cat#3879S). Where available, please include unique entities such as RRIDs, Model Organism Database numbers, accession numbers, and PDB or CAS IDs. For antibodies, if applicable and available, please also include the lot number or clone identity. For software or data resources, please include the URL where the resource can be downloaded. Please ensure accuracy of the identifiers, as they are essential for generation of hyperlinks to external sources when available. Please see the Elsevier list of Data Repositories with automated bidirectional linking for details. When listing more than one identifier for the same item, use semicolons to separate them (e.g. Cat#3879S; RRID: AB_2255011). If an identifier is not available, please enter “N/A” in the column.

o A NOTE ABOUT RRIDs: We highly recommend using RRIDs as the identifier (in particular for antibodies and organisms, but also for software tools and databases). For more details on how to obtain or generate an RRID for existing or newly generated resources, please visit the RII or search for RRIDs.

Please use the empty table that follows to organize the information in the sections defined by the subheading, skipping sections not relevant to your study. Please do not add subheadings. To add a row, place the cursor at the end of the row above where you would like to add the row, just outside the right border of the table. Then press the ENTER key to add the row. Please delete empty rows. Each entry must be on a separate row; do not list multiple items in a single table cell. Please see the sample table at the end of this document for examples of how reagents should be cited.

Key Resource Table

TABLE FOR AUTHOR TO COMPLETE

Please upload the completed table as a separate document. Please do not add subheadings to the Key Resources Table. If you wish to make an entry that does not fall into one of the subheadings below, please contact your handling editor. (NOTE: For authors publishing in Current Biology, please note that references within the KRT should be in numbered style, rather than Harvard.)

KEY RESOURCES TABLE

REAGENT or RESOURCE SOURCE IDENTIFIER

Antibodies

Bacterial and Virus Strains

Biological Samples

Human bronchoalveolar lavage fluid samples Peking University People's Hospital, Shenzhen Third People's Hospital, Fujian Provincial Hospital, the Second Affiliated Hospital of Harbin Medical University, the First Affiliated Hospital of Xi'an Jiaotong University, and hospitals in Wuhan

NA

Chemicals, Peptides, and Recombinant Proteins

Trizol LS Thermo Fisher Scientific

Cat#10296028

Critical Commercial Assays

Direct-zol RNA Miniprep Kit Zymo Research Cat#R2051

Trio RNA-Seq Library Preparation Kit NuGEN Cat#0357-32

Deposited Data

Reads mapped to human genome GRCh38 extracted from the raw sequencing data

This paper Genome Warehouse in National Genomics Data Center https://bigd.big.ac.cn/gsa; Project number: PRJCA002273

Experimental Models: Cell Lines

Experimental Models: Organisms/Strains

Oligonucleotides

Recombinant DNA

Software and Algorithms

HISAT2 v2.1.0 Kim et al., 2019 http://ccb.jhu.edu/software/hisat2/

FeatureCounts 2.0.0 Liao et al., 2014 http://subread.sourceforge.net/

Fastp 0.20.0 Chen et al., 2018 https://github.com/OpenGene/fastp

Quantile normalization (Bioconductor limma package v3.42.2)

Ritchie et al., 2015 https://bioconductor.org/packages/release/bioc/html/limma

Ingenuity Pathway Analysis, December 2019 release (v499329)

Kramer et al., 2014 https://digitalinsights.qiagen.com/

Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis (Bioconductor clusterProfiler package v3.14.3)

Yu et al., 2012 https://bioconductor.org/packages/release/bioc/html/clusterProfiler

STRING v11 Szklarczyk et al., 2019 https://string-db.org

Cytoscape v3.7.1 Shannon et al., 2003 https://cytoscape.org

CIBERSORT algorithm v1.06 Newman et al., 2015 https://cibersort.stanford.edu/

Samtools 1.9 Li et al., 2009 http://samtools.sourceforge.net/

Other

TABLE WITH EXAMPLES FOR AUTHOR REFERENCE

REAGENT or RESOURCE SOURCE IDENTIFIER

Antibodies

Rabbit monoclonal anti-Snail Cell Signaling Technology Cat#3879S; RRID: AB_2255011

Mouse monoclonal anti-Tubulin (clone DM1A) Sigma-Aldrich Cat#T9026; RRID: AB_477593

