{"paper_id":"d902f2c8-8410-4258-b820-dabbaa08e3a4","body_text":"Transcriptome Heterogeneity in COVID-19-induced Acute Respiratory Distress Syndrome | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Transcriptome Heterogeneity in COVID-19-induced Acute Respiratory Distress Syndrome Mototsugu Nishii, Hiroshi Honzawa, Hana Oki, Reo Matsumura, Kazuya Sakai, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3908055/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract COVID-19 is a major etiology of acute respiratory distress syndrome (ARDS). The biological phenotypes and underlying mechanisms in COVID-19-induced ARDS are not fully understood. Bronchoalveolar lavage fluid (BALF) cells and clinical data were collected from patients with COVID-19-induced ARDS. Principal component analysis of genome-wide expression data obtained from bulk RNA sequencing of BALF cells subgrouped COVID-19-induced ARDS patients. Moreover, comparing transcriptome profiles between the subgroups showed two biological phenotypes, illustrated by up- and down-regulation of interferon (IFN) responses, despite no significant differences in clinical characteristics including onset and outcomes. In the low-IFN phenotype, in contrast to the high-IFN phenotype, the TLR-MyD88-IFN regulatory factor (IRF) 5 and cGAS-STING1 axes related to type Ⅰ IFN and the IRF8-interleukin (IL)-12-STAT4 and IRF1-IL-15-DNAX-activation protein 10 axes related to type Ⅱ IFN were inactivated at the transcriptional level, together with the PERK-C/EBP homologous protein axis and the IL-10-hemoglobin scavenger receptor CD163 axis. The pathogenesis of ARDS in the low-IFN phenotype was illustrated by damage to type II alveolar epithelial cells due to increased viral replication by reduced antiviral response, cytotoxicity, and apoptotic signaling and impaired free hemoglobin catabolism. Our data uncovered heterogeneous IFN responses, the underlying mechanisms, and related pathogenesis in COVID-19-induced ARDS. Biological sciences/Computational biology and bioinformatics Biological sciences/Immunology ARDS Transcriptome biological heterogeneity interferon COVID-19 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Acute respiratory distress syndrome (ARDS), manifesting as rapidly impaired oxygenation requiring ventilator, represents a common pulmonary disorder in the intensive care unit (ICU). ARDS can be caused by many etiologies, such as trauma, transfusion history, infection, sepsis, pneumonia, and even ventilator-induced lung injury. 1–3 Particularly, COVID-19 caused by SARS-CoV-2, that has spread rapidly worldwide and has been classified as a global pandemic, is a major etiology of ARDS. 4–6 In-hospital mortality remains high at approximately 40–50% in overall patients with ARDS, and further increases along with severity of ARDS, from mild to severe. 4–8 Against this background, development of fundamental therapies for ARDS has been sought. A number of randomized controlled studies (RCTs) have been conducted to evaluate the efficacy of corticosteroid with the ability to modulate hyperinflammation on ARDS derived from various etiologies including COVID-19, demonstrating treatment effects on mortality, ventilator use, and duration of use. 9–12 These RCTs, however, also highlighted the presence of non-responders. This discrepancy indicates the biological heterogeneity of COVID-19-induced ARDS. Gene expression signatures are a well-established method to better characterize disease pathogenesis. So far, attempts have been made to elucidate the mechanisms by which ARDS develops in COVID-19 patients by comparing their transcriptome profiles to ARDS due to other causes and healthy controls. 13–16 Apart from the “cytokine storm,” a reduced immune state has also been observed in COVID-19-induced ARDS. 16 This discrepancy is explained by the fact that SARS-CoV-2 triggers antiviral innate immunity, while also inhibiting its establishment. 17 The biological heterogeneity in COVID-19-induced ARDS, however, are not yet fully understood. This has hindered the development of precision medicine. In the present study, we therefore explored biological phenotypes, underlying mechanisms, and related pathogenesis by comparing transcriptome data from bronchoalveolar lavage fluid (BALF) cells among COVID-19-induced ARDS patients. Results Patient profiles BALF cells were collected from 16 patients with COVID-19-induced ARDS. Ultimately, RNA of sufficient quality and quantity to allow bulk RNA-seq analysis was extracted from BALF cells of 13 patients, and transcriptome data as well as clinical and laboratory data were examined. Clinical characteristics are shown in Table 1 . BALF cells were collected within 10 days of intubation. Eight patients (62%) were initiated immunosuppressants prior to admission and developed severe respiratory failure requiring ventilator support. After hospitalization to our institutions, most patients (85%) were treated with remdesivir and dexamethasone, and all patients were treated with anticoagulant. Table 1 Patient characteristics Overall (n = 13) Age (year) 57 [ 52 – 65 ] Gender (male) 11 (85) BMI (kg/m 2 ) 30.6 [21.5–36.5] Duration from onset until intubation (day) 6.0 [5.0-10.5] Duration from intubation until BAL (day) 0.0 [0.0–5.0] Immunosuppressants use before admission (n,%) 8 (61.5) Past medical history (n,%) COPD 4 (30.8) HT 8 (61.5) DM 5 (38.5) Dialysis 1 (7.7) Therapies post-admission (n,%) 13 (100) Remdesivir 11 (84.6) Dexamethasone 11 (84.6) Anticoagulant 13 (100) All categorical variables were presented as n (%). Continuous variables are shown as median values and [interquartile ranges]. BMI, body mass index; BAL, Bronchoalveolar lavage; COPD, chronic obstructive pulmonary disease; HT, hypertension; DM, diabetes. Transcriptome heterogeneity in COVID-19-induced ARDS To elucidate lung transcriptome heterogeneity in COVID-19-induced ARDS, we examined transcriptome data from bulk RNA sequencing of BALF cells from healthy control patients (HC) and COVID-19-induced ARDS patients (Fig. 1 A). When the genome-wide expression were evaluated by principal component analysis, ARDS patients could be clearly distinguished from HC in principal component (PC) 1. More importantly, in the PC2, ARDS patients could be divided into PC2-high (Red: n = 6), PC2-middle (Green: n = 1), and PC2-low (Blue: n = 6) groups, as shown in Fig. 1 B. Differentially expressed genes between HC, PC2-low, PC2-middle, and PC2-high groups are presented in scaled heatmap, as shown in Fig. 1 C. These results indicate the presence of lung transcriptome heterogeneity in COVID-19-induced ARDS. Biological phenotypes of COVID-19-induced ARDS illustrated by the distinct IFN response. Biological phenotypes in COVID-19-induced ARDS were explored based on lung transcriptome heterogeneity. Transcriptome profiles in the PC2-high and PC2-low groups were evaluated by using the gene set enrichment analysis. Enrichment analysis with Hallmark gene sets is shown in Fig. 2 A and Table S1 . Interferon (IFN)-α (type Ⅰ IFN), IFN-γ (type Ⅱ IFN), and complement responses were significantly enriched in the PC2-high group compared to the PC2-low and HC groups. On the other hand, type Ⅱ IFN and complement responses were not enriched between the PC2-low and HC groups. Moreover, type Ⅰ IFN response was rather enriched in the HC group compared to the PC2-low group. Consistently, gene expression data analysis (GEA) showed that gene expression of type Ⅰ IFN-stimulated gene (ISG) 15 and type Ⅱ IFN was increased in the PC2-high group compared to the HC and PC2-low groups. Moreover, gene expression of type Ⅰ IFN-inducible C-C motif chemokine ligand 2 (CCL2) and type Ⅱ IFN-inducible C-X-C motif chemokine ligand 10 (CXCL10) 18 showed the same results (Fig. 2 B and Table S2 ). Additionally, type I IFN induces the production of intracellular complement factor B (CFB) in type II alveolar epithelial (AT) cells, which generates C3 activation. Activated C3 fragments engage cognate receptor C3aR1 on immune cells and induce their activation. 19 The GEA analysis showed that gene expression of CFB was increased in the PC2-high group compared to the HC and PC2-low groups. C3aR1 gene expression was also increased in the PC2-high group compared to the PC2-low group (Table S2 ). These results showed two biological phenotypes of COVID-19-induced ARDS: a high-IFN phenotype (PC2-high group) and a low-IFN phenotype (PC2-low group), indicated by up- and down-regulation of the IFN responses, respectively. On the other hand, enrichment analysis with Hallmark gene sets showed that tumor necrosis factor (TNF)-α signaling was enriched in the PC2-high group compared to the HC and PC2-low groups (Fig. 2 A and Table S1 ). In the GEA analysis, gene expression of interleukin (IL)-6 was increased in the PC2-high compared to the PC2-low group, but gene expression of other inflammatory cytokines such as tumor necrosis factor (TNF)-α, IL-1β, and NLR family pyrin domain containing 3 (NLRP3) was not (Fig. 2 C and Table S2 ). Cell type signature analysis showed that signature gene sets for macrophages, monocytes, dendritic cells, T cell, neutrophils, natural killer cells, lymphatic cells, and basophil mast cells were enriched in the PC2-high group compared to the PC2-low group (Fig. 2 D), suggesting a differential state of immune cell mobilization into the lungs. To evaluate inflammatory pathological damage in the lung, tight junction pathway, which are important for maintaining the structure of lung epithelial cells 20 , was assessed by the KEGG analysis. Tight junction pathway was significantly enriched in the PC2-high group compared to the HC and PC2-low groups (Fig. 2 E), but was not between the HC and PC2-low group (Table S1 ). The GEA analysis showed that gene expression of tight junction protein (TJP)1, TJP3, claudin (CLDN)3, and CLD8 was increased in the PC2-high compared to the HC and PC2-low groups (Fig. 2 F and Table S2 ). These results indicate that the physical lung damage by inflammation is more severe in the PC2-high group compared to the PC2-low group. Next, damage to AT cells was evaluated. In cell type signature analysis, gene set for type Ⅰ AT cells was enriched in the PC2-high group compared to the PC2-low group, whereas gene set for type Ⅱ AT cells was not enriched between the two groups (Fig. 2 G). GSA showed that among signature genes for type Ⅱ AT cells, expression of SFTPA1 and SFTPA2 was increased only in the PC2-low group compared to the HC group, but expression of SFTPB, SFTPC, and NKX2.1 was increased in both PC2-low and PC2-high groups compared to the HC group (Fig. 2 H and Table S2 ). These results suggest that, unlike the PC2-high group, where both type I and type II cells were damaged, type Ⅱ AT cells were predominantly damaged in the PC2-low groups. Taken together, our data uncovered two biological phenotypes of COVID-19-induced ARDS based on distinct IFN response, which were characterized by differential damage to AT cells. Clinical profiles in biological phenotypes Table 2 shows comparisons of the clinical profiles between PC2-low and PC2-high groups. There were no significant differences of age, gender, body mass index, duration from symptom onset until BAL, and comorbidities between the two groups. Blood laboratory data, such as cell counts of white blood cell (WBC), neutrophil, lymphocyte, red blood cell (RBC), and platelet and plasma levels of aspartate aminotransferase, lactate dehydrogenase, C-reactive protein (CRP), and D-dimer, also did not differ between the two groups. Moreover, SARS-CoV-2 RNA copy number in the BALF followed the same manner. The use of immunosuppressants prior to admission and medications post-admission such as antivirals, dexamethasone, and anticoagulants were also not significantly different between the two groups. Regarding outcomes, there were no significant differences in ECMO use and length of ICU stay, although mortality was significantly higher in the PC2-high group than in the PC2-low group. Collectively, the biological phenotypes in COVID-19-induced ARDS could not be understood by the clinical profile. Table 2 Comparisons of patient characteristics PC2-low (n = 6) PC2-high (n = 6) p Age (Year) 58[ 54 – 63 ] 57[ 50 – 73 ] 0.8721 Gender (Male) 5(83%) 5(83%) BMI (kg/m 2 ) 32.0[21.2–36.3] 28.6[20.7–41.8] 0.9361 Comorbidities HT n, (%) 3(50%) 4(67%) 0.5571 DM n, (%) 0(%) 2(33%) 0.0748 COPD n, (%) 2(33%) 2(33%) Clinical course Duration from onset to admission (day) 5.5[2.0–16.0] 5.0[1.5–10.0] 0.569 Duration from admission to intubation (day) 1.5[0-4.5] 0.5[0-1.3] 0.4933 Duration from intubation to BAL (day) 0.5[0-1.8] 3.5[0–10] 0.1698 Duration from onset to BAL (day) 9.0[5.8–19.0] 11.0[3.5–16.3] 0.8102 Laboratory data WBC (Χ10 3 /µL) 15.5[11.4–17.1] 12.8[9.8–13.5] 0.0927 Neut (X10 3 /µL) 13.0[8.9–15.2] 10.4[8.1–11.7] 0.1735 Lymph (X10 3 /µL) 0.9[0.36–1.8] 1.3[0.49–1.5] 0.4712 RBC (X10 6 /µL) 4.1[3.5–4.4] 4.0[3.8–4.5] 0.9660 PLT (X10 4 /µL) 29.8[12.5–42.7] 18.6[14.8–23.4] 0.2615 APTT (sec) 36.9[32.7–77.2] 50.5[36.1–69.6] 0.5745 PT-INR 1.2[0.9–1.2] 1.0[1.0-1.2] 0.6874 D-dimer (µg/mL) 3.6[1.7–16.8] 1.6[1.0-28.8] 0.4712 Alb (g/dL) 2.4[2.1–2.5] 2.4[2.0-2.8] 0.9360 AST (IU/L) 29.0[19.5–76.0] 42.5[31.8–54.5] 0.4712 ALT (IU/L) 26.5[16.5–45.5] 33.5[21.3–44.5] 0.6889 LDH (IU/L) 470.5[322.3-567.3] 487.5[412.3-641.5] 0.5752 eGFR (mL/min/1.73) 82.7[56.7-104.8] 53.7[29.0-90.5] 0.3785 CRP (mg/dL) 5.3[2.4–24.4] 6.5[3.7–12.8] 0.9362 RNA copy number (Log10 copy/µL) 3.4[-1.0-5.4] 4.0[1.5–6.3] 0.5529 Medications n, (%) Remdesivir 4(67%) 6(100%) 0.0748 Dexamethasone 4(67%) 6(100%) 0.0748 Anticoagulant 6(100%) 6(100%) Immunosuppressants before admission 3(50%) 4(67%) 0.5571 Outcomes ECMO 3(50%) 5(83%) 0.2129 Duration of ICU stay (day) 13.5[11.8–15.5] 18.5[14.0–44.0] 0.1081 Mortality 0(0%) 3(50%) 0.0229 All categorical variables were presented as n (%). Continuous variables are shown as median values and [interquartile ranges]. BMI, body mass index; HT, hypertension; DM, diabetes; COPD, chronic obstructive pulmonary disease; BAL, Bronchoalveolar lavage; WBC, white blood cell; Neut, neutrophil; Lymph, lymphocyte; RBC, red blood cell; PLT, platelet; APTT, activated partial thromboplastin time; PT-INR, prothrombin time-international normalized ratio; Alb, albumin; AST, aspartate aminotransferase; ALT, alanine aminotransferase; LDH, lactate dehydrogenase; eGFR, estimated Glomerular Filtration Rate; CRP, C-reactive protein; ECMO, extracorporeal membrane oxygenation; ICU, intensive care unit. Molecular mechanisms underlying distinct type Ⅰ IFN response. Molecular mechanisms underlying distinct type Ⅰ IFN response in COVID-19-induced ARDS were explored. Antiviral type I IFN response is initiated by recognition of SARS-CoV-2 via the retinoic acid-inducible gene I (RIG-I)-like receptor consisting of two central intracellular sentinels RIG-I and melanoma differentiation-associated protein 5 (MDA5) and the toll-like receptors (TLRs) including TLR3, TLR7, TLR8, and TLR9. 21 Consistently, the KEGG pathway analysis showed that RIG-I-like receptor and TLR signaling pathways were significantly enriched in the PC2-high group compared to the PC2-low group, but not between the PC2-low and HC groups (Fig. 3 A and Table S1 ). In the RIG-I-like receptor signaling pathway, RIG-I and MDA5 engage the mitochondrial antiviral signaling protein (MAVS), leading to the phosphorylation of TANK-binding kinase 1 (TBK1) and IκB kinase-ε (IKKε), which in turn activates IFN regulatory factor 3 (IRF3), ultimately initiating the production of type I IFN. 21–22 In the GEA analysis, expression of MAVS, TBK1, and IRF3 genes did not show any significant differences between the PC2-low and PC2-high groups, while in contrast to the PC2-high group, up-regulation of IKKε gene expression was prevented in the PC2-low group. RIG-I and MDA5 gene expression was decreased in the PC2-low group compared to the HC and PC2-high groups (Fig. 3 B and Table S2 ). Alternatively, SARS-CoV-2 has also been reported to be recognized by cyclic GMP-AMP synthase (cGAS), a cytosolic DNA sensor, which activates stimulator of interferon genes (STING1) and promotes type I IFN production via TBK1 recruitment and activation. 23 Interestingly, cGAS gene expression was increased in the PC2-high group, but not in the PC2-low group, compared to the HC group. Moreover, STING1 gene expression was decreased in the PC2-low group compared to the HC and PC2-high groups (Fig. 3 B and Table S2 ). Therefore, inactivation of RIG-I, MDA5, and IKKε transcription in the RIG-I-like receptor signaling pathway and inactivation of cGAS and STING1 transcription in the cGAS-STING signaling pathway illustrate the repression of type I IFN response unique to the PC2-low group. In the TLR signaling pathway, TLR3 uniquely recruits Toll/interleukin-1 receptor domain-containing adapter protein (TRIF), which triggers activation of IRF3 through the phosphorylation of TBK1 and elicits type I IFN production. 