key: cord-309556-xv3413k1
authors: Chow, Ryan D.; Chen, Sidi
title: The aging transcriptome and cellular landscape of the human lung in relation to SARS-CoV-2
date: 2020-04-15
journal: bioRxiv
DOI: 10.1101/2020.04.07.030684
sha: 
doc_id: 309556
cord_uid: xv3413k1

Since the emergence of SARS-CoV-2 in December 2019, Coronavirus Disease-2019 (COVID-19) has rapidly spread across the globe. Epidemiologic studies have demonstrated that age is one of the strongest risk factors influencing the morbidity and mortality of COVID-19. Here, we interrogate the transcriptional features and cellular landscapes of the aging human lung through integrative analysis of bulk and single-cell transcriptomics. By intersecting these age-associated changes with experimental data on host interactions between SARS-CoV-2 or its relative SARS-CoV, we identify several age-associated factors that may contribute to the heightened severity of COVID-19 in older populations. We observed that age-associated gene expression and cell populations are significantly linked to the heightened severity of COVID-19 in older populations. The aging lung is characterized by increased vascular smooth muscle contraction, reduced mitochondrial activity, and decreased lipid metabolism. Lung epithelial cells, macrophages, and Th1 cells decrease in abundance with age, whereas fibroblasts, pericytes and CD4+ Tcm cells increase in abundance with age. Several age-associated genes have functional effects on SARS-CoV replication, and directly interact with the SARS-CoV-2 proteome. Interestingly, age-associated genes are heavily enriched among those induced or suppressed by SARS-CoV-2 infection. These analyses illuminate potential avenues for further studies on the relationship between the aging lung and COVID-19 pathogenesis, which may inform strategies to more effectively treat this disease.

these data indicate that differential expression of SARS-CoV-2 host entry factors alone is unlikely to explain the relationship between age and severity of COVID-19 illness.

To discern the host cell types involved in COVID-19 entry, we turned to a single cell RNA-seq (scRNA-seq) dataset of 57,020 human lung cells from the Tissue Stability Cell Atlas 19 . In agreement with prior reports, analysis of the single cell lung transcriptomes revealed that alveolar type 2 (AT2) cells were comparatively enriched in ACE2 and TMPRSS2-expressing cells 20, 21 ( Supplementary Figure 3a-b) . However, ACE2-expressing cells represented only 1.69% of all AT2 cells, while 47.52% of AT2 cells expressed TMPRSS2. Alveolar type 1 (AT1) cells also showed detectable expression of ACE2 and TMPRSS2, but at lower frequencies (0.39% and 26.70%). CTSL expression could be broadly detected in many different cell types including AT2 cells, but its expression was particularly pronounced in macrophages (Supplementary Figure 3c) .

Since the expression of host entry factors ACE2, TMPRSS2 and CTSL did not increase with age, we next sought to identify all age-associated genes expressed in the human lung (Methods). Using a likelihood-ratio test 22 , we pinpointed the genes for which age significantly impacts their expression. With a stringent cutoff of adjusted p < 0.0001, we identified two clusters of genes in which their expression progressively changes with age (Figure 1d) . Cluster 1 is composed of 643 genes that increase in expression with age, while Cluster 2 contains 642 genes that decrease in expression with age. Gene ontology and pathway analysis of Cluster 1 genes (increasing with age) revealed significant enrichment for cell adhesion, vascular smooth muscle contraction, oxytocin signaling, and platelet activation, in addition to several other pathways (Figure 1e) . These findings are consistent with known physiologic changes of aging, including decreased pulmonary compliance 23 , and heightened risk for thrombotic diseases 24 . Of note, deregulation of the reninangiotensin system has been implicated in the pathogenesis of acute lung injury induced by SARS- China and Italy have found that patients with hypertension were more likely to develop ARDS 17 , require ICU admission 31 , and die from the disease 32 , though we note that correlative epidemiologic studies do not necessarily demonstrate causality.

