Coronavirus disease 2019 (COVID-19) is caused by the novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Confirmed cases of COVID-19 are growing rapidly with spread to 188 countries and regions (1). The number of reported mortalities worldwide is more than 435,000 predicting a case-fatality rate of approximately 3.4% (2). Based on data from other centers, COVID-19 often results in acute respiratory distress syndrome (ARDS) with the leading cause of death in COVID-19 positive (+) patients being respiratory failure with or without multiple organ dysfunction (i.e., cardiac and/or renal) (3–6). Currently, there are no specific therapies for COVID-19, and patients are provided only supportive care.
Recent reports and commentaries have suggested that the severity of COVID-19 may be due to a “cytokine storm” (7), which is the excessive or uncontrolled release of cytokines in response to a pathologic event, such as a viral infection (8). These suggestions are due to increased inflammatory cytokine levels, such as interleukin-6 (IL-6), as well as fever, cytopenia, and hyperferritinemia (4,9). Moreover, these commentaries have been accompanied by calls for the use of broad immunosuppression with steroids, IV immunoglobulin, and/or selective cytokine blockade as a therapeutic approach for COVID-19 (8,10). While patient mortality could be improved with immunosuppressive therapies, the evidence for changes in specific cytokines is incomplete, and often observed at a single timepoint with limited comparison to control groups (4,9). Additionally, as described in recent commentaries and reviews, the use of immunosuppressive therapies to treat critically ill patients, including those with ARDS, has often been challenging due to the potential to cause harm highlighting the need for rigorous data to support any proposed trials (11,12).
The overall aim of this study was to characterize the inflammatory profile of critically ill COVID-19 patients over the first 7 days of ICU stay to potentially identify therapeutic targets. Our specific objectives were 1) to determine the inflammatory analytes changing between COVID-19+ ICU patients and healthy controls; 2) to determine the inflammatory analyte differences between COVID-19+ and COVID-19 negative (−) ICU patients; and 3) to determine the changes in relevant inflammatory analytes over time in COVID-19+ ICU patients.
Study Participants and Clinical Data
This study was approved by the Western University, Human Research Ethics Board. We enrolled consecutive patients who were admitted to our level-3 academic ICU at London Health Sciences Centre-Victoria Campus (London, Ontario) and were suspected of having COVID-19 based on standard hospital screening procedures (13). We collected daily blood samples starting at admission and up to 3 days in COVID-19− patients, or up to 7 days in COVID-19+ patients. COVID-19 status was confirmed as part of standard hospital testing by detection of two SARS-CoV-2 viral genes using polymerase chain reaction (14). Patient baseline characteristics were recorded at admission and included age, sex, comorbidities, laboratory values, arterial partial pressure to inspired oxygen (P/F) ratio, and chest radiograph findings. Although ICU severity of illness scores have not been validated in COVID-19+ patients, we calculated multiple organ dysfunction score (MODS) and Sequential Organ Failure Assessment (SOFA) score for both COVID-19+ and COVID-19− patient groups to enable objective comparison of their illness severity. We also categorized both patient groups as having confirmed or suspected sepsis diagnosis using Sepsis 3.0 criteria. Clinical interventions received during the observation period were included and consisted of antibiotics, antiviral agents, systemic corticosteroids, vasoactive medications, renal replacement therapy, high-flow oxygen therapy, and mechanical ventilation (invasive and noninvasive). Final participant groups were constructed by age- and sex-matching COVID-19+ ICU patients with COVID-19− ICU patients, as well as healthy controls that had blood samples previously banked in the Translational Research Centre, London, ON, Canada) (directed by Dr. D. D. Fraser; https://translationalresearchcentre.com/) (15,16).
Standard operating procedures were used to ensure all samples were treated rapidly and equally. Blood was obtained via indwelling catheters daily in the morning and placed immediately on ice. Once transferred to a negative pressure hood, blood was centrifuged and plasma isolated, aliquoted at 250 µL and frozen at −80°C. All samples remained frozen until use and freeze/thaw cycles were avoided.
Levels of 57 inflammatory analytes were determined using multiplexed biomarker immunoassay kits according to manufacturers’ instructions (MilliporeSigma, 400 Summit Drive, Burlington, MA) or enzyme-linked immunosorbent assay (ELISA). For the former, plasma inflammatory analytes were measured using a Bio-PlexTM 200 Suspension Array system (Bio-Rad Laboratories, Hercules, CA), which used Luminex xMAPTM fluorescent bead-based technology (Luminex Corp, Austin, TX). Bioanalyte concentrations were calculated from standard curves using five-parameter logistic regression in Bio-Plex Manager 6.1 software. For the latter, plasma levels of TIMP1 (R&D Systems Duo Set #DY970-05, diluted 1:100 or 1:200), TIMP2 (R&D Systems Duo Set #DY971, diluted 1:100), and TIMP3 (R&D Systems Duo Set #DY973, diluted 1:3 or 1:4) were measured with ELISA.
Medians (interquartile ranges [IQRs]) and frequency (%) were used to report ICU patient baseline characteristics for continuous and categorical variables, respectively; continuous variables were compared using Mann-Whitney U tests (or Kruskal-Wallis tests, as appropriate), and categorical variables were compared using Fisher exact chi-square, with p < 0.05 considered statistically significant. Daily analyte concentrations were also reported as medians (IQRs), and comparisons between groups were examined using Mann-Whitney U tests. Given the number of analytes analyzed and the risk of false positives, a p value of < 0.01 was used as our standard for statistical significance. Receiver operating characteristic (ROC) curves were conducted to determine sensitivity and specificity of all continuous variables for predicting mortality. The area-under-the-curve (AUC) was calculated for each variable, and the coordinates of the curves were then analyzed to identify the cutoff values based on the highest sensitivity and specificity for predicting mortality. All analyses were conducted using SPSS version 26 (IBM Corp., Armonk, NY).