24 The GEA analysis showed that gene expression of TLR3 and TRIF was increased in the PC2-high group compared to the HC and PC2-high groups, but did not differ between the HC and PC2-low groups (Fig. 3 C and Table S2 ). On the other hand, recognition of an appropriate viral ligand by all TLRs, except TLR3 initiates recruitment of myeloid differentiation primary-response protein 88 (MyD88) that interacts with interleukin 1 receptor associated kinase 1 (IRAK1) and IRAK4, which in turn initiates TNF receptor-associated factor 6 (TRAF6) ubiquitination and subsequent phosphorylation of IκB kinase (IKK), leading to the activation of NF-κB and IRFs such as IRF5 and IRF7, ultimately eliciting the transcription of multiple pro-inflammatory cytokines and type I IFN. 24–29 The GEA analysis showed that there were no significant differences in the gene expression of IRAK1, IRAK4, TRAF6, and IKK between the PC2-low and PC2-high groups, but IRF7 gene expression was increased in the PC2-high group compared to the HC and PC2-low groups. On the other hand, gene expression of TLR7, TLR8, MyD88, and IRF5 was decreased in the PC2-low group than in the HC group as well as in the PC2-high group (Fig. 3 C and Table S2 ). Thus, the inactivation of TLRs7/8, MyD88, and IRF5 transcription as well as TLR3 and TRIF transcription in the TLR signaling pathway is implicated in the repression of the type Ⅰ IFN response in the PC2-low group. Subsequently, type Ⅰ IFN binding to IFN α and β receptor subunit 1 (IFNAR1) and IFNAR2 leads to the formation of the signal transducer and activator of transcription 1 (STAT1)/STAT2/IRF9 complex known as “ISGF3,” which translocates to the nucleus and binds to IFN-stimulated response elements, activating transcription of type I IFN-inducible ISGs with strong antiviral effects, such as 2'-5'-Oligoadenylate Synthetase (OAS), interferon induced protein with tetratricopeptide repeats (IFIT), RNAsel, and tripartite motif (TRIM). 30–31 The GEA analysis showed that despite no difference in gene expression of IFNAR1 and IFNAR2 between the PC2-low and PC2-high groups, gene expression of IRF9, STAT1, and STAT2 was decreased in the PC2-low group compared to the HC and PC2-high groups (Fig. 3 D and Table S2 ), suggesting disruption of ISGF3 transcription in the PC2-low group. Moreover, type I IFN-inducible ISGs gene expression also showed the same results (Fig. 3 D and Table S2 ). Collectively, these data suggest that systematic and multifaced inactivation of type I IFN-related signaling pathways underlies the low-IFN phenotype in COVID-19-induced ARDS. Molecular mechanisms underlying distinct type Ⅱ IFN response. We also explored molecular mechanisms underlying distinct type Ⅱ IFN response in COVID-19-induced ARDS. Binding of IFN-γ to its receptor 1 (IFN-γR1) activates STAT1 in antigen presenting cells, which transactivates IRF8 expression, leading to IL-12 production. 32 Subsequently, engagement of IL-12 to IL-12 receptor on Th1 T cells further stimulates and amplifies IFN-γ production via STAT4. 32 The GEA analysis showed that gene expression of IFN-γR1 and IRF8 was decreased in the PC2-low group compared to the HC and PC2-high groups. Moreover, in contrast to the PC2-high group, upregulation of IL-12 receptor subunits β1 and β2 (IL-12Rβ1 and IL-12Rβ2) and STAT4 gene expression was suppressed in the PC2-low group (Fig. 4 A and Table S2 ). Thus, inactivation of the IRF8-mediated IL-12/IFN-γ loop between immune cells at the transcriptional level is implicated in the impaired type Ⅱ IFN response. Alternatively, IFN-γ transactivates IRF1 expression in infected epithelial cell, leading to IL-15 and IL-15 receptor α-subunit (IL-15Rα) expression. 33–35 Subsequently, IL-15 and IL-15Rα on epithelial cells forms a complex with the heterodimer of the IL‑2/IL‑15 receptor β-chain (IL‑2/IL‑15Rβ) and the common cytokine receptor γ-chain (γc) on group 1 innate lymphoid cells (ILC1s) and natural killer (NK) cells, conferring IFN-γ production by ILC1s and NK cells and cytotoxic pathways in NK cells. 33 The GEA analysis showed that gene expression of γc did not show any differences among the HC, PC2-low and PC2-high groups. Additionally, IL‑2/IL‑15Rβ gene expression was increased in the PC2-low and PC2-high groups compared to the HC group, but did not differ between the PC2-low and PC2-high groups. However, gene expression of IL-15 was decreased in the PC2-low group compared to the HC and PC2-high groups. Moreover, in contrast to the PC2-high group, upregulation of IL-15Rα and IRF1 gene expression was prevented in the PC2-low group (Fig. 4 B and Table S2 ). Thus, inactivation of IRF1-mediated IL-15/IL-15Rα/IFN-γ loop between infected epithelial cells and immune cells at the transcriptional level can generate impaired type Ⅱ IFN response in COVID-19-induced ARDS lung. SARS-CoV-2 cause the downregulation of expression of MHC class I molecules as an immune evasion mechanism to prevent the destruction of infected cells by cytotoxic T lymphocytes (CTLs). 36 Instead, the host upregulates the expression of IL‑15 and the non-classical MHC class I molecules such as MHC class I polypeptide-related sequence A (MICA) in infected cells. In turn, IL-15 upregulates the expression of activating NK receptors such as natural killer group 2, member D (NKG2D), which can recognize MICA, on CTLs, which in cooperation with the adaptor molecule DNAX-activation protein 10 (DAP10), confers type Ⅱ IFN-activated killer activity on the CTLs. 33 The GEA analysis showed that NKG2D gene expression was increased in the PC2-low and PC2-high groups compared to the HC group, while gene expression of MICA and DAP10 was decreased in the PC2-low group compared to the HC group as well as the PC2-high group (Fig. 4 C and Table S2 ). Inactivation of the IRF1-IL-15-DAP10-IFN-γ axis across infected epithelial and immune cells, which interferes with linkage between MICA and NKG2D, illustrates a novel mechanism by which SARS-CoV-2 suppresses the cytotoxic effect of immune cells on infected cells. Differential unfolded protein response in biological phenotypes. Under coronavirus infection, endoplasmic reticulum (ER) stress emerges as explosive accumulation of misfolded or unfolded proteins in the ER of infected host cells and induces apoptosis in infected cells 37 , while simultaneously inducing an adaptive cellular response in the ER known as the unfolded protein response (UPR). 38 UPR is consisted of the three branches, namely inositol-requiring 1α (IRE1α), double-stranded RNA-dependent protein kinase (PKR)-like ER kinase (PERK), and activating transcription factor 6 (ATF6) signaling pathways. 39 The SARS-CoV-2 can activate these branches of the UPR. 40 In the REACTOME analysis, the IRE1α signaling pathway was enriched in the PC2-low group compared to the PC2-high and HC groups (Fig. 5 A and Table S1 ). Consistently, the GEA analysis showed that the expression of target genes of IRE1α branch, including genes encoding ER molecular chaperones (DNAJB9 and HSPA5) that refold proteins and genes encoding the ER-associated degradation (ERAD) component (EDEM1, Herpud1, and HRD1) which pulls ER stress proteins out of the ER lumen and passes them to proteasomes for degradation 41–44 , was increased in the PC2-low group compared to the PC2-high and HC groups (Fig. 5 B and Table S3 ). Moreover, IRE1α signaling controls ER stress-induced cell apoptosis by degrading death receptor 5 (DR5) mRNA, a pro-apoptotic gene, also known as TNFRSF10B. 45 Consistently, the DR5 gene expression was decreased in the PC2-low group compared to the HC and PC2-high groups (Fig. 5 B and Table S3 ). Alternatively, activated ATF6 branch induces the transcription of XBP1, a critical adaptor of IRE1α signaling pathway. 46–47 XBP-1 gene expression was increased in the PC2-low group compared to the PC2-high and HC groups (Fig. 5 B and Table S3 ). These results suggest that in the low-IFN phenotype, IRE1α and ATF6 branches cooperatively enhance the UPR, which adaptively processes ER stress and suppresses ER stress-induced pro-apoptotic signal in SARS-CoV-2-infected cells. Conversely, the PERK signaling pathway elicits apoptosis in infected cells via activating transcription of the C/EBP homologous protein (CHOP) and ATF3 genes. 38, 48 The GEA analysis showed that the expression of CHOP and ATF3 genes was increased in the PC2-high group compared to the HC and PC2-low groups (Fig. 5 C and Table S3 ). CHOP triggers apoptosis by inducing the transcription of DR5, growth arrest and DNA damage-inducible gene 34 (GADD34), ER oxidoreductin-1α (ERO1α), and tribbles-related protein 3 (Trb3). 49–50 Consistently, the gene expression of GADD34, ERO1α, and Trb3 was increased in the PC2-high group compared to the PC2-low group (Fig. 5 C and Table S3 ), as was gene expression of DR5 (Fig. 5 B). Enrichment analysis with Hallmark gene sets showed the enrichment of apoptosis pathway in the PC2-high group compared to the PC2-low and HC groups (Fig. 5 D and Table S1 ). The expression of pro-apoptotic BAX and BAK genes also showed the same results (Fig. 5 E and Table S3 ). 51 These observations suggest that the state of the IFN response regulates the balance between IRE1α and PERK branches and influences apoptotic signaling. Differential free hemoglobin catabolism in biological phenotypes It has been demonstrated that RBCs are present in the alveolar space of patients with ARDS 3 and that free hemoglobin released from RBCs by hemolysis and heme derived from free hemoglobin cause ARDS by damaging the alveolar-capillary barrier responsible for alveolar permeability 52 and the type Ⅱ AT cells responsible for gas exchange and maintaining lung compliance. 3, 53–54 On the other hand, free hemoglobin or heme is processed by the binding of hemoglobin:haptoglobin or heme:hemopexin complexes to the CD163 or CD91/LRP1 scavenger receptors on myeloid cells, respectively. 55–56 In canonical pathway analysis with REACTOME gene sets, heme scavenging pathway and binding and uptake of ligands by scavenger receptors pathway were enriched in the PC2-low group compared to the PC2-high group (Fig. 6 A and Table S1 ). The GEA analysis showed that expression of hemoglobin genes such as HBA1, HBA2, and HBB was significantly increased in the PC2-low and PC2-high groups than in the HC group (Fig. 6 B and Table S3 ). However, up-regulation of CD163 gene expression was diminished in the PC2-low group in contrast to the PC2-high group, while the expression of CD91 gene did not show significant differences among the HC, PC2-low, and PC2-high groups (Fig. 6 B and Table S3 ). These results suggest that, in contrast to the high-IFN phenotype, free hemoglobin catabolism in the lung by CD163-expressing myeloid cells is impaired in the low-IFN phenotype. The hemoglobin scavenger receptor CD163 is uniquely expressed on monocytes and macrophages, and its expression is strongly up-regulated by IL-10. 57–58 Consistently, cell type signature analysis of BALF cells suggested the less recruitment of macrophage and monocyte into the alveolar space in the PC2-low group compared to the PC2-high group (Fig. 2 D). Moreover, GEA analysis showed that up-regulation of IL-10 gene expression was diminished in the PC2-low group in contrast to the PC2-high group (Fig. 6 B and Table S3 ), as was CD163 gene expression. The IL-10 gene expression is up-regulated not only by type Ⅰ IFN but also by various transcription factors such as Foxp3, IRF4, Blimp1, MAF, and E4BP4. 59 Among transcriptional factors, the expression of E4BP4 gene was decreased consistently in the PC2-low group compared to the PC2-high group, but expression of IRF4 and Blimp1 genes did not show any significant differences between the 2 groups (Fig. 6 C and Table S3 ). Taken together, apart from reduced recruitment of macrophage and monocyte, diminished up-regulation of IL-10 by transcriptional inactivation of E4BP4 is implicated in the suppression of CD163 expression in the low-IFN phenotype. Discussion Here, we report the results of transcriptome profiling of BALF cells from critically ill patients with COVID-19-induced ARDS. Transcriptome heterogeneity in the present study uncovered two biological phenotypes illustrated by distinct IFN responses in the lung, their underlying mechanisms, and related pathogenesis. On the other hand, the biological phenotypes were found to be indistinguishable in the clinical profile. These results highlight clinical importance of assessing transcriptome profiles in the lung to understand biological heterogeneity of COVID-19-induced ARDS and develop precision medicine. SARS-CoV-2 elicits type Ⅰ and II IFN responses and generates COVID-19 manifested as cytokine storm. 21, 60–61 On the other hand, these responses can also be suppressed by SARS-CoV-2, as its genome contains open reading frames that encode for accessory proteins important for the modulation of the host’s infected cell metabolism and innate immunity evasion. 17 Transcriptome data of BALF cells showed two distinct biological phenotypes in COVID-19-induced ARDS lungs, namely the high-IFN and low-IFN phenotypes. It has been demonstrated that SARS-CoV-2 targets the RIG-I/MDA5, TLR3-TRIF, and IFNAR-ISGF3 signaling pathways and interferes with type Ⅰ IFN response. 17, 21 However, type Ⅰ IFN response-related signaling pathways targeted by SARS-CoV-2 to evade host defense are not yet fully understood. Here, our data provide new evidence that SARS-CoV-2 may target the cGAS-STING1 and TLRs7/8-MyD88-IRF5 signaling pathways to suppress type I IFN response. Alternatively, our data suggest that SARS-CoV-2 may inhibit the type II IFN response by targeting the IL-12-STAT4-IFN-γ loop via IRF8 across immune cells, which links MHC class I-mediated antigen presentation and cytotoxic activation. SARS-CoV-2 has also been shown to block the MHC class I-mediated antigen presentation pathway in infected epithelial cells by targeting the STAT1-IRF1-NLRC5, an MHC class I transactivator axis, thereby evading IFN-γ-mediated cytotoxic effect against infected cells. 36 On the other hand, the host upregulates the expression of IL‑15 and non-classical MHC class I molecule, MICA in infected epithelial cells via IRF1 signaling, which in turn confers IFN-γ-mediated cytotoxic activity on the immune cells in cooperation with the adaptor DAP10. 33 Our data imply a novel immune evasion mechanism of SARS-CoV-2 that further suppresses the MICA-mediated cytotoxic activation by targeting the IRF1-IL-15-DAP10-IFN-γ axis. SARS-CoV-2 has a multifaceted impact on the host defense system to survive. Our observations offer new insight into mechanisms by which SARS-CoV-2 hinders type Ⅰ and II IFN responses and generates their heterogeneity in lungs. IFN response states may cause different damage to alveoli. Our data suggest that, unlike the inflammatory damage to both type Ⅰ and type Ⅱ AT cells in the high-IFN phenotype, damage to type Ⅱ AT cells was predominant in the low-IFN phenotype. SARS-CoV-2 primarily infects and severely damages type Ⅱ AT cells rather than type Ⅰ AT cells, causing ARDS. 62–63 IFN response generates inflamed manifestation of disease, while also helping clearance of SARS-CoV-2, through eliciting the host immune system. 64 The pathogenesis of ARDS in the low-IFN phenotype may be typified by severe damage to type Ⅱ AT cells due to increased viral replication, rather than inflammatory pathological changes. Viral replication is regulated not only by the immune system but also by apoptosis. SARS-CoV-2-infected host cells induce apoptosis via ER stress to restrict viral replication. 37–38 These responses are regulated by the UPR consisted of three signaling pathways involving IRE1α, PERK and ATF6. 37–39 During coronavirus infection, the PERK signaling pathway directly links ER stress to apoptosis in infected host cells, whereas the IRE1α signaling pathway blocks this link. 37, 48, 51 Interestingly, in the high-IFN phenotype, the PERK signaling pathway was predominantly activated, as evidenced by promoted transcriptional activation of CHOP, which is critical for inducing apoptosis. However, in the low-IFN phenotype, predominant activation of the IRE1α signaling pathway was observed, accompanied by the decay of DR5 transcript, a pro-apoptotic gene targeted by CHOP. These observations provide a novel idea that the state of the IFN response regulates ER stress-induced apoptosis and viral replication in infected type Ⅱ AT cells by affecting the balance between the PERK and IRE1α branches. Free hemoglobin released by hemolysis of RBCs in the alveolar space can cause damage to type II AT cells predominantly through binding to cell surface receptors, causing ARDS. 53 Whether and how the IFN response status affects free hemoglobin load, however, remain elusive. Our data highlight that, in contrast to the high-IFN phenotype, the low-IFN phenotype renders type II AT cells more susceptible to free hemoglobin load by diminishing the elevated expression of the free hemoglobin scavenger receptor CD163. This phenomenon can be explained by promoted recruitment of CD163-expressing macrophages and monocytes in the alveolar space due to type I IFN-inducible chemokines, as previously reported. 