Cluster 2 genes (decreasing with age) were significantly enriched for mitochondrion, mitochondrial translation, metabolic pathways, and mitosis, among other pathways (Figure 1f) , which is consistent with prior observations of progressive mitochondrial dysfunction with aging [33] [34] [35] . Of note, Cluster 2 was also enriched for genes involved in lipid metabolism, fatty acid metabolism, peroxisome, and lysosomal membranes. Age-associated alterations in lipid metabolism could impact SARS-CoV-2 infection, as SARS-CoV can enter cells through cholesterol-rich lipid rafts [36] [37] [38] [39] . Similarly, age-associated alterations in lysosomes could influence late endocytic viral entry, as the protease cathepsin L cleaves SARS-CoV spike proteins from within lysosomes 40, 41 .

Having compiled a high-confidence set of age-associated genes, we sought to identify the lung cell types that normally express these genes, using the human lung single cell transcriptomics dataset from the Tissue Stability Cell Atlas 19 . By examining the scaled percentage of expressing cells within each cell subset, we identified age-associated genes predominantly enriched in different cell types. Cell types with highly enriched expression for certain Cluster 1 genes (increasing with age) included fibroblasts, muscle cells, and lymph vessels (Figure 2a) . In contrast, cell types with highly enriched expression for certain Cluster 2 genes (decreasing with age) included macrophages, dividing dendritic cells (DCs)/monocytes, and AT2 cells (Figure 2b) . Similar results were found using an independent human lung scRNA-seq dataset from the Human Lung Cell Atlas (Supplementary Figure 4a-b) 42 . Examining the muscle-enriched genes that increased in expression with age, gene ontology analysis revealed enrichment for vascular smooth muscle contraction, cGMP-PKG signaling, Z-disc, and actin cytoskeleton, among other pathways ( Figure   2c ). As for the AT2-enriched genes that decreased in expression with age, gene ontology analysis revealed enrichment for metabolic pathways, biosynthesis of antibiotics, lipid metabolism, extracellular exosome, and mitochondrial matrix (Figure 2d) . A subset of these enriched gene ontologies had also been identified by the bulk RNA-seq analysis (Figure 1e-f) . Thus, integrative analysis of bulk and single-cell transcriptomes revealed that many of the age-associated transcriptional changes in human lung can be mapped to specific cell subpopulations, suggesting that the overall abundance of these cell types, their transcriptional status, or both, may be altered with aging.

As the pathophysiology of viral-induced ARDS involves an intricate interplay of diverse cell types, most notably the immune system 43, 44 , aging-associated shifts in the lung cellular milieu 23 could contribute an important dimension to the relationship between age and risk of ARDS in patients with COVID-19 31 . To investigate the cellular landscape of the aging lung, we applied a gene signature-based approach 45 to infer the enrichment of different cell types from the bulk RNA-seq profiles. Since bulk RNA-seq measures the average expression of genes within a cell population, such datasets will reflect the relative proportions of the cell types that comprised the input population, though with the caveat that cell types can have overlapping expression profiles and such profiles may be altered in response to stimuli. Using this approach, we identified ageassociated alterations in the enrichment scores of several cell types (Figure 3a) . Whereas epithelial cells decreased with age, fibroblasts increased with age (Figure 3b ). This finding is consistent with the progressive loss of lung parenchyma due to reduced regenerative capacity of the aging lung 46 , as well as the increased risk for diseases such as chronic obstructive pulmonary disease and pulmonary fibrosis 47 . In addition, these results are concordant with the findings from analysis of human lung single-cell transcriptomes (Figure 2a-b) .

