We used available transcriptomic data to compare A549 lung cell line infected with SARS-CoV-2 vs. mock infection (henceforth A549CoV2vsControl), A549 infected with seasonal influenza A virus vs mock infection (A549IAVvsControl), and A549 in- fected with human respiratory syncytial virus vs mock infection (A549RSVvsControl). We also compared the transcriptional response in primary human bronchial epithelial (NHBE) between cells infected with SARS-CoV2 and mock infection (NHBE- CoV2vsControl). Finally, we compared the transcriptional response in COVID-19 lung tissues vs. healthy lung tissue (COVID19vsHealthy). These data were collected at Mount Sinai and are available in GEO as the GSE147507 data set 4.
Disrupted genes and biological processes. Fig. S3 shows a comparison of the affected biological processes in COVID19vsHealthy, NHBECoV2vsControl, A549CoV2vsControl, A549IAVvsControl, and A549RSVvsControl. The biological pro- cesses (BPs) are shown ordered by their significance in COVID19vsHealthy. In spite of a larger number of DEGs in the COVID-19-infected lung (815), there are only 7 significant biological processes involved, which may indicate a more coordinated, systemic response. In contrast, the changes in the NHBE cells are characterized by fewer DEGs (only 223) but span more uncoordinated biological processes. This is illustrated in Fig. S1 which shows the BPs ordered in the order of significance from NHBECoV2vsControl. Fig. S2 shows the Venn diagram comparing the differentially expressed genes (DEGs) in the two contrasts. A comparison of the DE genes across the five contrasts is shown in Fig. S4.
Putative mechanisms of disease. We performed an analysis aiming to identify putative mechanisms of disease. As part of this analysis we identified four genes that were predicted to be activated upstream regulators based on the observed changes in their downstream genes. These were IRF9, STAT2, IFNG, and IFNB1. These suggest two different potential mechanisms. The first appears to be triggered by STAT2 and IRF9, which have 16 common target genes that are also all significantly up-regulated (IFI6, IFIT1, IFIT2, IFIT3, IFITM1, IFITM3, OAS1, OAS3, OAS2, MX1, MX2, RSAD2, OASL, XAF1, IRF2, IRF7). This mechanism is also known to be involved in the response to influenza A (see influenza A pathway in Fig. S5).
The second putative mechanism involves interferon beta and gamma, which are targeting 5 common downstream genes: CXCL10, IDO1, DOX58, STAT1, which are up-regulated, and HMOX2 which is down-regulated. Interferon regulatory factors (IRFs) are subdivided into the interferonic IRFs (IRF2-3-7 and 9), the stress respon- sive IRFs (IRF1 and 5), the hematopoietic IRFs (IRF4 and 8) and morphogenic IRF6. IRF9 is a regulator of type I IFN signaling and is known to interact with STAT2 5 and STAT1 to form the heterotrimeric transcription factor complex (ISGF3) that binds to interferon-stimulated response elements (ISREs) to induce the expression of in- terferon stimulated genes (ISG). During viral infections, ISGs perform two key func- tions: 1) directly limit viral replication by shutting down protein synthesis and triggering apoptosis; 2) ISGs activate key components of the innate and adaptive immune sys- tem, including antigen presentation and production of cytokines. The genes triggered by the STAT2 and IRF9 pathway include genes responsible for limiting viral replication (IFI6, IFIT1, IFIT2, IFIT3, IFITM1, IFITM3, OAS1, OAS3, OAS2, MX1, MX2, RSAD2, OASL) and inducers of apoptosis (XAF1, IRF2, IRF7). CXCL10, IDO1, DOX58, and STAT1 are genes associated with lymphocyte recruitment and immune regulation.
Interestingly, STAT2 and IRF9 together are also identified as activated upstream regulators due to 15 downstream targets even in the NHBECoV2vsControl (see Fig. S6). However, in the NHBE cells, the interferon activators were replaced by an interleukin- based mechanism centered around IL1B, IL6, IL17A, adiponectin (ADIPOQ) and tu- mor necrosis factor (TNF).
We also looked at genes that are known to modulate or inhibit the inflammatory response such as IL1RN IL10, and IL13. In the COVID19vsHealthy, IL1RN was up with a log2 fold change of 6.2 fold (a 78-fold increase, FDR-corrected p = 10−6), IL10 was up 2.8 fold (FDR-corrected p = 0.55), while the measurement for IL13 was not available. In the NHBECoV2vsControl, IL1RN was up only 1.26 fold (FRD-corrected p = 0.035), while measurements for IL10 and IL13 were not available. However, in this contrast, 14 out 15 DE genes immediately downstream of IL10 and usually in- hibited by IL10 were up-regulated which strongly supports the hypothesis that IL10 is inhibited (FDR-corrected p = 5.17−9).
Impacted pathways. The significantly impacted pathways are shown in Fig. S7 ordered by their significance in COVID19vsHealthy. The p-values represent a com- bination of enrichment and perturbation p-values (see 6 for details) corrected with FDR. Fig. S8 shows the signaling pathways in all 5 experiments, ordered by their significance in NHBECoV2vsControl.
Fig. S9 shows the most impacted pathway, the Cytokine-cytokine interactions. Fig. S10 shows the Chemokine signaling pathway. On this pathway, the impact is due both to the large number of DE genes (26 out of 130), as well as to the clear signal propagation from the chemokines outside the cell (11 chemokines up-regulated), through the chemokine receptor and via the JAK and STAT mechanism. Fig. S12 shows another view of the mechanism involving the genes on this pathway and all their known interactions.
Proposed drugs. Once we identified the main regulatory pathways potentially associated with hyper-inflammation we evaluated in silico FDA-approved drugs that could show activity on multiple components of inflammation and consequently could be used for the management of severe COVID-19 cases. We considered the number of DE genes that would be reverted by each drug, as well as calculated a Bonferroni- corrected p-value indicating the suitability of each drug for repurposing in COVID-19 based on two different approaches (see methods). We looked for drugs that have both small p-values as well as revert a larger number of DE genes. The top three drugs drug identified by our analysis are shown in Fig. 1. Methylprednisolone is the drug that was identified as the most likely to work. This drug targets 27 genes that are found to be DE in COVID19vsHealthy. Out of these 27 genes, the drug would revert the changes in 25 of them. The drug also had an extremely significant p-value (p = 5.72−10) even after a Bonferroni correction which is the most stringent correction available. Methylprednisolone also reverted 22 out of 22 genes found to be DE in NHBECoV2vsControl, and 25 out of 26 genes found to be DE in A549CoV2vsControl. Fig. 2 shows the putative mechanism through methylprednisolone acts on the DE genes in COVID19vsHealthy, and how these genes influence the BPs found to be significantly impacted.
Clinical validation. In an independent study, 213 eligible subjects were en- rolled, 81 (38%) and 132 (62%) in pre-and post-methylprednisolone groups, respec- tively. The clinical characteristics and treatments received by the patients are shown in Table S1 and Table S2, respectively. As shown in Table S3 thirty day all-cause mortality occurred at a significantly lower rate in post-methylprednisolone group com- pared to pre-methylprednisolone group (29.6% vs. 16.6%, p=0.027). No statis- tical difference was detected in the proportion of patients prescribed empiric an- tibiotics or the time to empiric therapy. The survival curves of the pre- and post- methylprednisolone groups are shown in Fig. 3.