64–65 Alternatively, our data emphasized an association between IL-10 and CD163. In view of the regulatory mechanisms of IL-10 expression 66–67 , the IFN response and the transcriptional activation of E4BP4 may up-regulate CD163 expression in the COVID-19 lung. These observations underline the novel involvement of the IFN response in the lung in the catabolism of pathogenic free hemoglobin, which is responsible for COVID-19-induced ARDS. Clinical implications Mortality was lower in the low-IFN phenotype compared to the high-IFN phenotype. However, even the low-IFN phenotype required ECMO for severe respiratory failure and prolonged management in the ICU. Understanding and modulating the state of IFN response in the lung may contribute to evasion or early withdrawal from ECMO. The clinical benefits of therapeutic strategies targeting IFN response on COVID-19-induced ARDS should be explored. Study limitations This study has some limitations. BALF cells contained heterogeneous cells and bulk analysis of them could not intrinsically identify biological processes in single cells. Thus, we could not precisely determine in which cells the observed differences in gene expression were occurring. However, as discussed above, our comparative analysis suggests that BALF has practical utility for assessing heterogeneous biological phenomena in the lung. Our sample size limits the generalizability of these findings and requires validation in a larger cohort. We were unable to directly measure protein expression in the lower airway, which limits the scope of our biological analysis. Ultimately, the results in this study may need to be validated in experimental models. In the present study, transcriptomic data from BALF cells suggested various potential therapeutic targets based on the biological heterogeneity of COVID-19-induced ARDS. Unfortunately, BAL is highly invasive and can only be performed in limited patients. In the future, comprehensive proteome analysis of peripheral blood proteins is needed to identify biomarkers indicative of heterogeneous pathogenesis and therapeutic targets in the ARDS lungs. Conclusions Transcriptome profiling of BALF cells uncovered the two biological phenotypes of COVID-19-induced ARDS based on distinct IFN response. Underlying the low-IFN phenotype, unlike the common high-IFN phenotype, was a multifaced inactivation of the MyD88-IRF5 and cGAS-STING1 axes associated with type I IFN response and the IRF8-IL-12-STAT4 and IRF1-IL-15-DAP10 axes associated with type II IFN response as well as the PERK-CHOP axis and the IL-10-CD163 axis. The pathogenesis of ARDS in the low-IFN phenotype was illustrated by severe damage to type Ⅱ AT cells due to increased viral replication by reduced antiviral response, cytotoxicity, and apoptotic signaling and impaired free hemoglobin catabolism. The present study advances our understanding of COVID-19-induced ARDS and contributes to the development of precision medicine for emerging and re-emerging lethal viral infections. Methods Study design and setting This was prospective analysis of data set from a multi-center observational COVID-19 study across two advanced medical institutions in Japan. In the observational study, 16 patients with ARDS due to COVID-19, who were underwent BAL for diagnosis, admitted to the Yokohama City University Hospital and Yokohama City University Medical Center between April 2021 and February 2022 were enrolled and observed until hospital discharge after the enrollment. Ethical considerations This study was approved by the Institutional Ethics Board of the Yokohama City University Hospital (No. B210100010). All methods were conducted in accordance with relevant guidelines and regulations (Declaration of Helsinki). Patients were provided negative and positive information regarding this study, including the purpose and contribution of this study, the use of personal information, and complications associated with blood and BALF collection, and were asked to participate in this study. Ultimately, we obtained agreement from the patients to participate in the study and to access their medical and laboratory records by written informed consent or Opt-out method. The study had no risks or negative consequences for those who participated in the study. Medical record numbers were used for data collection and no personal identifiers were collected or used in the research report. Patients The inclusion criteria were as follows: (1) admission to the ICU for invasive mechanical ventilation for ARDS, (2) older than 18 years in age, (3) availability of BALF specimen within 10 days after intubation. The exclusion criteria were as follows: (1) no BALF specimen available within 10 days of intubation, (2) inadequate quality of BALF specimens for RNA-seq, (3) those with missing data, including clinical, laboratory, and outcome data, (4) those without consent for participation. Ultimately, in 13 patients with COVID-19-induced ARDS, clinical and transcriptomic data were evaluated. Clinical data collection Routinely available clinical data (demographic data, comorbidities, and clinical courses since symptom onset), laboratory data, treatment data before and after admission, and clinical outcomes after admission were collected and stored securely. Subject charts, chest X-rays, and chest computed tomography scan were reviewed by at least two study authors to confirm a diagnosis of ARDS using the Berlin Definition. 68 COVID-19 was diagnosed by two study physicians using either nucleic acid amplification test or antigen test. Sample processing and RNA sequencing After the collection of BALF, fresh samples were transported to a BSL-3 laboratory at ambient temperature. Cells were pelleted at 300 × g at 4°C for 5 min, and ammonium-chloride-potassium lysing buffer was added at room temperature for 2 min to lysate RBCs, followed by the addition of PFE buffer containing 2% FBS and 1mM EDTA in 1X phosphate-buffered saline at 4°C for stopping the reaction. Resuspended cells with PFE buffer were centrifuged at 300 × g at 4°C for 5 min, followed by passage through a 70-mm filter and cell count on a hemocytometer. RNA isolation was performed using RNeasy Micro Kit (QIAGEN) according to the manufacturer’s protocol. The quality of isolated RNA was evaluated by the TapeStation system using High Sensitivity RNA ScreenTape (Agilent). The stranded libraries were prepared and subjected to 150-bp paired-end sequencing on NovaSeq 6000 (Illumina), generating more than 20 million reads per sample. Expression data generation, principal component analysis, and differential expression analysis Reads were aligned to the GRCh38 reference sequence using STAR. 69 Raw read counts and transcripts per million data were obtained with StringTie. 70 In the edgeR package 71 , low-count genes were filtered by the filterByExpr function, and trimmed mean of M values (TMM)-normalized counts were calculated by the calcNormFactor function. To examine sample clustering, the plotMDS function was executed with the \"gene.selection\" parameter set to \"common\", which calculates principal components. Differential expression analysis was done by the glmQLFTest function. Comparison against external datasets The RNA-seq reads for three BALF samples from healthy controls without ARDS and COVID-19 (HC) were obtained from the SRA database (accession numbers, SRR10571724, SRR10571730 and SRR10571732) 72 and processed in the same manner as described above. Transcriptome data were compared between COVID19-induced ARDS patients and HC. Gene set enrichment analysis (GSEA) After clustering by principal component analysis of genome-wide expression profiles from 13 patients with COVID-19-induced ARDS and 3 HC, transcriptional profiles belonging to each cluster were analyzed by enriching gene expression data generated by TMM-normalized gene counts with ontology gene sets including three subsets of biological process (BP), molecular function (MF), and cellular component (CC), Hallmark gene sets, and pathway gene sets including KEGG and REACTOME from the Human Molecular Signatures Database (MSigDB). 73 Quantification of viral RNA load in BALF RNA extraction from BALF supernatants of COVID-19 patients was performed by QIAamp Viral RNA Mini Kit (QIAGEN, 52906) according to the manufacturer’s instructions. The viral gene was quantified as Ct-value by real-time qPCR with N2 primer pairs (TaKaRa, XD0008, forward primer: AAATTTTGGGGACCAGGAAC, reverse primer: TGGCAGCTGTGTAGGTCAAC, probe: FAM-ATGTCGCGCATTGGCATGGA-BHQ). Ultimately, viral RNA load was calculated by plotting Ct-values onto the standard curve constructed based on the standard product (NIHON GENE RESEARCH LABORATORIES, JP-NN2-PC), as previously described. 74 Statistical analysis All analyses for clinical data were performed using the JMP ver. 12.2 software and Python 3. 10. 4. All categorical variables were presented as frequency (%). Continuous variables were shown as median and interquartile range [IQR]. Categorical and continuous variables were compared by chi-square analysis and tests of variance, respectively. P < 0.05 was set as the threshold for screening significant results. In the GSEA analysis, statistical significance (nominal P value < 0.05) of enrichment for gene sets in the phenotype was calculated by using an empirical phenotype-based permutation test procedure that preserves the complex correlation structure of the gene expression data generated by TMM-normalized gene counts. 73 The degree to enrichment was indicated as a normalized enrichment score (NES). In gene expression data analysis, gene expression data were expressed as log2 fold change relative to the mean of the healthy control gene expression data. P-values indicating significant differences in gene expression data between groups were determined by ANOVA test followed by the Wilcoxon test. Abbreviations A new coronavirus: COVID-19 ARDS: acute respiratory distress syndrome ECMO: extracorporeal membranous oxygenation BALF: bronchoalveolar lavage fluid ICU: intensive care unit IFN: interferon IL: interleukin Declarations Ethics approval and consent to participate This study was approved by the Institutional Ethics Board of the Yokohama City University Hospital (No. B210100010). We obtained written informed consent for participation in the study and access to medical and laboratory records from patients. Availability of data and materials The datasets used during this study are available from the corresponding author on reasonable request. Competing interests The authors declare that there are no conflicts of interest regarding the publication of this paper. Funding This research was funded by the Japan Agency for Medical Research and Development (grant no. 20fk0108405h0001), the Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research (grant no. 21K09026), and Yokohama foundation for advancement of medical science. These funding organizations did not play a role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript and did not provide financial support in the form of authors' salaries. These funding sources only provided financial support in the form of research materials. Authors' contributions MN prepared and wrote the manuscript, collected the references, and performed the analysis of clinical and transcriptome data. TB and TT participated in the bioinformatic analysis, provided suggestions, and supervised the study. MN organized and reviewed the manuscript and coordinated all authors. KS and HO performed RNA extraction from BALF supernatants and analyzed Ct values of virus RNA. HH, RS, RM, and KS performed RNA extraction from BALF cells, and HH measured their quality. IT provided clinical support. All authors read and approved the final manuscript. Acknowledgments We thank our colleagues at the Department of Emergency Medicine, Yokohama City University, Yokohama City University Hospital, and Yokohama City University Medical Center for their kind assistance. Corresponding author : Mototsugu Nishii References Lium B. Adult respiratory distress syndrome (ARDS). Incidence, clinical findings, pathomorphology and pathogenesis. A review. Nord Vet Med. 1983;35:38–47. Janz DR, Ware LB. Biomarkers of ALI/ARDS: pathogenesis, discovery, and relevance to clinical trials. Semin Respir Crit Care Med. 2013;34:537–548. doi: 10.1055/s-0033-1351124. Janz DR, Ware LB. The role of red blood cells and cell-free hemoglobin in the pathogenesis of ARDS. 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Supplementary Files TableS12scientificreport.docx TableS2scientificreport4.docx TableS3scientificreport2.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-3908055\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":271524985,\"identity\":\"cf9efcce-d8bf-4335-8f60-6fe51f099bfb\",\"order_by\":0,\"name\":\"Mototsugu Nishii\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFUlEQVRIie3QP0vDQBjH8SccNMul80HFCL6BJ2RQwT9vJSHQLgoZHYLclEmc6+RbqIurTzm4Lqdz4Cbp2qEgSIcqth2kFK7FTfC+Uzj4kN8dgM/3J1NEUxT7AC1g6+fMBSDQ+bBfHqe/IMwcKj69zuUmcRbXBoij6A1GdT4uq6cYQoMwqyA8chA0L0QCxdXAaJX2tU0kv8TgVgM7kQ4iXjPCJWl6dYe3bPDccIRIAkNyDHuYIGWLYbgiX/ZCCo7B5xYCZJAIRYZNV3ei2uZLwrb9BUlnQ4kiuTe6SKM7W0jeLdWeFs67xFKp9/n8Jm6P6mTMP+yZDNXj26Q6LVwv9tPB+ozFtyhwF4k3Z5zvJD6fz/df+gaa32EHExgohQAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"Yokohama City University Graduate School of Medicine\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Mototsugu\",\"middleName\":\"\",\"lastName\":\"Nishii\",\"suffix\":\"\"},{\"id\":271524986,\"identity\":\"94bd4b70-c766-432d-8fe0-2f7e2ecfa8db\",\"order_by\":1,\"name\":\"Hiroshi Honzawa\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Yokohama City University Graduate School of Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Hiroshi\",\"middleName\":\"\",\"lastName\":\"Honzawa\",\"suffix\":\"\"},{\"id\":271524987,\"identity\":\"184c98b8-edf6-4fae-865d-76f487eb356b\",\"order_by\":2,\"name\":\"Hana Oki\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Yokohama City University Graduate School of Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Hana\",\"middleName\":\"\",\"lastName\":\"Oki\",\"suffix\":\"\"},{\"id\":271524988,\"identity\":\"bbc30b1f-fcdc-4621-81e0-db679a639fed\",\"order_by\":3,\"name\":\"Reo Matsumura\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Yokohama City University Graduate School of Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Reo\",\"middleName\":\"\",\"lastName\":\"Matsumura\",\"suffix\":\"\"},{\"id\":271524989,\"identity\":\"ad90e5f6-5f3a-4a65-89ae-9251044cd03b\",\"order_by\":4,\"name\":\"Kazuya 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Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Tatsuma\",\"middleName\":\"\",\"lastName\":\"Ban\",\"suffix\":\"\"},{\"id\":271524992,\"identity\":\"0cf8e05b-99a6-4a37-a8b6-3949a39406d2\",\"order_by\":7,\"name\":\"Tomohiko Tamura\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Yokohama City University Graduate School of Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Tomohiko\",\"middleName\":\"\",\"lastName\":\"Tamura\",\"suffix\":\"\"},{\"id\":271524993,\"identity\":\"0c178608-6981-44f5-82d1-cd2180e83055\",\"order_by\":8,\"name\":\"Ichiro Takeuchi\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Yokohama City University Medical Center\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Ichiro\",\"middleName\":\"\",\"lastName\":\"Takeuchi\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-01-29 05:34:53\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-3908055/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-3908055/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":50818538,\"identity\":\"64d8ffd5-476b-4c40-8d44-5f5fc20304ec\",\"added_by\":\"auto\",\"created_at\":\"2024-02-07 20:18:26\",\"extension\":\"jpg\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":2124782,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eTranscriptome heterogeneity.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eA.\\u003c/strong\\u003e \\u003cstrong\\u003eTranscriptome analysis\\u003c/strong\\u003e. Transcriptome data in broncho-alveolar lavage fluid (BALF) cells from healthy control patients (HC) (n=3) and COVID-19-induced ARDS patients (n=13) were analyzed by bulk RNA-seq. Transcriptome data from the HC were obtained from the open data base (doi: 10.1038/s41467-019-13751-9). The transcriptome profile of each group was explored by gene set enrichment analysis (GSEA). \\u003cstrong\\u003eB.\\u003c/strong\\u003e \\u003cstrong\\u003eGenome-wide analysis.\\u003c/strong\\u003e Transcriptome changes were examined using principal component analysis (PCA). COVID-19-induced ARDS patients could be clearly distinguished from HC (Glay: n = 3) in principal component (PC) 1. Moreover, COVID-19-induced ARDS patients could be divided into PC2-high (Red: n = 6), PC2-middle (Green: n = 1), and PC2-low (Blue: n = 6) groups. \\u003cstrong\\u003eC.