Among the innate immune cell populations, the enrichment scores of total macrophages were inversely associated with age (Figure 3c) . Macrophages are major drivers of innate immune responses in the lung, acting as first-responders against diverse respiratory infections 48 . Thus, the age-associated decrease in macrophage abundance may be a possible factor related to the greater severity of lung pathology in patients with COVID-19. Although macrophage accumulation is often associated with the pathologic inflammation of viral ARDS 49, 50 , pulmonary macrophages can act to limit the duration and severity of infection by efficiently phagocytosing dead infected cells and released virions [51] [52] [53] . Notably, macrophages infected with SARS-CoV have been found to abort the replication cycle of the virus 54,55 , further supporting their role in antiviral responses.

However, macrophages may suppress antiviral adaptive immune responses 48, 56 , inhibiting viral clearance in mouse models of SARS-CoV infection 57 . In aggregate, these prior reports suggest that the precise role of lung macrophages in SARS-CoV-2 pathophysiology is likely contextdependent. It is also plausible that the increased numbers of macrophages are not the primary distinction between young and old patients, but rather the functional status of the macrophages. In line with this, we observed that the age-associated changes in macrophages were specifically attributed to the pro-inflammatory M1 macrophage subset but not the immunoregulatory M2 subset 58 (Figure 3c) , though this binary classification scheme represents an oversimplification of macrophage function. Nevertheless, elucidating the consequences of age-associated changes in lung macrophages may reveal insights into the differential outcomes of older patients with COVID-19. Further studies are needed to investigate whether macrophages or other innate immune cells respond to SARS-CoV-2 infection, and how their numbers or function may change with aging.

Among the adaptive immune cell populations, we observed that Th1 cells and CD4 + Tcm cells trended in opposite directions with aging (Figure 3d) . While the lungs of younger donors were enriched for Th1 cells, they were comparatively depleted for CD4 + Tcm cells; the inverse was true in the lungs of older donors. Of note, mouse models of SARS-CoV infection have indicated important roles for CD4 + T cell responses in viral clearance 59, 60 . Additionally, Th1 cells are responsive to SARS-CoV vaccines 61 and promote macrophage activation against viruses 62 . It is therefore possible that age-associated shifts in CD4 + T cell subtypes within the lung may influence the subsequent host immune response in response to coronavirus infection. However, future studies will be needed to determine the role of Th1 cells and other adaptive immune cells in the response to SARS-CoV-2, and how these dynamics may change with aging.

We next explored the roles of lung age-associated genes in host responses to viral infection. Since functional screening data with SARS-CoV-2 has not yet been described (as of March 30, 2020), we instead searched for data on SARS-CoV. While these two viruses belong to the same genus (Betacoronaviridae) and are conserved to some extent 8 , they are nevertheless two distinct viruses with different epidemiological features, indicating unique virology and host biology. Therefore, data from experiments performed with SARS-CoV must be interpreted with caution.

We reassessed the results from a prior in vitro siRNA screen of host factors involved in SARS-CoV infection 63 . In this kinase-focused screen, 130 factors were determined to have a significant effect on SARS-CoV replication. Notably, 11 of the 130 factors exhibited age-associated gene expression patterns (Figure 4a) , with 4 genes in Cluster 1 (increasing with age) and 7 genes in Cluster 2 (decreasing with age). The 4 genes in Cluster 1 were all associated with increased SARS infectivity upon siRNA knockdown; these genes included CLK1, AKAP6, ALPK2, and ITK.

Paradoxically, while knockdown of CLK1 was associated with increased SARS infectivity, cell viability was also found to be increased (Figure 4b) . Of the 7 genes in Cluster 2, 6 were associated with increased SARS infectivity and reduced cell viability upon siRNA knockdown (AURKB, CDKL2, PDIK1L, CDKN3, MST1R, and ADK). Age-related downregulation of these 6 factors could be related to the increased severity of illness in older patients. However, we emphasize that until rigorous follow-up experiments are performed with SARS-CoV-2, the therapeutic potential of targeting these factors in patients with COVID-19 is unknown.