Rabbit polyclonal anti-BMAL1 This paper N/A

Bacterial and Virus Strains

pAAV-hSyn-DIO-hM3D(Gq)-mCherry Krashes et al., 2011 Addgene AAV5; 44361-AAV5

AAV5-EF1a-DIO-hChR2(H134R)-EYFP Hope Center Viral Vectors Core

N/A

Cowpox virus Brighton Red BEI Resources NR-88

Zika-SMGC-1, GENBANK: KX266255 Isolated from patient (Wang et al., 2016)

N/A

Staphylococcus aureus ATCC ATCC 29213

Streptococcus pyogenes: M1 serotype strain: strain SF370; M1 GAS

ATCC ATCC 700294

Biological Samples

Healthy adult BA9 brain tissue University of Maryland Brain & Tissue Bank; http://medschool.umaryland.edu/btbank/

Cat#UMB1455

Human hippocampal brain blocks New York Brain Bank http://nybb.hs.columbia.edu/

Patient-derived xenografts (PDX) Children's Oncology Group Cell Culture and Xenograft Repository

http://cogcell.org/

Chemicals, Peptides, and Recombinant Proteins

MK-2206 AKT inhibitor Selleck Chemicals S1078; CAS: 1032350-13-2

SB-505124 Sigma-Aldrich S4696; CAS: 694433-59-5 (free base)

Picrotoxin Sigma-Aldrich P1675; CAS: 124-87-8

Human TGF-β R&D 240-B; GenPept: P01137

Activated S6K1 Millipore Cat#14-486

GST-BMAL1 Novus Cat#H00000406-P01

Critical Commercial Assays

EasyTag EXPRESS 35S Protein Labeling Kit Perkin-Elmer NEG772014MC

CaspaseGlo 3/7 Promega G8090

TruSeq ChIP Sample Prep Kit Illumina IP-202-1012

Deposited Data

Raw and analyzed data This paper GEO: GSE63473

B-RAF RBD (apo) structure This paper PDB: 5J17

Human reference genome NCBI build 37, GRCh37 Genome Reference Consortium

http://www.ncbi.nlm.nih.gov/projects/genome/assembly/grc/human/

Nanog STILT inference This paper; Mendeley Data

http://dx.doi.org/10.17632/wx6s4mj7s8.2

Affinity-based mass spectrometry performed with 57 genes

This paper; and Mendeley Data

Table S8; http://dx.doi.org/10.17632/5hvpvspw82.1

Experimental Models: Cell Lines

Hamster: CHO cells ATCC CRL-11268

D. melanogaster: Cell line S2: S2-DRSC Laboratory of Norbert Perrimon

FlyBase: FBtc0000181

Human: Passage 40 H9 ES cells MSKCC stem cell core facility

N/A

Human: HUES 8 hESC line (NIH approval number NIHhESC-09-0021)

HSCI iPS Core hES Cell Line: HUES-8

Experimental Models: Organisms/Strains

C. elegans: Strain BC4011: srl-1(s2500) II; dpy-18(e364) III; unc-46(e177)rol-3(s1040) V.

Caenorhabditis Genetics Center

WB Strain: BC4011; WormBase: WBVar00241916

D. melanogaster: RNAi of Sxl: y[1] sc[*] v[1]; P{TRiP.HMS00609}attP2

Bloomington Drosophila Stock Center

BDSC:34393; FlyBase: FBtp0064874

S. cerevisiae: Strain background: W303 ATCC ATTC: 208353

Mouse: R6/2: B6CBA-Tg(HDexon1)62Gpb/3J The Jackson Laboratory JAX: 006494

Mouse: OXTRfl/fl: B6.129(SJL)-Oxtrtm1.1Wsy/J The Jackson Laboratory RRID: IMSR_JAX:008471

Zebrafish: Tg(Shha:GFP)t10: t10Tg Neumann and Nuesslein-Volhard, 2000

ZFIN: ZDB-GENO-060207-1

Arabidopsis: 35S::PIF4-YFP, BZR1-CFP Wang et al., 2012 N/A

Arabidopsis: JYB1021.2:

pS24(AT5G58010)::cS24:GFP(-G):NOS #1

NASC NASC ID: N70450

Oligonucleotides

siRNA targeting sequence: PIP5K I alpha #1: ACACAGUACUCAGUUGAUA

This paper N/A

Primers for XX, see Table SX This paper N/A

Primer: GFP/YFP/CFP Forward: GCACGACTTCTTCAAGTCCGCCATGCC

This paper N/A

Morpholino: MO-pax2a GGTCTGCTTTGCAGTGAATATCCAT

Gene Tools ZFIN: ZDB-MRPHLNO-061106-5

ACTB (hs01060665_g1) Life Technologies Cat#4331182

RNA sequence: hnRNPA1_ligand: UAGGGACUUAGGGUUCUCUCUAGGGACUUAGGGUUCUCUCUAGGGA

This paper

N/A

Recombinant DNA

pLVX-Tight-Puro (TetOn) Clonetech Cat#632162

Plasmid: GFP-Nito This paper N/A

cDNA GH111110 Drosophila Genomics Resource Center

DGRC:5666; FlyBase:FBcl0130415

AAV2/1-hsyn-GCaMP6- WPRE

Chen et al., 2013

N/A

Mouse raptor: pLKO mouse shRNA 1 raptor Thoreen et al., 2009 Addgene Plasmid #21339

Software and Algorithms

ImageJ Schneider et al., 2012 https://imagej.nih.gov/ij/

Bowtie2 Langmead and Salzberg, 2012

http://bowtie-bio.sourceforge.net/bowtie2/index.shtml

Samtools Li et al., 2009 http://samtools.sourceforge.net/

Weighted Maximal Information Component Analysis v0.9

Rau et al., 2013 https://github.com/ChristophRau/wMICA

ICS algorithm This paper; Mendeley Data

http://dx.doi.org/10.17632/5hvpvspw82.1

Other

Sequence data, analyses, and resources related to the ultra-deep sequencing of the AML31 tumor, relapse, and matched normal.

This paper http://aml31.genome.wustl.edu

Resource website for the AML31 publication

This paper https://github.com/chrisamiller/aml31SuppSite

FPR1

CXCL10CXCL8

CXCL2

CCL4

GPR183

C3AR1 P2RY12HCAR2

IL1B

HCAR3RPLP2

RPS27

MRPS10

RPS15A

RSRC1

EIF3H

RPL32

RPL7A

SRPRB

RIOK2

RPL37

EIF1AXRPL36AL

RPS25

RPS14

RPS4X

RPL30

RPS13

RPS18

RPS23

RPL13A

CCL20P2RY14

FFAR2

GNG5

ADM2

PROK2

ADM

GNG10

CCL8

OXTR

PTGFR

CCL2

ANXA1

CXCL6CXCL11

LPAR3

GNG12

CCL7

CCL3

CCR1

HEBP1CCL4L1

FPR3 CXCR2

CXCR1

CXCL1SRP54

RIOK3

GSPT1

RPS8

PNO1

RPL24

RPL28

NOB1

RPL31

RPL8

RPS27L

UPF3A

SEC11C

RPS9

RPL26RPL35A

Color LegendKEGG-hsa03010 RibosomeKEGG-hsa04062 Chemokine signaling pathway

NonVir-HVir-H

SARS2-H

C D

IPA Z-score

Interferon SignalingEIF2 SignalingRole of IL−17F in Allergic Inflammatory Airway DiseasesCholecystokinin/Gastrin−mediated SignalingCorticotropin Releasing Hormone SignalingB Cell Receptor SignalingOsteoarthritis Pathwayp53 SignalingFcγ RIIB Signaling in B LymphocytesChemokine SignalingGNRH SignalingAryl Hydrocarbon Receptor SignalingActivation of IRF by Cytosolic Pattern Recognition ReceptorsFcγ Receptor−mediated Phagocytosis in Macrophages and MonocytesProtein Kinase A SignalingHepatic Fibrosis Signaling PathwayRole of NFAT in Regulation of the Immune ResponseLeukocyte Extravasation SignalingCD28 Signaling in T Helper CellsCREB Signaling in NeuronsSynaptogenesis Signaling PathwayLXR/RXR ActivationEndocannabinoid Neuronal Synapse PathwayPPAR SignalingCalcium−induced T Lymphocyte ApoptosisGPCR−Mediated Nutrient Sensing in Enteroendocrine Cells

−3−2−10123

A B

−20

0

20

−25 0 25

PC1 (21%)

PC2

(6.3

%)

HealthySARS2

Non-viral CAP

Virus-like CAP

MSMB

FN1

WFDC2

SCD

S100A8

LRP1SERPINB3

CXCL17

IFIT1

GPNMB

SLC11A1

STATH

EHF

SPRR2A

MTRNR2L1

OLR1

MS4A14

IFITM3

MARCKS

0

2

4

6

8

−5 0 5

−log

10 (q

−val

ue)