\\u003c/strong\\u003e \\u003cstrong\\u003eScaled heatmap.\\u003c/strong\\u003e Differentially expressed genes indicated by Log2 count per million (CPM) are represented in scaled heatmap comparing among HC, PC2-low, PC2-middle, and PC2-high groups.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure12024scientificreport.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3908055/v1/4847282d6f1061c648988179.jpg\"},{\"id\":50818542,\"identity\":\"b16bca06-fa79-4029-8d7d-f844728bb4fb\",\"added_by\":\"auto\",\"created_at\":\"2024-02-07 20:18:27\",\"extension\":\"jpg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":8285785,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eHeterogeneous biological responses in COVID-19-induced ARDS lungs.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eA. Enrichment analysis with Hallmark gene sets\\u003c/strong\\u003e. The normalized enrichment score (NES) in the PC2-high relative to the PC2-low and the statistical significance of the enrichment between the two groups in each gene set were estimated by using the gene set enrichment analysis (GSEA) method. G1: Enrichment of the PC2-high relative to the PC2-low, G2: Enrichment of the PC2-high relative to the healthy control patients (HC), G3: Enrichment of the PC2-low relative to the HC. Significant enrichment is shown as the orange column. \\u003cstrong\\u003eB and C. Gene expression analysis in interferon (B) and pro-inflammatory cytokines (C)\\u003c/strong\\u003e. Individual gene expression data (closed cycles) are expressed as the log2 fold change (FC) relative to the mean of the healthy control gene expression data. Significant differences (*) in gene expression data between the groups were determined by ANOVA test followed by Wilcoxon test. \\u003cstrong\\u003eD and E.\\u003c/strong\\u003e \\u003cstrong\\u003eImmune\\u003c/strong\\u003e \\u003cstrong\\u003ecell type signature analysis (D) and tight junction pathway analysis with KEGG gene sets (E). \\u003c/strong\\u003eThe NES in the PC2-high relative to the PC2-low and the statistical significance of the enrichment between the two groups in each gene set are shown.\\u003cstrong\\u003e \\u003c/strong\\u003eFDR: false discovery rate. \\u003cstrong\\u003eF. Gene expression analysis in tight junction pathway. \\u003c/strong\\u003eIndividual gene expression data (closed cycles) are expressed as the log2 FC. Significant differences (*) in gene expression data between the groups are shown. \\u003cstrong\\u003eG. Alveolar epithelial cell type signature analysis. \\u003c/strong\\u003eThe NES in the PC2-high relative to the PC2-low and the statistical significance of the enrichment between the two groups are shown.\\u003cstrong\\u003e H. Gene expression analysis in signature genes of alveolar epithelial cell. \\u003c/strong\\u003eIndividual gene expression data (closed cycles) are expressed as the log2 FC. Significant differences (*) in gene expression data between the groups are shown.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure22024scientificreport.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3908055/v1/f70e0422068dca7c21888672.jpg\"},{\"id\":50818539,\"identity\":\"1a70b801-a202-48b4-8b60-557decf46b39\",\"added_by\":\"auto\",\"created_at\":\"2024-02-07 20:18:26\",\"extension\":\"jpg\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":11440529,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eDifferential type Ⅰ interferon-related signaling in COVID-19-induced ARDS lungs.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eA. Canonical pathway analysis with KEGG gene sets\\u003c/strong\\u003e. The normalized enrichment score (NES) in the PC2-high relative to the PC2-low and the statistical significance of the enrichment between the two groups in each gene set were estimated by using the gene set enrichment analysis method. G1: Enrichment of the PC2-high relative to the PC2-low, G2: Enrichment of the PC2-high relative to the healthy control patients (HC), G3: Enrichment of the PC2-low relative to the HC. Significant enrichment is shown as the orange column. \\u003cstrong\\u003eB-D. Gene expression analysis in RIG-I/MDA5 and cGAS-STING signaling pathways (B), TLR signaling pathway (C), and type Ⅰ interferon-inducible signaling pathway (D). \\u003c/strong\\u003eIndividual gene expression data (closed cycles) are expressed as the log2 fold change (FC) relative to the mean of the healthy control gene expression data. Significant differences (*) in gene expression data between the groups were determined by ANOVA test followed by Wilcoxon test.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure32024scientificreport.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3908055/v1/e66343ea5f6239f9458f8171.jpg\"},{\"id\":50818543,\"identity\":\"0815ce1e-2a27-4c2b-999e-934db9a031bc\",\"added_by\":\"auto\",\"created_at\":\"2024-02-07 20:18:27\",\"extension\":\"jpg\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":4143980,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eDifferential type Ⅱ interferon-related signaling in COVID-19-induced ARDS lungs.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eA-C. Gene expression analysis in IRF8-IL-12-STAT4-IFN-γ signaling loop (A), IRF1-IL-15-IFN-γ signaling loop (B), and intercellular signaling in relation to cytotoxic effect of IFN-γ (C). \\u003c/strong\\u003eIndividual gene expression data (closed cycles) are expressed as the log2 fold change (FC) relative to the mean of the healthy control gene expression data. Significant differences (*) in gene expression data between the groups were determined by ANOVA test followed by Wilcoxon test.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure42024scientificreport.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3908055/v1/ffb4befaf06479a420dc0b97.jpg\"},{\"id\":50818540,\"identity\":\"022cbf5f-9668-4335-8883-dcd0cd3000bb\",\"added_by\":\"auto\",\"created_at\":\"2024-02-07 20:18:27\",\"extension\":\"jpg\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":6487294,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eDifferential unfolded protein response in COVID-19-induced ARDS lungs.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eA. Inositol-requiring 1α (IRE1α) pathway analysis with REACTOME gene sets. \\u003c/strong\\u003eThe normalized enrichment score (NES) in the PC2-low relative to the PC2-high and the statistical significance of the enrichment between the two groups in each gene set were estimated by using the gene set enrichment analysis method. FDR: false discovery rate. \\u003cstrong\\u003eB and C.\\u003c/strong\\u003e \\u003cstrong\\u003eGene expression analysis in inositol-requiring 1α (IRE1α) (B) and double-stranded RNA-dependent protein kinase-like ER kinase (PERK) (C) signaling pathways. \\u003c/strong\\u003eIndividual gene expression data (closed cycles) are expressed as the log2 fold change (FC) relative to the mean of the healthy control gene expression data. Significant differences (*) in gene expression data between the groups were determined by ANOVA test followed by Wilcoxon test. \\u003cstrong\\u003eD. Apoptosis pathway analysis with Hallmark gene sets. \\u003c/strong\\u003eThe NES in the PC2-high relative to the PC2-low and the statistical significance of the enrichment between the two groups in each gene set are shown.\\u003cstrong\\u003e \\u003c/strong\\u003eFDR: false discovery rate. \\u003cstrong\\u003eE.\\u003c/strong\\u003e \\u003cstrong\\u003eGene expression analysis in apoptosis pathway. \\u003c/strong\\u003eIndividual gene expression data (closed cycles) are expressed as the log2 fold change (FC) relative to the mean of the healthy control gene expression data. Significant differences (*) in gene expression data between the groups are shown.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure52024scientificreport.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3908055/v1/f8aed32373c23cd283d4f282.jpg\"},{\"id\":50818547,\"identity\":\"002b88e7-36f5-42e5-aa75-5942aadf391a\",\"added_by\":\"auto\",\"created_at\":\"2024-02-07 20:18:27\",\"extension\":\"jpg\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":4498024,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eDifferential free hemoglobin catabolism in COVID-19-induced ARDS lungs.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eA. Scavenging pathway analysis with REACTOME gene sets. \\u003c/strong\\u003eThe normalized enrichment score (NES) in the PC2-low relative to the PC2-high and the statistical significance of the enrichment between the two groups in each gene set were estimated by using the gene set enrichment analysis method. FDR: false discovery rate.\\u003cstrong\\u003e B and C.\\u003c/strong\\u003e \\u003cstrong\\u003eGene expression analysis in free hemoglobin scavenging pathway (B) and regulatory pathway of IL-10 expression (C). \\u003c/strong\\u003eIndividual gene expression data (closed cycles) are expressed as the log2 fold change (FC) relative to the mean of the healthy control gene expression data. Significant differences (*) in gene expression data between the groups were determined by ANOVA test followed by Wilcoxon test.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure62024scientificreport.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3908055/v1/8219844af7910d010be0b05c.jpg\"},{\"id\":52490168,\"identity\":\"ae3683b9-6fbe-4ded-aef2-76be66fec47a\",\"added_by\":\"auto\",\"created_at\":\"2024-03-12 08:16:12\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1628905,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3908055/v1/bd352bf5-c26a-4270-8dad-7f667e10c278.pdf\"},{\"id\":50818545,\"identity\":\"83f7a597-b33a-447e-bc7f-10143cdfec6a\",\"added_by\":\"auto\",\"created_at\":\"2024-02-07 20:18:27\",\"extension\":\"docx\",\"order_by\":10,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":22277,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"TableS12scientificreport.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3908055/v1/1c02a659351cd72c6ed31066.docx\"},{\"id\":50818546,\"identity\":\"1374a71a-ea4b-450a-ac06-5f62196b55ff\",\"added_by\":\"auto\",\"created_at\":\"2024-02-07 20:18:27\",\"extension\":\"docx\",\"order_by\":11,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":34674,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"TableS2scientificreport4.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3908055/v1/9408ee294032d8f65c65f1c4.docx\"},{\"id\":50818544,\"identity\":\"06adc7be-20ab-43e5-b6f6-6808ae8ad529\",\"added_by\":\"auto\",\"created_at\":\"2024-02-07 20:18:27\",\"extension\":\"docx\",\"order_by\":12,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":24098,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"TableS3scientificreport2.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3908055/v1/bac0a642a57dd611783d206d.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Transcriptome Heterogeneity in COVID-19-induced Acute Respiratory Distress Syndrome\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eAcute respiratory distress syndrome (ARDS), manifesting as rapidly impaired oxygenation requiring ventilator, represents a common pulmonary disorder in the intensive care unit (ICU). ARDS can be caused by many etiologies, such as trauma, transfusion history, infection, sepsis, pneumonia, and even ventilator-induced lung injury.\\u003csup\\u003e1\\u0026ndash;3\\u003c/sup\\u003e Particularly, COVID-19 caused by SARS-CoV-2, that has spread rapidly worldwide and has been classified as a global pandemic, is a major etiology of ARDS.\\u003csup\\u003e4\\u0026ndash;6\\u003c/sup\\u003e In-hospital mortality remains high at approximately 40\\u0026ndash;50% in overall patients with ARDS, and further increases along with severity of ARDS, from mild to severe.\\u003csup\\u003e4\\u0026ndash;8\\u003c/sup\\u003e Against this background, development of fundamental therapies for ARDS has been sought. A number of randomized controlled studies (RCTs) have been conducted to evaluate the efficacy of corticosteroid with the ability to modulate hyperinflammation on ARDS derived from various etiologies including COVID-19, demonstrating treatment effects on mortality, ventilator use, and duration of use.\\u003csup\\u003e9\\u0026ndash;12\\u003c/sup\\u003e These RCTs, however, also highlighted the presence of non-responders. This discrepancy indicates the biological heterogeneity of COVID-19-induced ARDS.\\u003c/p\\u003e \\u003cp\\u003eGene expression signatures are a well-established method to better characterize disease pathogenesis. So far, attempts have been made to elucidate the mechanisms by which ARDS develops in COVID-19 patients by comparing their transcriptome profiles to ARDS due to other causes and healthy controls.\\u003csup\\u003e13\\u0026ndash;16\\u003c/sup\\u003e Apart from the \\u0026ldquo;cytokine storm,\\u0026rdquo; a reduced immune state has also been observed in COVID-19-induced ARDS.\\u003csup\\u003e16\\u003c/sup\\u003e This discrepancy is explained by the fact that SARS-CoV-2 triggers antiviral innate immunity, while also inhibiting its establishment.\\u003csup\\u003e17\\u003c/sup\\u003e The biological heterogeneity in COVID-19-induced ARDS, however, are not yet fully understood. This has hindered the development of precision medicine. In the present study, we therefore explored biological phenotypes, underlying mechanisms, and related pathogenesis by comparing transcriptome data from bronchoalveolar lavage fluid (BALF) cells among COVID-19-induced ARDS patients.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePatient profiles\\u003c/h2\\u003e \\u003cp\\u003eBALF cells were collected from 16 patients with COVID-19-induced ARDS. Ultimately, RNA of sufficient quality and quantity to allow bulk RNA-seq analysis was extracted from BALF cells of 13 patients, and transcriptome data as well as clinical and laboratory data were examined. Clinical characteristics are shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. BALF cells were collected within 10 days of intubation. Eight patients (62%) were initiated immunosuppressants prior to admission and developed severe respiratory failure requiring ventilator support. After hospitalization to our institutions, most patients (85%) were treated with remdesivir and dexamethasone, and all patients were treated with anticoagulant.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003ePatient characteristics\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"2\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOverall (n\\u0026thinsp;=\\u0026thinsp;13)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge (year)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e57 [\\u003cspan additionalcitationids=\\\"CR53 CR54 CR55 CR56 CR57 CR58 CR59 CR60 CR61 CR62 CR63 CR64\\\" citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR65\\\" class=\\\"CitationRef\\\"\\u003e65\\u003c/span\\u003e]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGender (male)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e11 (85)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBMI (kg/m\\u003csup\\u003e2\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e30.6 [21.5\\u0026ndash;36.5]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDuration from onset until intubation (day)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e6.0 [5.0-10.5]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDuration from intubation until BAL (day)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.0 [0.0\\u0026ndash;5.0]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eImmunosuppressants use before admission (n,%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e8 (61.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePast medical history (n,%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCOPD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4 (30.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e8 (61.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5 (38.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDialysis\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1 (7.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTherapies post-admission (n,%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e13 (100)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRemdesivir\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e11 (84.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDexamethasone\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e11 (84.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAnticoagulant\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e13 (100)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"2\\\"\\u003eAll categorical variables were presented as n (%). Continuous variables are shown as median values and [interquartile ranges]. BMI, body mass index; BAL, Bronchoalveolar lavage; COPD, chronic obstructive pulmonary disease; HT, hypertension; DM, diabetes.