Using the human lung scRNA-seq data, we then determined which cell types predominantly express these host factors. Of the 4 genes in Cluster 1 that had a significant impact on SARS-CoV replication, CLK1 was universally expressed, while ALPK2 expression was rarely detected (Figure 4c, Supplementary Figure 5a) . ITK was preferentially expressed in lymphocytic populations, and AKAP6 was most frequently expressed in ciliated cells and muscle cells. Of the 7 overlapping genes in Cluster 2, AURKB and CDKN3 were predominantly expressed in proliferating immune cell populations, such as macrophages, DCs/monocytes, T cells, and NK cells (Figure 4d, Supplementary Figure 5b) . MST1R, PDIK1L, and PSKH1 were infrequently expressed, through their expression was detected in a portion of AT2 cells (5.54%, 5.30%, and 5.47%, respectively). Finally, ADK and CDKL2 exhibited preferential enrichment in AT2 cells (51.40% and 34.43%). In aggregate, these analyses showed that the age-associated genes with functional roles in SARS-CoV are expressed in specific cell types of the human lung.

We then investigated whether age-associated genes in the human lung interact with proteins encoded by SARS-CoV-2. A recent study interrogated the human host factors that interact with 27 different SARS-CoV-2 proteins 64 , revealing the SARS-CoV-2 : Human protein interactome in cell lines expressing recombinant SARS-CoV-2 proteins. By cross-referencing the interacting host factors with the set of age-associated genes, we identified 20 factors at the intersection ( Figure   5a ). 4 of these genes showed an increase in expression with age (i.e. Cluster 1 genes), while 16 decreased in expression with age (Cluster 2 genes). Mapping these factors to their interacting SARS-CoV-2 proteins, we noted that the age-associated host factors which interact with M, Nsp13, Nsp1, Nsp7, Nsp8, Orf3a, Orf8, Orf9c, and Orf10 proteins generally decrease in expression with aging (Figure 5b) . However, a notable exception was Nsp12, as the age-associated hostfactors that interact with Nsp12 both showed increased expression with aging (CRTC3 and MYCBP2) (Figure 5c ). Nsp12 encodes for the primary RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2, and is a prime target for developing therapies against COVID-19. The observation that CRTC3 and MYCBP2 increase in expression with aging is intriguing, as these genes may be related to the activity of Nsp12/RdRp in host cells. Of note, MYCBP2 is a known repressor of cAMP signaling 65, 66 , and cAMP signaling potently inhibits contraction of airway smooth muscle cells 67 . Thus, age-associated increases in MYCBP2 could promote smooth muscle contraction, which is concordant with our analyses on age-associated gene signatures in the lung (Figure 1e) . MYCBP2 might possibly contribute to COVID-19 pathology by not only interacting with SARS-CoV-2 RdRp, but also through its normal physiologic role in promoting smooth muscle contraction.

To assess the cell type-specific expression patterns of these various factors, we further analyzed the lung scRNA-seq data. Of the SARS-CoV-2 interacting genes that increase in expression with age, MYCBP2 was frequently expressed across several populations, particularly proliferating immune populations (DC/monocyte, T cells, macrophages), muscle cells, fibroblasts, and lymph vessels (Figure 5d) . MYCBP2 was also expressed in 21.86% of AT2 cells. CEP68 was preferentially expressed in lymph vessels, while AKAP8L and CRTC3 showed relatively uniform expression frequencies across cell types, including a fraction of AT2 cells (8.53% and 9.07% expressing cells, respectively). Of the SARS-CoV-2 interacting genes that decrease in expression age, NPC2 and NDUFB9 were broadly expressed in many cell types, including AT2 cells (99.96% Together, these analyses highlight specific age-associated factors that interact with the SARS-CoV-2 proteome, in the context of the lung cell types in which these factors are normally expressed.