Up 1014Down 739

OLR1

MSR1

SCD CXCL8

MS4A7

LYZ

MRC1

TFRC

MME

EML4

G0S2COLGALT1

SERPING1

GPRIN3 SLC11A1

FMNL2

VSIG4

SRRM2PECAM1

FN1

−5 0 5

Up 112Down 243

SCD

MRC1

OLR1MS4A7

MSR1

LYZ

SNTB1

SRRM2

HEATR5B

CD74

CHRDL1

DENND1C

GPNMBMS4A14

HLA−DQA1TFRC

EML4

SERPING1

−5 0 5

Up 113Down 223

Log2 (fold change)

SARS2-H Vir-H NonVir-H

Figure 1

A

NonVir-H Vir-H

SARS2-H C8 C7C1C2 C5 C6 C4C3

DSO14

4

ClusterInterleukinChemokineInterferonTumor necrosis factorColony−stimulating factorOther cytokinesReceptor

C8 C7 C1 C2 C5 C6 C4 C3

IL1RNIL1BIL4CXCL17CXCL8CCL2CXCL1CCL4CXCL10CXCL2CCL3CCL20CXCL6CCL7CXCL11CCL8CXCL16TNFSF10TNFSF13BTNFSF12OSMCCR1CXCR2IL5RACXCR1IFNGR2IL1R1OSMRIFNAR1IL1R2CSF3RIL17RAIL4RIL10RATNFRSF13C

B

NonVir-H Vir-H

SARS2-H

−6

0

6Log2FC

Relative viral reads101

10-2

IL1B/IL1RN1.2

0.2

Figure 2

B

SARS-CoV

-2

SARS-CoV

SARS-CoV

prec

risis

Yello

w Feve

rHCV

EBV

Dengu

e pred

ict

A

NonVir-H Vir-H SARS2-H

DSO

14 4 Cluster C1 RNA process C2 RNA process C3 IFN regulators−antiviral effectors

C4 Metabolic regulation C5 Inflammation

EIF4E3SELLNMIIRF2PCGF5NBNTMEM123TMEM60ANXA1PHACTR2AIDACAPZA2TMX1C1GALT1CIR1SSBARL5BELF1DENND1BNME7CD2APHBP1SMCHD1ARHGAP12ASCC3GPD2RNF138FOXN2TANKFMR1BET1RSBN1LIFIT1IFITM3IFITM2IFITM1IFIT2BAG1ISG15IFIH1TNFSF10RSAD2CASP4IFIT3CHMP5EPSTI1IRF7STAT1LYSMD2SAT1PSMB8

ZFP36PHACTR4CSRNP1MARCKSIL1RNCCL2CCR1NINJ1KLF6SAMSN1CXCL10CCRL2PLSCR1DAPP1CCL3SNX10C3AR1B4GALT5NAMPTSLAMF7TNFSF13BRGS1FPR3GADD45BRNF19BLYNFGL2GCH1OPTNADMP2RY14PMAIP1

C

−606

Log2FC

C8 C7 C1 C2 C5 C6 C4 C3

Relative viral reads

101 10-2

Figure 3

*

*

0.0

0.1

0.2

0.3NK cells resting

0.00

0.05

0.10

0.15

NK cells activated

0.0

0.1

0.2

0.3

0.4

0.5

Monocytes

****

0.0

0.2

0.4

0.6

0.8Macrophages M0

0.0

0.1

0.2

Macrophages M1***

0.0

0.2

0.4

0.6Macrophages M2

* * * *

0.0

0.1

0.2

0.3

0.4

Dendritic cells resting

****

* ***

0.00

0.05

0.10

0.15

0.20

Dendritic cells activated

*

**

0.00

0.05

0.10

0.15

0.20

Mast cells resting

* * *

0.0

0.2

0.4

0.6

Mast cells activated

0.00

0.05

0.10

0.15

0.20Eosinophils

** * *

0.0

0.2

0.4

0.6

0.8

NeutrophilsC

ell p

ropo

rtion

s

0.00

0.25

0.50

0.75

1.00

Healthy SARS2 Virus−like CAP Non-viral CAP

Cell typesB cells

Dendritic

Eosinophils

Macrophages

Mast cells

Monocyte

Neutrophils NK cellsT cells

A B

Averge cell proportions

Group Healthy SARS2 Virus-like CAP Non-viral CAP

Figure 4