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eTranscriptome heterogeneity in COVID-19-induced ARDS\\u003c/h2\\u003e \\u003cp\\u003eTo elucidate lung transcriptome heterogeneity in COVID-19-induced ARDS, we examined transcriptome data from bulk RNA sequencing of BALF cells from healthy control patients (HC) and COVID-19-induced ARDS patients (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eA). When the genome-wide expression were evaluated by principal component analysis, ARDS patients could be clearly distinguished from HC in principal component (PC) 1. More importantly, in the PC2, ARDS patients could be divided into PC2-high (Red: n\\u0026thinsp;=\\u0026thinsp;6), PC2-middle (Green: n\\u0026thinsp;=\\u0026thinsp;1), and PC2-low (Blue: n\\u0026thinsp;=\\u0026thinsp;6) groups, as shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eB. Differentially expressed genes between HC, PC2-low, PC2-middle, and PC2-high groups are presented in scaled heatmap, as shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eC. These results indicate the presence of lung transcriptome heterogeneity in COVID-19-induced ARDS.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eBiological phenotypes of COVID-19-induced ARDS illustrated by the distinct IFN response.\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eBiological phenotypes in COVID-19-induced ARDS were explored based on lung transcriptome heterogeneity. Transcriptome profiles in the PC2-high and PC2-low groups were evaluated by using the gene set enrichment analysis. Enrichment analysis with Hallmark gene sets is shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA and Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e. Interferon (IFN)-α (type Ⅰ IFN), IFN-γ (type Ⅱ IFN), and complement responses were significantly enriched in the PC2-high group compared to the PC2-low and HC groups. On the other hand, type Ⅱ IFN and complement responses were not enriched between the PC2-low and HC groups. Moreover, type Ⅰ IFN response was rather enriched in the HC group compared to the PC2-low group. Consistently, gene expression data analysis (GEA) showed that gene expression of type Ⅰ IFN-stimulated gene (ISG) 15 and type Ⅱ IFN was increased in the PC2-high group compared to the HC and PC2-low groups. Moreover, gene expression of type Ⅰ IFN-inducible C-C motif chemokine ligand 2 (CCL2) and type Ⅱ IFN-inducible C-X-C motif chemokine ligand 10 (CXCL10)\\u003csup\\u003e18\\u003c/sup\\u003e showed the same results (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eB and Table \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e). Additionally, type I IFN induces the production of intracellular complement factor B (CFB) in type II alveolar epithelial (AT) cells, which generates C3 activation. Activated C3 fragments engage cognate receptor C3aR1 on immune cells and induce their activation.\\u003csup\\u003e19\\u003c/sup\\u003e The GEA analysis showed that gene expression of CFB was increased in the PC2-high group compared to the HC and PC2-low groups. C3aR1 gene expression was also increased in the PC2-high group compared to the PC2-low group (Table \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e). These results showed two biological phenotypes of COVID-19-induced ARDS: a high-IFN phenotype (PC2-high group) and a low-IFN phenotype (PC2-low group), indicated by up- and down-regulation of the IFN responses, respectively. On the other hand, enrichment analysis with Hallmark gene sets showed that tumor necrosis factor (TNF)-α signaling was enriched in the PC2-high group compared to the HC and PC2-low groups (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA and Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e). In the GEA analysis, gene expression of interleukin (IL)-6 was increased in the PC2-high compared to the PC2-low group, but gene expression of other inflammatory cytokines such as tumor necrosis factor (TNF)-α, IL-1β, and NLR family pyrin domain containing 3 (NLRP3) was not (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eC and Table \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e). Cell type signature analysis showed that signature gene sets for macrophages, monocytes, dendritic cells, T cell, neutrophils, natural killer cells, lymphatic cells, and basophil mast cells were enriched in the PC2-high group compared to the PC2-low group (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eD), suggesting a differential state of immune cell mobilization into the lungs. To evaluate inflammatory pathological damage in the lung, tight junction pathway, which are important for maintaining the structure of lung epithelial cells \\u003csup\\u003e20\\u003c/sup\\u003e, was assessed by the KEGG analysis. Tight junction pathway was significantly enriched in the PC2-high group compared to the HC and PC2-low groups (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eE), but was not between the HC and PC2-low group (Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e). The GEA analysis showed that gene expression of tight junction protein (TJP)1, TJP3, claudin (CLDN)3, and CLD8 was increased in the PC2-high compared to the HC and PC2-low groups (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eF and Table \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e). These results indicate that the physical lung damage by inflammation is more severe in the PC2-high group compared to the PC2-low group. Next, damage to AT cells was evaluated. In cell type signature analysis, gene set for type Ⅰ AT cells was enriched in the PC2-high group compared to the PC2-low group, whereas gene set for type Ⅱ AT cells was not enriched between the two groups (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eG). GSA showed that among signature genes for type Ⅱ AT cells, expression of SFTPA1 and SFTPA2 was increased only in the PC2-low group compared to the HC group, but expression of SFTPB, SFTPC, and NKX2.1 was increased in both PC2-low and PC2-high groups compared to the HC group (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eH and Table \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e). These results suggest that, unlike the PC2-high group, where both type I and type II cells were damaged, type Ⅱ AT cells were predominantly damaged in the PC2-low groups. Taken together, our data uncovered two biological phenotypes of COVID-19-induced ARDS based on distinct IFN response, which were characterized by differential damage to AT cells.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eClinical profiles in biological phenotypes\\u003c/h2\\u003e \\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e shows comparisons of the clinical profiles between PC2-low and PC2-high groups. There were no significant differences of age, gender, body mass index, duration from symptom onset until BAL, and comorbidities between the two groups. Blood laboratory data, such as cell counts of white blood cell (WBC), neutrophil, lymphocyte, red blood cell (RBC), and platelet and plasma levels of aspartate aminotransferase, lactate dehydrogenase, C-reactive protein (CRP), and D-dimer, also did not differ between the two groups. Moreover, SARS-CoV-2 RNA copy number in the BALF followed the same manner. The use of immunosuppressants prior to admission and medications post-admission such as antivirals, dexamethasone, and anticoagulants were also not significantly different between the two groups. Regarding outcomes, there were no significant differences in ECMO use and length of ICU stay, although mortality was significantly higher in the PC2-high group than in the PC2-low group. Collectively, the biological phenotypes in COVID-19-induced ARDS could not be understood by the clinical profile.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eComparisons of patient characteristics\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePC2-low (n\\u0026thinsp;=\\u0026thinsp;6)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePC2-high (n\\u0026thinsp;=\\u0026thinsp;6)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ep\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge (Year)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e58[\\u003cspan additionalcitationids=\\\"CR55 CR56 CR57 CR58 CR59 CR60 CR61 CR62\\\" citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e54\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e63\\u003c/span\\u003e]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e57[\\u003cspan additionalcitationids=\\\"CR51 CR52 CR53 CR54 CR55 CR56 CR57 CR58 CR59 CR60 CR61 CR62 CR63 CR64 CR65 CR66 CR67 CR68 CR69 CR70 CR71 CR72\\\" citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR73\\\" class=\\\"CitationRef\\\"\\u003e73\\u003c/span\\u003e]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.8721\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGender (Male)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5(83%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5(83%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBMI (kg/m\\u003csup\\u003e2\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e32.0[21.2\\u0026ndash;36.3]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e28.6[20.7\\u0026ndash;41.8]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.9361\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eComorbidities\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHT n, (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3(50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4(67%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.5571\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDM n, (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0(%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2(33%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.0748\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCOPD n, (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2(33%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2(33%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eClinical course\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDuration from onset to admission (day)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5.5[2.0\\u0026ndash;16.0]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5.0[1.5\\u0026ndash;10.0]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.569\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDuration from admission to intubation (day)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.5[0-4.5]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.5[0-1.3]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.4933\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDuration from intubation to BAL (day)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.5[0-1.8]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.5[0\\u0026ndash;10]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.1698\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDuration from onset to BAL (day)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e9.0[5.8\\u0026ndash;19.0]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e11.0[3.5\\u0026ndash;16.3]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.8102\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eLaboratory data\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWBC (Χ10\\u003csup\\u003e3\\u003c/sup\\u003e/\\u0026micro;L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e15.5[11.4\\u0026ndash;17.1]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e12.8[9.8\\u0026ndash;13.5]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.0927\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNeut (X10\\u003csup\\u003e3\\u003c/sup\\u003e/\\u0026micro;L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e13.0[8.9\\u0026ndash;15.2]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e10.4[8.1\\u0026ndash;11.7]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.1735\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLymph (X10\\u003csup\\u003e3\\u003c/sup\\u003e/\\u0026micro;L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.9[0.36\\u0026ndash;1.8]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.3[0.49\\u0026ndash;1.5]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.4712\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRBC (X10\\u003csup\\u003e6\\u003c/sup\\u003e/\\u0026micro;L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4.1[3.5\\u0026ndash;4.4]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.0[3.8\\u0026ndash;4.5]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.9660\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePLT (X10\\u003csup\\u003e4\\u003c/sup\\u003e/\\u0026micro;L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e29.8[12.5\\u0026ndash;42.7]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e18.6[14.8\\u0026ndash;23.4]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.2615\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAPTT (sec)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e36.9[32.7\\u0026ndash;77.2]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e50.5[36.1\\u0026ndash;69.6]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.5745\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePT-INR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.2[0.9\\u0026ndash;1.2]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.0[1.0-1.2]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.6874\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eD-dimer (\\u0026micro;g/mL)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3.6[1.7\\u0026ndash;16.8]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.6[1.0-28.8]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.4712\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAlb (g/dL)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.4[2.1\\u0026ndash;2.5]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.4[2.0-2.8]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.9360\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAST (IU/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e29.0[19.5\\u0026ndash;76.0]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e42.5[31.8\\u0026ndash;54.5]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.4712\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eALT (IU/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e26.5[16.5\\u0026ndash;45.5]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e33.5[21.3\\u0026ndash;44.5]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.6889\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLDH (IU/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e470.5[322.3-567.3]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e487.5[412.3-641.5]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.5752\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eeGFR (mL/min/1.73)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e82.7[56.7-104.8]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e53.7[29.0-90.5]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.3785\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCRP (mg/dL)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5.3[2.4\\u0026ndash;24.4]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.5[3.7\\u0026ndash;12.8]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.9362\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRNA copy number (Log10 copy/\\u0026micro;L)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3.4[-1.0-5.4]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.0[1.5\\u0026ndash;6.3]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.5529\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eMedications n, (%)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRemdesivir\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4(67%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6(100%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.0748\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDexamethasone\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4(67%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6(100%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.0748\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAnticoagulant\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e6(100%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6(100%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eImmunosuppressants before admission\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3(50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4(67%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.5571\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eOutcomes\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eECMO\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3(50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5(83%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.2129\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDuration of ICU stay (day)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e13.5[11.8\\u0026ndash;15.5]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e18.5[14.0\\u0026ndash;44.0]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.1081\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMortality\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0(0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3(50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.0229\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"4\\\"\\u003eAll categorical variables were presented as n (%). Continuous variables are shown as median values and [interquartile ranges]. BMI, body mass index; HT, hypertension; DM, diabetes; COPD, chronic obstructive pulmonary disease; BAL, Bronchoalveolar lavage; WBC, white blood cell; Neut, neutrophil; Lymph, lymphocyte; RBC, red blood cell; PLT, platelet; APTT, activated partial thromboplastin time; PT-INR, prothrombin time-international normalized ratio; Alb, albumin; AST, aspartate aminotransferase; ALT, alanine aminotransferase; LDH, lactate dehydrogenase; eGFR, estimated Glomerular Filtration Rate; CRP, C-reactive protein; ECMO, extracorporeal membrane oxygenation; ICU, intensive care unit.