Finally, we assessed whether SARS-CoV-2 infection directly alters the expression of lung ageassociated genes. A recent study profiled the in vitro transcriptional changes associated with SARS-CoV-2 infection in different human lung cell lines 68 . We specifically focused on the data from A549 lung cancer cells, A549 cells transduced with an ACE2 expression vector (A549-ACE2), and Calu-3 lung cancer cells. Several age-associated genes were found to be differentially expressed upon SARS-CoV-2 infection (Figure 6a-c) . Of note, the overlap between lung ageassociated genes and SARS-CoV-2 regulated genes was statistically significant across all 3 cell lines (Figure 6d-f) , suggesting a degree of similarity between the transcriptional changes associated with aging and with SARS-CoV-2 infection. Among the age-associated genes that were induced by SARS-CoV-2 infection, the majority of these genes increase in expression with age (Cluster 1) (Figure 6g-i) . Conversely, among the age-associated genes that were repressed by SARS-CoV-2 infection, most of these genes decrease in expression with age (Cluster 2). Of note, the directionality of SARS-CoV-2 regulation (induced or repressed) and the directionality of ageassociation (increase or decrease with age) were significantly associated across all 3 cell lines (Figure 6g-i) .

To identify a consensus set of age-associated genes that are regulated by SARS-CoV-2 infection, we integrated the analyses from all 3 cell lines. 603 genes were consistently induced by SARS-CoV-2 infection (Figure 7a ). Of these, 20 genes are in Cluster 1 (increase with age) and 2 genes are in Cluster 2 (decrease with age). The 20 induced genes in Cluster 1 include several factors involved in RAS signaling (RAB8B, RASA2, and RASGRP1) as well as CLK1, which was shown to be involved in host responses to SARS-CoV infection (Figure 4a-b) . On the other hand, 641 genes were concordantly repressed by SARS-CoV-2 infection (Figure 7b 

Here we systematically analyzed the transcriptome of the aging human lung and its relationship to SARS-CoV-2. We found that the aging lung is characterized by a wide array of changes that could contribute to the worse outcomes of older patients with COVID-19. On the transcriptional level, we first identified 1,285 genes that exhibit age-associated expression patterns. We subsequently demonstrated that the aging lung is characterized by several gene signatures, including increased vascular smooth muscle contraction, reduced mitochondrial activity, and decreased lipid metabolism. By integrating these data with single cell transcriptomes of human lung tissue, we further pinpointed the specific cell types that normally express the age-associated genes. We showed that lung epithelial cells, macrophages, and Th1 cells decrease in abundance with age, whereas fibroblasts and pericytes increase in abundance with age. These systematic changes in tissue composition and cell interactions can potentially propagate positive feedback loops that predispose the airways to pathological contraction 69 . We find that some of the age-associated genes have been previously identified as host factors with a functional role in SARS-CoV replication 63 , and a fraction of the age-associated factors have been shown to directly interact with the SARS-CoV-2 proteome 64 . Furthermore, age-associated genes are significantly enriched among genes directly regulated by SARS-CoV-2 infection 68 , suggesting transcriptional parallels between the aging lung and SARS-CoV-2 infection. Moreover, it is intriguing that the genes induced by SARS-CoV-2 infection tend to increase in expression with aging, and vice versa.

Whether any of these age-associated changes causally contribute to the heightened susceptibility of COVID-19 in older populations remains to be experimentally tested.

It is also important to note that the datasets analyzed here were not from patients with COVID-19.

Given the limited data that is currently publicly available, we emphasize that the analyses presented here at this stage should not be used to guide clinical practice. These analyses resulted in a number of previously unnoted observations and phenomena that illuminate new directions for subsequent research efforts on SARS-CoV-2, generating genetically-tractable hypotheses for why advanced age is one of the strongest risk factors for COVID-19 morbidity and mortality.

Ultimately, we hope such knowledge can help the field to sooner develop rational therapies for COVID-19 that are rooted in concrete biological mechanisms.

We thank Akiko Iwasaki, Craig Wilen, Hongyu Zhao, Wei Liu, Wenxuan Deng, Andre Levchenko, Katie Zhu, Ruth Montgomery, Bram Gerriten, Steven Kleinstein and a number of other colleagues for their critical comments and suggestions, which were incorporated into the analyses and manuscript. We thank Antonio Giraldez, Andre Levchenko, Chris Incarvito, Mike Crair, and Scott Strobel for their support on COVID-19 research. We thank our colleagues in the Chen lab, the Genetics Department, the Systems Biology Institute and various Yale entities. We also want to thank all of the healthcare workers who are risking their health on the frontlines to treat patients with this disease.

RC and SC conceived and designed the study. RC developed the analysis approach, performed all data analyses, and created the figures. RC and SC prepared the manuscript. SC supervised the work.

No competing interests related to this study.

The authors have no competing interests as defined by Nature Research, or other interests that might be perceived to influence the interpretation of the article. The authors are committed to freely share all COVID-19 related data, knowledge and resources to the community to facilitate the development of new treatment or prevention approaches against SARS-CoV-2 / COVID-19 as soon as possible.

As a note for full disclosure, SC is a co-founder, funding recipient and scientific advisor of EvolveImmune Therapeutics, which is not related to this study. c. DAVID gene ontology and pathway analysis of Cluster 1 age-associated genes that exhibit enriched expression in muscle cells. d. DAVID gene ontology and pathway analysis of Cluster 2 age-associated genes that exhibit enriched expression in alveolar type 2 (AT2) cells. a. Venn diagram of the intersection between age-associated genes in human lung and the SARS-CoV-2 : Human protein interactome (Gordon et al., 2020) . Of the 20 age-associated genes that were found to also interact with SARS-CoV-2, 4 of them increased in expression with age, while 16 decreased with age. b. Age-associated genes in human lung and their interaction with SARS-CoV-2 proteins, where each block contains a SARS-CoV-2 protein (underlined) and its interacting age-associated factors.

Blocks are colored by the dominant directionality of the age association (orange, decreasing with age; blue, increasing with age). Gene targets with already approved drugs, investigational new drugs, or preclinical molecules are additionally denoted with an asterisk. 

The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS 10, 11 . RNA-seq raw counts and normalized TPM matrices were downloaded from the GTEx Portal (https://gtexportal.org/home/index.html) on March 18, 2020, release v8. All accessed data used in this study are publicly available on the web portal and have been deidentified, except for patient age range and gender.

Case-fatality rates in China and Italy were from the Chinese CDC and Italian ISS, 

For visualization of RNA-seq expression data, the TPM values were log2 transformed and plotted in R (v3.6.1). All boxplots are Tukey boxplots, with interquartile range (IQR) boxes and 1.5 × IQR whiskers. Pairwise statistical comparisons in the plots were assessed by two-tailed Mann-Whitney test, while statistical comparisons across all age groups were performed by Kruskal-Wallis test.

To identify age-associated genes, the raw counts values were analyzed by DESeq2 (v1.24.0) 22 , using the likelihood ratio test (LRT). Age-associated genes were determined at a significance threshold of adjusted p < 0.0001. Genes passing the significance threshold were then scaled to z-scores and clustered using the degPatterns function from the R package DEGreport (v1.20.0). Gene clusters with progressive and consistent trends with age were retained for downstream analysis.

Gene ontology and pathway enrichment analysis was performed using DAVID (v6.8) 70 (https://david.ncifcrf.gov/), separating the age-associated genes into the two clusters (increasing or decreasing with age), as described above.

scRNA-seq data were analyzed in R (v3.6.1) using Seurat 71,72 and custom scripts. Of the 1285 age-associated genes identified from GTEx bulk transcriptomes, 1049 genes were matched in the Tissue Stability Cell Atlas dataset and 1021 genes were matched in the Human Lung Cell Atlas dataset. To determine the percentage of cells expressing a given gene, the expression matrices were converted to binary matrices by setting a threshold of expression > 0. Cell typespecific expression frequencies for each gene were then calculated using the provided cell type annotations. To identify genes preferentially expressed in a specific cell type, we further scaled the expression frequencies in R to obtain z-scores. Data were visualized in R using the NMF package 73 . Where applicable, gene ontology analysis was performed with DAVID (v6.8) 70 , using genes with z-score > 2 in the cell type of interest for analysis.