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eMolecular mechanisms underlying distinct type Ⅰ IFN response.\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eMolecular mechanisms underlying distinct type Ⅰ IFN response in COVID-19-induced ARDS were explored. Antiviral type I IFN response is initiated by recognition of SARS-CoV-2 via the retinoic acid-inducible gene I (RIG-I)-like receptor consisting of two central intracellular sentinels RIG-I and melanoma differentiation-associated protein 5 (MDA5) and the toll-like receptors (TLRs) including TLR3, TLR7, TLR8, and TLR9.\\u003csup\\u003e21\\u003c/sup\\u003e Consistently, the KEGG pathway analysis showed that RIG-I-like receptor and TLR signaling pathways were significantly enriched in the PC2-high group compared to the PC2-low group, but not between the PC2-low and HC groups (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA and Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eIn the RIG-I-like receptor signaling pathway, RIG-I and MDA5 engage the mitochondrial antiviral signaling protein (MAVS), leading to the phosphorylation of TANK-binding kinase 1 (TBK1) and IκB kinase-ε (IKKε), which in turn activates IFN regulatory factor 3 (IRF3), ultimately initiating the production of type I IFN.\\u003csup\\u003e21\\u0026ndash;22\\u003c/sup\\u003e In the GEA analysis, expression of MAVS, TBK1, and IRF3 genes did not show any significant differences between the PC2-low and PC2-high groups, while in contrast to the PC2-high group, up-regulation of IKKε gene expression was prevented in the PC2-low group. RIG-I and MDA5 gene expression was decreased in the PC2-low group compared to the HC and PC2-high groups (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eB and Table \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e). Alternatively, SARS-CoV-2 has also been reported to be recognized by cyclic GMP-AMP synthase (cGAS), a cytosolic DNA sensor, which activates stimulator of interferon genes (STING1) and promotes type I IFN production via TBK1 recruitment and activation.\\u003csup\\u003e23\\u003c/sup\\u003e Interestingly, cGAS gene expression was increased in the PC2-high group, but not in the PC2-low group, compared to the HC group. Moreover, STING1 gene expression was decreased in the PC2-low group compared to the HC and PC2-high groups (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eB and Table \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e). Therefore, inactivation of RIG-I, MDA5, and IKKε transcription in the RIG-I-like receptor signaling pathway and inactivation of cGAS and STING1 transcription in the cGAS-STING signaling pathway illustrate the repression of type I IFN response unique to the PC2-low group.\\u003c/p\\u003e \\u003cp\\u003eIn the TLR signaling pathway, TLR3 uniquely recruits Toll/interleukin-1 receptor domain-containing adapter protein (TRIF), which triggers activation of IRF3 through the phosphorylation of TBK1 and elicits type I IFN production.\\u003csup\\u003e24\\u003c/sup\\u003e The GEA analysis showed that gene expression of TLR3 and TRIF was increased in the PC2-high group compared to the HC and PC2-high groups, but did not differ between the HC and PC2-low groups (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eC and Table \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e). On the other hand, recognition of an appropriate viral ligand by all TLRs, except TLR3 initiates recruitment of myeloid differentiation primary-response protein 88 (MyD88) that interacts with interleukin 1 receptor associated kinase 1 (IRAK1) and IRAK4, which in turn initiates TNF receptor-associated factor 6 (TRAF6) ubiquitination and subsequent phosphorylation of IκB kinase (IKK), leading to the activation of NF-κB and IRFs such as IRF5 and IRF7, ultimately eliciting the transcription of multiple pro-inflammatory cytokines and type I IFN.\\u003csup\\u003e24\\u0026ndash;29\\u003c/sup\\u003e The GEA analysis showed that there were no significant differences in the gene expression of IRAK1, IRAK4, TRAF6, and IKK between the PC2-low and PC2-high groups, but IRF7 gene expression was increased in the PC2-high group compared to the HC and PC2-low groups. On the other hand, gene expression of TLR7, TLR8, MyD88, and IRF5 was decreased in the PC2-low group than in the HC group as well as in the PC2-high group (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eC and Table \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e). Thus, the inactivation of TLRs7/8, MyD88, and IRF5 transcription as well as TLR3 and TRIF transcription in the TLR signaling pathway is implicated in the repression of the type Ⅰ IFN response in the PC2-low group.\\u003c/p\\u003e \\u003cp\\u003eSubsequently, type Ⅰ IFN binding to IFN α and β receptor subunit 1 (IFNAR1) and IFNAR2 leads to the formation of the signal transducer and activator of transcription 1 (STAT1)/STAT2/IRF9 complex known as \\u0026ldquo;ISGF3,\\u0026rdquo; which translocates to the nucleus and binds to IFN-stimulated response elements, activating transcription of type I IFN-inducible ISGs with strong antiviral effects, such as 2'-5'-Oligoadenylate Synthetase (OAS), interferon induced protein with tetratricopeptide repeats (IFIT), RNAsel, and tripartite motif (TRIM).\\u003csup\\u003e30\\u0026ndash;31\\u003c/sup\\u003e The GEA analysis showed that despite no difference in gene expression of IFNAR1 and IFNAR2 between the PC2-low and PC2-high groups, gene expression of IRF9, STAT1, and STAT2 was decreased in the PC2-low group compared to the HC and PC2-high groups (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eD and Table \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e), suggesting disruption of ISGF3 transcription in the PC2-low group. Moreover, type I IFN-inducible ISGs gene expression also showed the same results (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eD and Table \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e). Collectively, these data suggest that systematic and multifaced inactivation of type I IFN-related signaling pathways underlies the low-IFN phenotype in COVID-19-induced ARDS.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eMolecular mechanisms underlying distinct type Ⅱ IFN response.\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eWe also explored molecular mechanisms underlying distinct type Ⅱ IFN response in COVID-19-induced ARDS. Binding of IFN-γ to its receptor 1 (IFN-γR1) activates STAT1 in antigen presenting cells, which transactivates IRF8 expression, leading to IL-12 production.\\u003csup\\u003e32\\u003c/sup\\u003e Subsequently, engagement of IL-12 to IL-12 receptor on Th1 T cells further stimulates and amplifies IFN-γ production via STAT4.\\u003csup\\u003e32\\u003c/sup\\u003e The GEA analysis showed that gene expression of IFN-γR1 and IRF8 was decreased in the PC2-low group compared to the HC and PC2-high groups. Moreover, in contrast to the PC2-high group, upregulation of IL-12 receptor subunits β1 and β2 (IL-12Rβ1 and IL-12Rβ2) and STAT4 gene expression was suppressed in the PC2-low group (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eA and Table \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e). Thus, inactivation of the IRF8-mediated IL-12/IFN-γ loop between immune cells at the transcriptional level is implicated in the impaired type Ⅱ IFN response.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eAlternatively, IFN-γ transactivates IRF1 expression in infected epithelial cell, leading to IL-15 and IL-15 receptor α-subunit (IL-15Rα) expression.\\u003csup\\u003e33\\u0026ndash;35\\u003c/sup\\u003e Subsequently, IL-15 and IL-15Rα on epithelial cells forms a complex with the heterodimer of the IL‑2/IL‑15 receptor β-chain (IL‑2/IL‑15Rβ) and the common cytokine receptor γ-chain (γc) on group 1 innate lymphoid cells (ILC1s) and natural killer (NK) cells, conferring IFN-γ production by ILC1s and NK cells and cytotoxic pathways in NK cells.\\u003csup\\u003e33\\u003c/sup\\u003e The GEA analysis showed that gene expression of γc did not show any differences among the HC, PC2-low and PC2-high groups. Additionally, IL‑2/IL‑15Rβ gene expression was increased in the PC2-low and PC2-high groups compared to the HC group, but did not differ between the PC2-low and PC2-high groups. However, gene expression of IL-15 was decreased in the PC2-low group compared to the HC and PC2-high groups. Moreover, in contrast to the PC2-high group, upregulation of IL-15Rα and IRF1 gene expression was prevented in the PC2-low group (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eB and Table \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e). Thus, inactivation of IRF1-mediated IL-15/IL-15Rα/IFN-γ loop between infected epithelial cells and immune cells at the transcriptional level can generate impaired type Ⅱ IFN response in COVID-19-induced ARDS lung.\\u003c/p\\u003e \\u003cp\\u003eSARS-CoV-2 cause the downregulation of expression of MHC class I molecules as an immune evasion mechanism to prevent the destruction of infected cells by cytotoxic T lymphocytes (CTLs).\\u003csup\\u003e36\\u003c/sup\\u003e Instead, the host upregulates the expression of IL‑15 and the non-classical MHC class I molecules such as MHC class I polypeptide-related sequence A (MICA) in infected cells. In turn, IL-15 upregulates the expression of activating NK receptors such as natural killer group 2, member D (NKG2D), which can recognize MICA, on CTLs, which in cooperation with the adaptor molecule DNAX-activation protein 10 (DAP10), confers type Ⅱ IFN-activated killer activity on the CTLs.\\u003csup\\u003e33\\u003c/sup\\u003e The GEA analysis showed that NKG2D gene expression was increased in the PC2-low and PC2-high groups compared to the HC group, while gene expression of MICA and DAP10 was decreased in the PC2-low group compared to the HC group as well as the PC2-high group (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eC and Table \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e). Inactivation of the IRF1-IL-15-DAP10-IFN-γ axis across infected epithelial and immune cells, which interferes with linkage between MICA and NKG2D, illustrates a novel mechanism by which SARS-CoV-2 suppresses the cytotoxic effect of immune cells on infected cells.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eDifferential unfolded protein response in biological phenotypes.\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eUnder coronavirus infection, endoplasmic reticulum (ER) stress emerges as explosive accumulation of misfolded or unfolded proteins in the ER of infected host cells and induces apoptosis in infected cells\\u003csup\\u003e37\\u003c/sup\\u003e, while simultaneously inducing an adaptive cellular response in the ER known as the unfolded protein response (UPR).\\u003csup\\u003e38\\u003c/sup\\u003e UPR is consisted of the three branches, namely inositol-requiring 1α (IRE1α), double-stranded RNA-dependent protein kinase (PKR)-like ER kinase (PERK), and activating transcription factor 6 (ATF6) signaling pathways.\\u003csup\\u003e39\\u003c/sup\\u003e The SARS-CoV-2 can activate these branches of the UPR.\\u003csup\\u003e40\\u003c/sup\\u003e In the REACTOME analysis, the IRE1α signaling pathway was enriched in the PC2-low group compared to the PC2-high and HC groups (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eA and Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e). Consistently, the GEA analysis showed that the expression of target genes of IRE1α branch, including genes encoding ER molecular chaperones (DNAJB9 and HSPA5) that refold proteins and genes encoding the ER-associated degradation (ERAD) component (EDEM1, Herpud1, and HRD1) which pulls ER stress proteins out of the ER lumen and passes them to proteasomes for degradation \\u003csup\\u003e41\\u0026ndash;44\\u003c/sup\\u003e, was increased in the PC2-low group compared to the PC2-high and HC groups (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eB and Table \\u003cspan refid=\\\"MOESM3\\\" class=\\\"InternalRef\\\"\\u003eS3\\u003c/span\\u003e). Moreover, IRE1α signaling controls ER stress-induced cell apoptosis by degrading death receptor 5 (DR5) mRNA, a pro-apoptotic gene, also known as TNFRSF10B.\\u003csup\\u003e45\\u003c/sup\\u003e Consistently, the DR5 gene expression was decreased in the PC2-low group compared to the HC and PC2-high groups (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eB and Table \\u003cspan refid=\\\"MOESM3\\\" class=\\\"InternalRef\\\"\\u003eS3\\u003c/span\\u003e). Alternatively, activated ATF6 branch induces the transcription of XBP1, a critical adaptor of IRE1α signaling pathway.\\u003csup\\u003e46\\u0026ndash;47\\u003c/sup\\u003e XBP-1 gene expression was increased in the PC2-low group compared to the PC2-high and HC groups (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eB and Table \\u003cspan refid=\\\"MOESM3\\\" class=\\\"InternalRef\\\"\\u003eS3\\u003c/span\\u003e). These results suggest that in the low-IFN phenotype, IRE1α and ATF6 branches cooperatively enhance the UPR, which adaptively processes ER stress and suppresses ER stress-induced pro-apoptotic signal in SARS-CoV-2-infected cells.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eConversely, the PERK signaling pathway elicits apoptosis in infected cells via activating transcription of the C/EBP homologous protein (CHOP) and ATF3 genes.\\u003csup\\u003e38, 48\\u003c/sup\\u003e The GEA analysis showed that the expression of CHOP and ATF3 genes was increased in the PC2-high group compared to the HC and PC2-low groups (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eC and Table \\u003cspan refid=\\\"MOESM3\\\" class=\\\"InternalRef\\\"\\u003eS3\\u003c/span\\u003e). CHOP triggers apoptosis by inducing the transcription of DR5, growth arrest and DNA damage-inducible gene 34 (GADD34), ER oxidoreductin-1α (ERO1α), and tribbles-related protein 3 (Trb3).\\u003csup\\u003e49\\u0026ndash;50\\u003c/sup\\u003e Consistently, the gene expression of GADD34, ERO1α, and Trb3 was increased in the PC2-high group compared to the PC2-low group (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eC and Table \\u003cspan refid=\\\"MOESM3\\\" class=\\\"InternalRef\\\"\\u003eS3\\u003c/span\\u003e), as was gene expression of DR5 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eB). Enrichment analysis with Hallmark gene sets showed the enrichment of apoptosis pathway in the PC2-high group compared to the PC2-low and HC groups (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eD and Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e). The expression of pro-apoptotic BAX and BAK genes also showed the same results (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eE and Table \\u003cspan refid=\\\"MOESM3\\\" class=\\\"InternalRef\\\"\\u003eS3\\u003c/span\\u003e).\\u003csup\\u003e51\\u003c/sup\\u003e These observations suggest that the state of the IFN response regulates the balance between IRE1α and PERK branches and influences apoptotic signaling.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eDifferential free hemoglobin catabolism in biological phenotypes\\u003c/h2\\u003e \\u003cp\\u003eIt has been demonstrated that RBCs are present in the alveolar space of patients with ARDS \\u003csup\\u003e3\\u003c/sup\\u003e and that free hemoglobin released from RBCs by hemolysis and heme derived from free hemoglobin cause ARDS by damaging the alveolar-capillary barrier responsible for alveolar permeability \\u003csup\\u003e52\\u003c/sup\\u003e and the type Ⅱ AT cells responsible for gas exchange and maintaining lung compliance.\\u003csup\\u003e3, 53\\u0026ndash;54\\u003c/sup\\u003e On the other hand, free hemoglobin or heme is processed by the binding of hemoglobin:haptoglobin or heme:hemopexin complexes to the CD163 or CD91/LRP1 scavenger receptors on myeloid cells, respectively.