To infer the cellular composition of each lung sample, we analyzed the TPM expression matrices using the xCell algorithm 45 . The resultant cell type enrichment tables were analyzed in R. For data visualization, cell type enrichment scores were scaled to z-scores, and the median zscore for each age group was expressed as a heatmap, using the superheat package 74 . Ageassociation was assessed across all age groups by Kruskal-Wallis test.

To assess whether any age-associated genes affect host responses to SARS-CoV (a coronavirus related to SARS-CoV-2), we analyzed the data from a published siRNA screen of host factors influencing SARS-CoV 63 (Data Set S1 in the publication; accessed on March 20, 2020).

For data visualization, each point corresponding to a target gene was size-scaled and color-coded according to the age-association statistical analyses described above.

To assess whether any lung age-associated genes encode proteins that interact with the SARS-CoV-2 proteome, we compiled the data from a preprint manuscript detailing the human host factors that interact with 27 different proteins in the SARS-CoV-2 proteome 64 (accessed on March 23, 2020).

To assess whether the expression of lung age-associated genes is influenced by SARS-CoV-2 infection, we utilized the data from a preprint manuscript detailing the transcriptional response to SARS-CoV-2 infection 68 , from the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE147507) (accessed on April 13, 2020).

Differentially expressed genes were determined using the Wald test in DESeq2 (v1.24.0) 22 comparing SARS-CoV-2 infected cells to batch-matched mock controls, with a significance threshold of adjusted p < 0.05.

Of the 1285 age-associated genes, 988 genes were matched to the RNA-seq dataset.

Statistical significance of overlaps between the gene sets was assessed by hypergeometric test, assuming 21,797 total genes as annotated in the RNA-seq dataset and 988 age-associated genes.

Statistical significance of the association between the directionality of SARS-CoV-2 regulation and the directionality of age-association was assessed by two-tailed Fischer's exact test. Gene ontology and pathway enrichment analysis was performed using DAVID (v6.8) 70 (https://david.ncifcrf.gov/).

Comprehensive information on the statistical analyses used are included in various places, including the figures, figure legends and results, where the methods, significance, p-values and/or tails are described. All error bars have been defined in the figure legends or methods.

Codes used for data analysis or generation of the figures related to this study are available upon request to the corresponding author and will be deposited to GitHub upon publication for free public access.

All relevant processed data generated during this study are included in this article and its supplementary information files. Raw data are from various sources as described above. All data and resources related to this study are freely available upon request to the corresponding author. Tables   Table S1 : Demographics of donors for GTEx lung samples. (Gordon et al., 2020) with age-association statistics from this study. Table S22 : Differential expression analysis in A549 cells, infected with SARS-CoV-2 vs mock control, with age-association annotations. Table S23 : Differential expression analysis in A549-ACE2 cells, infected with SARS-CoV-2 vs mock control, with age-association annotations. Table S24 : Differential expression analysis in Calu-3 cells, infected with SARS-CoV-2 vs mock control, with age-association annotations. CDKN1B  GNA13  RAB8B  N4BP3  RASGRP1  CAMSAP2  MXI1  GEM  C1S  RBM33  RASA2  BRWD1  PHC3  GATAD2B  PNRC1  ARID4B  ATRX  CLK1  PNISR  PRTG  H1F0  NCKIPSD  CNN3  PTOV1  CFD  RHOC  POLR2F  GNAI2  TK1  TM4SF4  PSKH1  AP1M2  MSH2  ATP6V0E2  PGAM5  MVK  AACS  ACACA  ACSS2  NIPSNAP1  NSDHL  AIFM1  AGR2  GIPC1  MMAB  HADH  QDPR  AP1S1  NDUFA7  POLDIP2  AHCY  MRPS28  SAMM50  PRPF19  NUP93  GBA  CLN3  APEH  ATIC  ARPC1B  PPP1CA  APRT  ATP6V0D1  AKR1A1  PHB  C1QBP  PARP1  CBR1  ACAT1  PDHB  NDUFA9  MRPL27  PRDX3  MTCH2  ORMDL2  NDUFS3 