\\u003csup\\u003e55\\u0026ndash;56\\u003c/sup\\u003e In canonical pathway analysis with REACTOME gene sets, heme scavenging pathway and binding and uptake of ligands by scavenger receptors pathway were enriched in the PC2-low group compared to the PC2-high group (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eA and Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e). The GEA analysis showed that expression of hemoglobin genes such as HBA1, HBA2, and HBB was significantly increased in the PC2-low and PC2-high groups than in the HC group (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eB and Table \\u003cspan refid=\\\"MOESM3\\\" class=\\\"InternalRef\\\"\\u003eS3\\u003c/span\\u003e). However, up-regulation of CD163 gene expression was diminished in the PC2-low group in contrast to the PC2-high group, while the expression of CD91 gene did not show significant differences among the HC, PC2-low, and PC2-high groups (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eB and Table \\u003cspan refid=\\\"MOESM3\\\" class=\\\"InternalRef\\\"\\u003eS3\\u003c/span\\u003e). These results suggest that, in contrast to the high-IFN phenotype, free hemoglobin catabolism in the lung by CD163-expressing myeloid cells is impaired in the low-IFN phenotype.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe hemoglobin scavenger receptor CD163 is uniquely expressed on monocytes and macrophages, and its expression is strongly up-regulated by IL-10.\\u003csup\\u003e57\\u0026ndash;58\\u003c/sup\\u003e Consistently, cell type signature analysis of BALF cells suggested the less recruitment of macrophage and monocyte into the alveolar space in the PC2-low group compared to the PC2-high group (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eD). Moreover, GEA analysis showed that up-regulation of IL-10 gene expression was diminished in the PC2-low group in contrast to the PC2-high group (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eB and Table \\u003cspan refid=\\\"MOESM3\\\" class=\\\"InternalRef\\\"\\u003eS3\\u003c/span\\u003e), as was CD163 gene expression. The IL-10 gene expression is up-regulated not only by type Ⅰ IFN but also by various transcription factors such as Foxp3, IRF4, Blimp1, MAF, and E4BP4.\\u003csup\\u003e59\\u003c/sup\\u003e Among transcriptional factors, the expression of E4BP4 gene was decreased consistently in the PC2-low group compared to the PC2-high group, but expression of IRF4 and Blimp1 genes did not show any significant differences between the 2 groups (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eC and Table \\u003cspan refid=\\\"MOESM3\\\" class=\\\"InternalRef\\\"\\u003eS3\\u003c/span\\u003e). Taken together, apart from reduced recruitment of macrophage and monocyte, diminished up-regulation of IL-10 by transcriptional inactivation of E4BP4 is implicated in the suppression of CD163 expression in the low-IFN phenotype.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eHere, we report the results of transcriptome profiling of BALF cells from critically ill patients with COVID-19-induced ARDS. Transcriptome heterogeneity in the present study uncovered two biological phenotypes illustrated by distinct IFN responses in the lung, their underlying mechanisms, and related pathogenesis. On the other hand, the biological phenotypes were found to be indistinguishable in the clinical profile. These results highlight clinical importance of assessing transcriptome profiles in the lung to understand biological heterogeneity of COVID-19-induced ARDS and develop precision medicine.\\u003c/p\\u003e \\u003cp\\u003eSARS-CoV-2 elicits type Ⅰ and II IFN responses and generates COVID-19 manifested as cytokine storm.\\u003csup\\u003e21, 60\\u0026ndash;61\\u003c/sup\\u003e On the other hand, these responses can also be suppressed by SARS-CoV-2, as its genome contains open reading frames that encode for accessory proteins important for the modulation of the host\\u0026rsquo;s infected cell metabolism and innate immunity evasion.\\u003csup\\u003e17\\u003c/sup\\u003e Transcriptome data of BALF cells showed two distinct biological phenotypes in COVID-19-induced ARDS lungs, namely the high-IFN and low-IFN phenotypes. It has been demonstrated that SARS-CoV-2 targets the RIG-I/MDA5, TLR3-TRIF, and IFNAR-ISGF3 signaling pathways and interferes with type Ⅰ IFN response. \\u003csup\\u003e17, 21\\u003c/sup\\u003e However, type Ⅰ IFN response-related signaling pathways targeted by SARS-CoV-2 to evade host defense are not yet fully understood. Here, our data provide new evidence that SARS-CoV-2 may target the cGAS-STING1 and TLRs7/8-MyD88-IRF5 signaling pathways to suppress type I IFN response. Alternatively, our data suggest that SARS-CoV-2 may inhibit the type II IFN response by targeting the IL-12-STAT4-IFN-γ loop via IRF8 across immune cells, which links MHC class I-mediated antigen presentation and cytotoxic activation. SARS-CoV-2 has also been shown to block the MHC class I-mediated antigen presentation pathway in infected epithelial cells by targeting the STAT1-IRF1-NLRC5, an MHC class I transactivator axis, thereby evading IFN-γ-mediated cytotoxic effect against infected cells.\\u003csup\\u003e36\\u003c/sup\\u003e On the other hand, the host upregulates the expression of IL‑15 and non-classical MHC class I molecule, MICA in infected epithelial cells via IRF1 signaling, which in turn confers IFN-γ-mediated cytotoxic activity on the immune cells in cooperation with the adaptor DAP10.\\u003csup\\u003e33\\u003c/sup\\u003e Our data imply a novel immune evasion mechanism of SARS-CoV-2 that further suppresses the MICA-mediated cytotoxic activation by targeting the IRF1-IL-15-DAP10-IFN-γ axis. SARS-CoV-2 has a multifaceted impact on the host defense system to survive. Our observations offer new insight into mechanisms by which SARS-CoV-2 hinders type Ⅰ and II IFN responses and generates their heterogeneity in lungs.\\u003c/p\\u003e \\u003cp\\u003eIFN response states may cause different damage to alveoli. Our data suggest that, unlike the inflammatory damage to both type Ⅰ and type Ⅱ AT cells in the high-IFN phenotype, damage to type Ⅱ AT cells was predominant in the low-IFN phenotype. SARS-CoV-2 primarily infects and severely damages type Ⅱ AT cells rather than type Ⅰ AT cells, causing ARDS.\\u003csup\\u003e62\\u0026ndash;63\\u003c/sup\\u003e IFN response generates inflamed manifestation of disease, while also helping clearance of SARS-CoV-2, through eliciting the host immune system.\\u003csup\\u003e64\\u003c/sup\\u003e The pathogenesis of ARDS in the low-IFN phenotype may be typified by severe damage to type Ⅱ AT cells due to increased viral replication, rather than inflammatory pathological changes.\\u003c/p\\u003e \\u003cp\\u003eViral replication is regulated not only by the immune system but also by apoptosis. SARS-CoV-2-infected host cells induce apoptosis via ER stress to restrict viral replication.\\u003csup\\u003e37\\u0026ndash;38\\u003c/sup\\u003e These responses are regulated by the UPR consisted of three signaling pathways involving IRE1α, PERK and ATF6.\\u003csup\\u003e37\\u0026ndash;39\\u003c/sup\\u003e During coronavirus infection, the PERK signaling pathway directly links ER stress to apoptosis in infected host cells, whereas the IRE1α signaling pathway blocks this link.\\u003csup\\u003e37, 48, 51\\u003c/sup\\u003e Interestingly, in the high-IFN phenotype, the PERK signaling pathway was predominantly activated, as evidenced by promoted transcriptional activation of CHOP, which is critical for inducing apoptosis. However, in the low-IFN phenotype, predominant activation of the IRE1α signaling pathway was observed, accompanied by the decay of DR5 transcript, a pro-apoptotic gene targeted by CHOP. These observations provide a novel idea that the state of the IFN response regulates ER stress-induced apoptosis and viral replication in infected type Ⅱ AT cells by affecting the balance between the PERK and IRE1α branches.\\u003c/p\\u003e \\u003cp\\u003eFree hemoglobin released by hemolysis of RBCs in the alveolar space can cause damage to type II AT cells predominantly through binding to cell surface receptors, causing ARDS.\\u003csup\\u003e53\\u003c/sup\\u003e Whether and how the IFN response status affects free hemoglobin load, however, remain elusive. Our data highlight that, in contrast to the high-IFN phenotype, the low-IFN phenotype renders type II AT cells more susceptible to free hemoglobin load by diminishing the elevated expression of the free hemoglobin scavenger receptor CD163. This phenomenon can be explained by promoted recruitment of CD163-expressing macrophages and monocytes in the alveolar space due to type I IFN-inducible chemokines, as previously reported.\\u003csup\\u003e64\\u0026ndash;65\\u003c/sup\\u003e Alternatively, our data emphasized an association between IL-10 and CD163. In view of the regulatory mechanisms of IL-10 expression \\u003csup\\u003e66\\u0026ndash;67\\u003c/sup\\u003e, the IFN response and the transcriptional activation of E4BP4 may up-regulate CD163 expression in the COVID-19 lung. These observations underline the novel involvement of the IFN response in the lung in the catabolism of pathogenic free hemoglobin, which is responsible for COVID-19-induced ARDS.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eClinical implications\\u003c/h2\\u003e \\u003cp\\u003eMortality was lower in the low-IFN phenotype compared to the high-IFN phenotype. However, even the low-IFN phenotype required ECMO for severe respiratory failure and prolonged management in the ICU. Understanding and modulating the state of IFN response in the lung may contribute to evasion or early withdrawal from ECMO. The clinical benefits of therapeutic strategies targeting IFN response on COVID-19-induced ARDS should be explored.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStudy limitations\\u003c/h2\\u003e \\u003cp\\u003eThis study has some limitations. BALF cells contained heterogeneous cells and bulk analysis of them could not intrinsically identify biological processes in single cells. Thus, we could not precisely determine in which cells the observed differences in gene expression were occurring. However, as discussed above, our comparative analysis suggests that BALF has practical utility for assessing heterogeneous biological phenomena in the lung. Our sample size limits the generalizability of these findings and requires validation in a larger cohort. We were unable to directly measure protein expression in the lower airway, which limits the scope of our biological analysis. Ultimately, the results in this study may need to be validated in experimental models. In the present study, transcriptomic data from BALF cells suggested various potential therapeutic targets based on the biological heterogeneity of COVID-19-induced ARDS. Unfortunately, BAL is highly invasive and can only be performed in limited patients. In the future, comprehensive proteome analysis of peripheral blood proteins is needed to identify biomarkers indicative of heterogeneous pathogenesis and therapeutic targets in the ARDS lungs.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Conclusions\",\"content\":\"\\u003cp\\u003eTranscriptome profiling of BALF cells uncovered the two biological phenotypes of COVID-19-induced ARDS based on distinct IFN response. Underlying the low-IFN phenotype, unlike the common high-IFN phenotype, was a multifaced inactivation of the MyD88-IRF5 and cGAS-STING1 axes associated with type I IFN response and the IRF8-IL-12-STAT4 and IRF1-IL-15-DAP10 axes associated with type II IFN response as well as the PERK-CHOP axis and the IL-10-CD163 axis. The pathogenesis of ARDS in the low-IFN phenotype was illustrated by severe damage to type Ⅱ AT cells due to increased viral replication by reduced antiviral response, cytotoxicity, and apoptotic signaling and impaired free hemoglobin catabolism. The present study advances our understanding of COVID-19-induced ARDS and contributes to the development of precision medicine for emerging and re-emerging lethal viral infections.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section3\\\"\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \"},{\"header\":\"Methods\",\"content\":\"\\u003ch2\\u003eStudy design and setting\\u003c/h2\\u003e\\u003cp\\u003eThis was prospective analysis of data set from a multi-center observational COVID-19 study across two advanced medical institutions in Japan. In the observational study, 16 patients with ARDS due to COVID-19, who were underwent BAL for diagnosis, admitted to the Yokohama City University Hospital and Yokohama City University Medical Center between April 2021 and February 2022 were enrolled and observed until hospital discharge after the enrollment.\\u003c/p\\u003e\\u003ch2\\u003eEthical considerations\\u003c/h2\\u003e\\u003cp\\u003e This study was approved by the Institutional Ethics Board of the Yokohama City University Hospital (No. B210100010). All methods were conducted in accordance with relevant guidelines and regulations (Declaration of Helsinki). Patients were provided negative and positive information regarding this study, including the purpose and contribution of this study, the use of personal information, and complications associated with blood and BALF collection, and were asked to participate in this study. Ultimately, we obtained agreement from the patients to participate in the study and to access their medical and laboratory records by written informed consent or Opt-out method. The study had no risks or negative consequences for those who participated in the study. Medical record numbers were used for data collection and no personal identifiers were collected or used in the research report.\\u003c/p\\u003e\\u003ch2\\u003ePatients\\u003c/h2\\u003e\\u003cp\\u003eThe inclusion criteria were as follows: (1) admission to the ICU for invasive mechanical ventilation for ARDS, (2) older than 18 years in age, (3) availability of BALF specimen within 10 days after intubation. The exclusion criteria were as follows: (1) no BALF specimen available within 10 days of intubation, (2) inadequate quality of BALF specimens for RNA-seq, (3) those with missing data, including clinical, laboratory, and outcome data, (4) those without consent for participation. Ultimately, in 13 patients with COVID-19-induced ARDS, clinical and transcriptomic data were evaluated.\\u003c/p\\u003e\\u003ch2\\u003eClinical data collection\\u003c/h2\\u003e\\u003cp\\u003eRoutinely available clinical data (demographic data, comorbidities, and clinical courses since symptom onset), laboratory data, treatment data before and after admission, and clinical outcomes after admission were collected and stored securely. Subject charts, chest X-rays, and chest computed tomography scan were reviewed by at least two study authors to confirm a diagnosis of ARDS using the Berlin Definition.\\u003csup\\u003e68\\u003c/sup\\u003e COVID-19 was diagnosed by two study physicians using either nucleic acid amplification test or antigen test.\\u003c/p\\u003e\\u003ch2\\u003eSample processing and RNA sequencing\\u003c/h2\\u003e\\u003cp\\u003eAfter the collection of BALF, fresh samples were transported to a BSL-3 laboratory at ambient temperature. Cells were pelleted at 300 × g at 4°C for 5 min, and ammonium-chloride-potassium lysing buffer was added at room temperature for 2 min to lysate RBCs, followed by the addition of PFE buffer containing 2% FBS and 1mM EDTA in 1X phosphate-buffered saline at 4°C for stopping the reaction. Resuspended cells with PFE buffer were centrifuged at 300 × g at 4°C for 5 min, followed by passage through a 70-mm filter and cell count on a hemocytometer. RNA isolation was performed using RNeasy Micro Kit (QIAGEN) according to the manufacturer’s protocol. The quality of isolated RNA was evaluated by the TapeStation system using High Sensitivity RNA ScreenTape (Agilent). The stranded libraries were prepared and subjected to 150-bp paired-end sequencing on NovaSeq 6000 (Illumina), generating more than 20\\u0026nbsp;million reads per sample.\\u003c/p\\u003e\\u003ch2\\u003eExpression data generation, principal component analysis, and differential expression analysis\\u003c/h2\\u003e\\u003cp\\u003eReads were aligned to the GRCh38 reference sequence using STAR.\\u003csup\\u003e69\\u003c/sup\\u003e Raw read counts and transcripts per million data were obtained with StringTie.\\u003csup\\u003e70\\u003c/sup\\u003e In the edgeR package \\u003csup\\u003e71\\u003c/sup\\u003e, low-count genes were filtered by the filterByExpr function, and trimmed mean of M values (TMM)-normalized counts were calculated by the calcNormFactor function. To examine sample clustering, the plotMDS function was executed with the \\\"gene.selection\\\" parameter set to \\\"common\\\", which calculates principal components. Differential expression analysis was done by the glmQLFTest function.