A T P 1 B 1 A L G 5 N E U 1 P R I M 1 C E N P F H O O K 1 G C C 1 P I G O N A R S 2 A A R 2 A T P 6 V 1 A N D U F A F 1 A G P S N P C 2 P P T 1 N D U F B 9

-log 10 (adj. p-value) GEM  C1S  GNA13  RAB8B  BRWD1  CNOT6L  CDKN1B  CAMSAP2  MXI1  RASA2  RBM33  GATAD2B  PHC3  CLK1  ARID4B  ATRX  PNISR  PNRC1  PRTG  H1F0  NCKIPSD  CNN3  GNAI2  RHOC  CFD  PTOV1  POLR2F  AP1M2  AGR2  TK1  TM4SF4  GBA  PGAM5  PSKH1  ACSS2  ACACA  AACS  MVK  MSH2  ATP6V0E2  NUP93  NIPSNAP1  NSDHL  AIFM1  ACAT1  CBR1  GIPC1  MMAB  HADH  MRPS28  AP1S1  POLDIP2  QDPR  AHCY  ATIC  APEH  CLN3  PRPF19  SAMM50  ARPC1B  PPP1CA  APRT  PARP1  PDHB  MTCH2  NDUFA9  MRPL27  ASNA1  NDUFS3  C1QBP  NDUFA7  PHB  ORMDL2  ATP6V0D1  AKR1A1  PRDX3 

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Alveolar Macrophages in the Resolution of Inflammation, Tissue Repair, and Tolerance to Infection

Antibody-dependent infection of human macrophages by severe acute respiratory syndrome coronavirus

Cytokine Responses in Severe Acute Respiratory Syndrome Coronavirus-Infected Macrophages In Vitro: Possible Relevance to Pathogenesis

Alveolar macrophage elimination in vivo is associated with an increase in pulmonary immune response in mice

Evasion by stealth: inefficient immune activation underlies poor T cell response and severe disease in SARS-CoV

Macrophage plasticity, polarization, and function in health and disease

T cell responses are required for protection from clinical disease and for virus clearance in severe acute respiratory syndrome coronavirus-infected mice

Cellular immune responses to severe acute respiratory syndrome coronavirus (SARS-CoV) infection in senescent BALB/c mice: CD4+ T cells are important in control of SARS-CoV infection

Induction of Th1 type response by DNA vaccinations with N, M, and E genes against SARS-CoV in mice

Expanding roles for CD4+ T cells in immunity to viruses

A Kinome-Wide Small Interfering RNA Screen Identifies Proviral and Antiviral Host Factors in Severe Acute Respiratory Syndrome Coronavirus Replication, Including Double-Stranded RNA-Activated Protein Kinase and Early Secretory Pathway Proteins

A SARS-CoV-2-Human Protein-Protein Interaction Map Reveals Drug Targets and Potential Drug-Repurposing

Protein associated with Myc (PAM) is a potent inhibitor of adenylyl cyclases

PAM mediates sustained inhibition of cAMP signaling by sphingosine-1-phosphate

cAMP Regulation of Airway Smooth Muscle Function

SARS-CoV-2 launches a unique transcriptional signature from in vitro, ex vivo, and in vivo systems

A microphysiological model of the bronchial airways reveals the interplay of mechanical and biochemical signals in bronchospasm

Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources

Integrating single-cell transcriptomic data across different conditions, technologies, and species

Comprehensive Integration of Single-Cell Data

A flexible R package for nonnegative matrix factorization

Superheat: An R Package for Creating Beautiful and Extendable Heatmaps for Visualizing Complex Data