\\u003c/p\\u003e\\u003ch2\\u003eComparison against external datasets\\u003c/h2\\u003e\\u003cp\\u003eThe RNA-seq reads for three BALF samples from healthy controls without ARDS and COVID-19 (HC) were obtained from the SRA database (accession numbers, SRR10571724, SRR10571730 and SRR10571732) \\u003csup\\u003e72\\u003c/sup\\u003e and processed in the same manner as described above. Transcriptome data were compared between COVID19-induced ARDS patients and HC.\\u003c/p\\u003e\\u003ch2\\u003eGene set enrichment analysis (GSEA)\\u003c/h2\\u003e\\u003cp\\u003eAfter clustering by principal component analysis of genome-wide expression profiles from 13 patients with COVID-19-induced ARDS and 3 HC, transcriptional profiles belonging to each cluster were analyzed by enriching gene expression data generated by TMM-normalized gene counts with ontology gene sets including three subsets of biological process (BP), molecular function (MF), and cellular component (CC), Hallmark gene sets, and pathway gene sets including KEGG and REACTOME from the Human Molecular Signatures Database (MSigDB).\\u003csup\\u003e73\\u003c/sup\\u003e\\u003c/p\\u003e\\u003ch2\\u003eQuantification of viral RNA load in BALF\\u003c/h2\\u003e\\u003cp\\u003eRNA extraction from BALF supernatants of COVID-19 patients was performed by QIAamp Viral RNA Mini Kit (QIAGEN, 52906) according to the manufacturer’s instructions. The viral gene was quantified as Ct-value by real-time qPCR with N2 primer pairs (TaKaRa, XD0008, forward primer: AAATTTTGGGGACCAGGAAC, reverse primer: TGGCAGCTGTGTAGGTCAAC, probe: FAM-ATGTCGCGCATTGGCATGGA-BHQ). Ultimately, viral RNA load was calculated by plotting Ct-values onto the standard curve constructed based on the standard product (NIHON GENE RESEARCH LABORATORIES, JP-NN2-PC), as previously described.\\u003csup\\u003e74\\u003c/sup\\u003e\\u003c/p\\u003e\\u003ch2\\u003eStatistical analysis\\u003c/h2\\u003e\\u003cp\\u003eAll analyses for clinical data were performed using the JMP ver. 12.2 software and Python 3. 10. 4. All categorical variables were presented as frequency (%). Continuous variables were shown as median and interquartile range [IQR]. Categorical and continuous variables were compared by chi-square analysis and tests of variance, respectively. P \\u0026lt; 0.05 was set as the threshold for screening significant results. In the GSEA analysis, statistical significance (nominal P value \\u0026lt; 0.05) of enrichment for gene sets in the phenotype was calculated by using an empirical phenotype-based permutation test procedure that preserves the complex correlation structure of the gene expression data generated by TMM-normalized gene counts.\\u003csup\\u003e73\\u003c/sup\\u003e The degree to enrichment was indicated as a normalized enrichment score (NES). In gene expression data analysis, gene expression data were expressed as log2 fold change relative to the mean of the healthy control gene expression data. P-values indicating significant differences in gene expression data between groups were determined by ANOVA test followed by the Wilcoxon test.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cp\\u003eA new coronavirus: COVID-19\\u003c/p\\u003e\\n\\u003cp\\u003eARDS: acute respiratory distress syndrome\\u003c/p\\u003e\\n\\u003cp\\u003eECMO: extracorporeal membranous oxygenation\\u003c/p\\u003e\\n\\u003cp\\u003eBALF: bronchoalveolar lavage fluid\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eICU:\\u0026nbsp;intensive care unit\\u003c/p\\u003e\\n\\u003cp\\u003eIFN:\\u0026nbsp;interferon\\u003c/p\\u003e\\n\\u003cp\\u003eIL: interleukin\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eEthics approval and consent to participate\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study was approved by the Institutional Ethics Board of the Yokohama City University Hospital (No. B210100010).\\u0026nbsp;We obtained written informed consent for participation in the study and access to medical and laboratory records from patients.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eAvailability of data and materials\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe datasets used during this study are available from the corresponding author on reasonable request.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eCompeting interests\\u003c/em\\u003e\\u003c/strong\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that there are no conflicts of interest regarding the publication of this paper.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eFunding\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis research was funded by the Japan Agency for Medical Research and Development (grant no. 20fk0108405h0001), the Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research (grant no. 21K09026), and Yokohama foundation for advancement of medical science.\\u0026nbsp;These funding organizations did not play a role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript and did not provide financial support in the form of authors\\u0026apos; salaries. These funding sources only provided financial support in the form of research materials.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eAuthors\\u0026apos; contributions\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eMN prepared and wrote the manuscript, collected the references, and performed the analysis of clinical and transcriptome data. TB and TT participated in the bioinformatic analysis, provided suggestions, and supervised the study. MN organized and reviewed the manuscript and coordinated all authors. KS and HO performed RNA extraction from BALF supernatants and analyzed Ct values of virus RNA. HH, RS, RM, and KS performed RNA extraction from BALF cells, and HH measured their\\u0026nbsp;quality.\\u0026nbsp;IT provided clinical support. All authors read and approved the final manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eAcknowledgments\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe thank our colleagues at the Department of Emergency Medicine, Yokohama City University, Yokohama City University Hospital, and\\u0026nbsp;Yokohama City University Medical Center\\u0026nbsp;for their kind assistance.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eCorresponding author\\u003c/em\\u003e\\u003c/strong\\u003e\\u003cstrong\\u003e:\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003e\\u003c/strong\\u003eMototsugu Nishii\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eLium B. Adult respiratory distress syndrome (ARDS). Incidence, clinical findings, pathomorphology and pathogenesis. A review. Nord Vet Med. 1983;35:38\\u0026ndash;47.\\u003c/li\\u003e\\n\\u003cli\\u003eJanz DR, Ware LB. Biomarkers of ALI/ARDS: pathogenesis, discovery, and relevance to clinical trials. Semin Respir Crit Care Med. 2013;34:537\\u0026ndash;548. doi: 10.1055/s-0033-1351124.\\u003c/li\\u003e\\n\\u003cli\\u003eJanz DR, Ware LB. The role of red blood cells and cell-free hemoglobin in the pathogenesis of ARDS. J Intensive Care. 2015;3:20. doi: 10.1186/s40560-015-0086-3.\\u003c/li\\u003e\\n\\u003cli\\u003eWang D, et al. Clinical Characteristics of 138 Hospitalized Patients with 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China. JAMA. 2020;323:1061\\u0026ndash;1069. doi: 10.1001/jama.2020.1585.\\u003c/li\\u003e\\n\\u003cli\\u003eHuang C, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395:497506. doi: 10.1016/S0140-6736(20)30183-5.\\u003c/li\\u003e\\n\\u003cli\\u003eChen N, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. 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Sci Rep. 2020;10:19395. doi: 10.1038/s41598-020-76404-8. \\u003c/li\\u003e\\n\\u003cli\\u003eGrant RA, et al. Circuits between infected macrophages and T cells in SARS-CoV-2 pneumonia. Nature. 2021;590:635\\u0026ndash;641. \\u003c/li\\u003e\\n\\u003cli\\u003eSarma A, et al. Tracheal aspirate RNA sequencing identifies distinct immunological features of COVID-19 ARDS. Nat Commun. 2021;12:5152. doi: 10.1038/s41467-021-25040-5. \\u003c/li\\u003e\\n\\u003cli\\u003eMinkoff JM, tenOever B. Innate immune evasion strategies of SARS-CoV-2. Nat Rev Microbiol. 2023;21:178\\u0026ndash;194. doi: 10.1038/s41579-022-00839-1.\\u003c/li\\u003e\\n\\u003cli\\u003eLehmann MH, et al. CCL2 expression is mediated by type I IFN receptor and recruits NK and T cells to the lung during MVA infection. J Leukoc Biol. 2016;99:1057\\u0026ndash;1064. doi: 10.1189/jlb.4MA0815-376RR.\\u003c/li\\u003e\\n\\u003cli\\u003eAfzali B, Noris M, Lambrecht BN, Kemper C. The state of complement in COVID-19. 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Hum Genet. 2020;139:707\\u0026ndash;721. doi: 10.1007/s00439-020-02154-2.\\u003c/li\\u003e\\n\\u003cli\\u003eJabri B, Abadie V. IL-15 functions as a danger signal to regulate tissue-resident T cells and tissue destruction. Nat Rev Immunol. 2015;15:771\\u0026ndash;783. doi: 10.1038/nri3919.\\u003c/li\\u003e\\n\\u003cli\\u003eOgasawara K, Hida S, Azimi N, Tagaya Y, Sato T, Yokochi-Fukuda T, Waldmann TA, Taniguchi T, Taki S. Requirement for IRF-1 in the microenvironment supporting development of natural killer cells. Nature. 1998;391:700\\u0026ndash;703. doi: 10.1038/35636. \\u003c/li\\u003e\\n\\u003cli\\u003eKim TS, Rha MS, Shin EC. IFN-\\u0026gamma; Induces IL-15 Trans-Presentation by Epithelial Cells via IRF1. J Immunol. 2022;208:338\\u0026ndash;346. doi: 10.4049/jimmunol.2100057.\\u003c/li\\u003e\\n\\u003cli\\u003eYoo JS, et al. SARS-CoV-2 inhibits induction of the MHC class I pathway by targeting the STAT1-IRF1-NLRC5 axis. Nat Commun. 2021;12:6602. doi: 10.1038/s41467-021-26910-8.\\u003c/li\\u003e\\n\\u003cli\\u003eFung TS, Huang M, Liu DX. Coronavirus-induced ER stress response and its involvement in regulation of coronavirus-host interactions. Virus Res. 2014:194:110\\u0026ndash;123. doi: 10.1016/j.virusres.2014.09.016.\\u003c/li\\u003e\\n\\u003cli\\u003eHetz C, Papa FR. The Unfolded Protein Response and Cell Fate Control. Mol. Cell. 2018;69:169\\u0026ndash;181. doi: 10.1016/j.molcel.2017.06.017.\\u003c/li\\u003e\\n\\u003cli\\u003eSchr\\u0026ouml;der M, Kaufman RJ. The mammalian unfolded protein response. Annu Rev Biochem. 2005;74:739\\u0026ndash;789. doi: 10.1146/annurev.biochem.73.011303.074134. \\u003c/li\\u003e\\n\\u003cli\\u003eEchavarr\\u0026iacute;a-Consuegra L, et al. Manipulation of the unfolded protein response: A pharmacological strategy against coronavirus infection. 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Cell Physiol Biochem. 2017;43:1337\\u0026ndash;1345. doi: 10.1159/000481845.\\u003c/li\\u003e\\n\\u003cli\\u003eLu M, et al. Opposing unfolded-protein-response signals converge on death receptor 5 to control apoptosis. Science. 2014;345:98\\u0026ndash;101. doi: 10.1126/science.1254312. \\u003c/li\\u003e\\n\\u003cli\\u003eShen J, Chen X, Hendershot L, and Prywes R. ER Stress Regulation of ATF6 Localization by Dissociation of BiP/GRP78 Binding and Unmasking of Golgi Localization Signals. Dev Cell. 2002;3:99\\u0026ndash;111. doi: 10.1016/s1534-5807(02)00203-4.\\u003c/li\\u003e\\n\\u003cli\\u003eLee K, et al. IRE1-mediated unconventional mRNA splicing and S2P-mediated ATF6 cleavage merge to regulate XBP1 in signaling the unfolded protein response. Genes Dev. 2002;16:452\\u0026ndash;466. doi: 10.1101/gad.964702.\\u003c/li\\u003e\\n\\u003cli\\u003eXue M, Feng L. The Role of Unfolded Protein Response in Coronavirus Infection and Its Implications for Drug Design. Front Microbiol. 2021;12:808593. doi: 10.3389/fmicb.2021.808593.\\u003c/li\\u003e\\n\\u003cli\\u003eMarciniak SJ, et al. CHOP induces death by promoting protein synthesis and oxidation in the stressed endoplasmic reticulum. Genes Dev. 2004;18:3066\\u0026ndash;3077. doi: 10.1101/gad.1250704. \\u003c/li\\u003e\\n\\u003cli\\u003eOhoka N, et al. TRB3, a novel ER stress-inducible gene, is induced via ATF4-CHOP pathway and is involved in cell death. EMBO J. 2005;24:1243\\u0026ndash;1255. doi: 10.1038/sj.emboj.7600596. \\u003c/li\\u003e\\n\\u003cli\\u003eHu H, Tian M, Ding C, Yu S. The C/EBP Homologous Protein (CHOP) Transcription Factor Functions in Endoplasmic Reticulum Stress-Induced Apoptosis and Microbial Infection. Front Immunol. 2019:9:3083. doi: 10.3389/fimmu.2018.03083.\\u003c/li\\u003e\\n\\u003cli\\u003eShaver CM, et al. Cell-free hemoglobin: a novel mediator of acute lung injury. Am J Physiol Lung Cell Mol Physiol. 2016;310:L532\\u0026ndash;541. doi: 10.1152/ajplung.00155.2015.\\u003c/li\\u003e\\n\\u003cli\\u003eNielsen MJ, M\\u0026oslash;ller HJ, Moestrup SK. Hemoglobin and heme scavenger receptors. Antioxid Redox Signal. 2010;12:261\\u0026ndash;273. doi: 10.1089/ars.2009.2792.\\u003c/li\\u003e\\n\\u003cli\\u003eMumby S, Ramakrishnan L, Evans TW, Griffiths MJD, Quinlan GJ. Methemoglobin-induced signaling and chemokine responses in human alveolar epithelial cells. Am J Physiol Lung Cell Mol Physiol. 2014;306:L88\\u0026ndash;100. doi: 10.1152/ajplung.00066.2013.\\u003c/li\\u003e\\n\\u003cli\\u003eKristiansen M, et al. Identification of the haemoglobin scavenger receptor. Nature. 2001;409:198\\u0026ndash;201. doi: 10.1038/35051594. \\u003c/li\\u003e\\n\\u003cli\\u003eHvidberg V, et al. Identification of the receptor scavenging hemopexin-heme complexes. Blood. 2005;106:2572\\u0026ndash;2579. doi: 10.1182/blood-2005-03-1185.\\u003c/li\\u003e\\n\\u003cli\\u003eBuechler C, et al. Regulation of scavenger receptor CD163 expression in human monocytes and macrophages by pro- and antiinflammatory stimuli. J Leukoc Biol. 2000;67:97\\u0026ndash;103.\\u003c/li\\u003e\\n\\u003cli\\u003eSulahian TH, et al. Human monocytes express CD163, which is upregulated by IL-10 and identical to p155. Cytokine. 2000;12:1312\\u0026ndash;1321.\\u003c/li\\u003e\\n\\u003cli\\u003eKubo M, Motomura Y. Transcriptional regulation of the anti-inflammatory cytokine IL-10 in acquired immune cells. Front Immunol. 2012;3:275. doi: 10.3389/fimmu.2012.00275.\\u003c/li\\u003e\\n\\u003cli\\u003eKhanmohammadi S, Rezaei N. Role of Toll-like receptors in the pathogenesis of COVID-19. J Med Virol. 2021;93:2735\\u0026ndash;2739. doi: 10.1002/jmv.26826.\\u003c/li\\u003e\\n\\u003cli\\u003eTodorović-Raković N, Whitfield JR. 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Combining IL-6 and SARS-CoV-2 RNAaemia-based risk stratification for fatal outcomes of COVID-19. PLoS One. 2021;16:e0256022. doi: 10.1371/journal.pone.0256022.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"ARDS, Transcriptome, biological heterogeneity, interferon, COVID-19 \",\"lastPublishedDoi\":\"10.21203/rs.3.rs-3908055/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-3908055/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eCOVID-19 is a major etiology of acute respiratory distress syndrome (ARDS). The biological phenotypes and underlying mechanisms in COVID-19-induced ARDS are not fully understood. Bronchoalveolar lavage fluid (BALF) cells and clinical data were collected from patients with COVID-19-induced ARDS. Principal component analysis of genome-wide expression data obtained from bulk RNA sequencing of BALF cells subgrouped COVID-19-induced ARDS patients. Moreover, comparing transcriptome profiles between the subgroups showed two biological phenotypes, illustrated by up- and down-regulation of interferon (IFN) responses, despite no significant differences in clinical characteristics including onset and outcomes. In the low-IFN phenotype, in contrast to the high-IFN phenotype, the TLR-MyD88-IFN regulatory factor (IRF) 5 and cGAS-STING1 axes related to type Ⅰ IFN and the IRF8-interleukin (IL)-12-STAT4 and IRF1-IL-15-DNAX-activation protein 10 axes related to type Ⅱ IFN were inactivated at the transcriptional level, together with the PERK-C/EBP homologous protein axis and the IL-10-hemoglobin scavenger receptor CD163 axis. The pathogenesis of ARDS in the low-IFN phenotype was illustrated by damage to type II alveolar epithelial cells due to increased viral replication by reduced antiviral response, cytotoxicity, and apoptotic signaling and impaired free hemoglobin catabolism. Our data uncovered heterogeneous IFN responses, the underlying mechanisms, and related pathogenesis in COVID-19-induced ARDS.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Transcriptome Heterogeneity in COVID-19-induced Acute Respiratory Distress Syndrome\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-02-07 20:18:21\",\"doi\":\"10.21203/rs.3.rs-3908055/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"30fdcf69-1440-4b1d-ab08-fe3aa44a4315\",\"owner\":[],\"postedDate\":\"February 7th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":28625234,\"name\":\"Biological sciences/Computational biology and bioinformatics\"},{\"id\":28625235,\"name\":\"Biological sciences/Immunology\"}],\"tags\":[],\"updatedAt\":\"2024-03-12T08:07:31+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-02-07 20:18:21\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-3908055\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-3908055\",\"identity\":\"rs-3908055\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}