COVID-19 analyte data were visualized with a nonlinear dimensionality reduction on the full data matrix using the t-distributed stochastic nearest neighbor embedding (t-SNE) algorithm (17). t-SNE assumes that the “optimal” representation of the data lies on a manifold with complex geometry, but low dimension, embedded in the full dimensional space of the raw data. For feature selection, we pooled analyte data across 1–3 ICU days for each of the COVID-19+ and COVID-19− cohorts and normalized observations within analyte. A random forest classifier was trained on the variables to predict COVID-19 status. A random forest is a set of decision trees and, consequently, we were able to interrogate this collection of trees to identify the features that have the highest predictive value (viz., those features that frequently appear near the top of the decision tree). We limited the decision trees to a maximum depth of five levels and constrained the forest to 50 trees to avoid overfitting the small dataset. We further explored the ability to perform automated classification of COVID-19+ versus COVID-19− patients from their analyte spectra, conservatively employing only a single decision tree and limiting the maximum tree depth to three levels. We trained and tested the classifier using a five-fold cross-validation approach.
We investigated 10 COVID-19+ ICU patients (median years of age = 61.0; IQR = 54.8–67.0), 10 age- and sex-matched COVID-19− ICU patients (median years of age = 58.0; IQR = 52.5–63.0), and 10 age- and sex-matched healthy controls (median years of age = 57.5; IQR = 52.8–62.8; p = 0.686). Baseline demographic characteristics, comorbidities, laboratory values, and chest radiograph findings are reported in Table 1. COVID-19-ICU patients had significantly higher unilateral pneumonia, whereas COVID-19+ ICU patients were more likely to have bilateral pneumonia. Sepsis was confirmed by infectious pathogen identification in only 20% of COVID-19- ICU patients, while sepsis was suspected in the remaining 80%. All other reported baseline measures were nonsignificant between patients, although a mortality rate of 40% was determined for COVID-19+ ICU patients.
We measured 57 inflammatory analytes in plasma using either fluorescent bead-based multiplex technology or ELISA. Table 2 shows that 20 inflammatory analytes were significantly different between COVID-19+ ICU patients and healthy controls (the remaining 37 nonsignificant analytes are shown in Supplemental Table 1, Supplemental Digital Content 1, http://links.lww.com/CCX/A209). All significantly different analytes were elevated in COVID-19+ ICU patients relative to healthy controls except MMP2 that was decreased.
COVID-19+ and COVID-19− cohorts were then plotted in two dimensions following dimensionality reduction by stochastic neighbor embedding (Fig. 1A). The dimensionality reduction shows that the daily analyte measurements (ICU days 1–3) between the two cohorts were distinct and easily separable. To determine which analytes were most informative for COVID-19 status classification, we performed feature selection with a random forest classifier. The top six features were identified for the binary outcome of COVID-19+ versus COVID-19− in the following order: tumor necrosis factor (TNF), granzyme B, heat shock protein 70 (HSP70), interleukin-18 (IL-18), interferon-gamma-inducible protein 10 (IP-10), and elastase 2 (Fig. 1B). We then trained and tested a simple decision-tree classifier that yielded a classifier accuracy, or the ability of the analytes to predict COVID-19 status, of 98% (p < 0.001, five-fold cross-validation).
Supplemental Table 2 (Supplemental Digital Content 2, http://links.lww.com/CCX/A210) lists 17 inflammatory analytes that were significantly different between COVID-19+ and COVID-19– patients on any or all of ICU days 1–3 (the remaining 40 nonsignificant analytes for ICU days 1–3 are shown in Supplemental Table 3, Supplemental Digital Content 3, http://links.lww.com/CCX/A211). All significant analytes were elevated in COVID-19+ ICU patients relative to COVID-19– ICU patients. While many analytes were significantly different between COVID-19+ and COVID-19− patients over time, the top six analytes determined by feature classification over ICU days 1–3 are listed first, and were TNF, granzyme B, HSP70, and IL-18; IP-10 and elastase 2 were also significantly different between COVID-19+ and COVID-19− patients, but starting on ICU day 2. A time course for these six markers is shown in Figure 2 over ICU days 1–3 for COVID-19– patients and over ICU days 1–7 for COVID-19+ patients. The mean values for these six analytes remained elevated in COVID-19+ patients across all seven ICU days. The remainder of the analytes measured over time are shown in Supplemental Figure 1 (Supplemental Digital Content 4, http://links.lww.com/CCX/A212; legend, Supplemental Digital Content 5, http://links.lww.com/CCX/A213), with some analytes increasing (e.g., MMP1) and some decreasing (e.g., IFNγ and IL-1RA) over seven ICU days.
The feature matrix for day 1 COVID-19+ ICU patients was classified for mortality using a Random Forest classifier (1,000 trees) and three-fold cross-validation. As HSP70 was the leading analyte associated with COVID-19+ death, a ROC curve was then conducted to determine the sensitivity and specificity of HSP70 for predicting mortality. The AUC for HSP70 was 1.00, indicating perfect sensitivity and specificity for our 10 COVID-19+ ICU patients. Using Youden’s Index, the HSP70 cutoff value for predicting mortality was >264,380 pg/mL. Of note, with the addition of the 10 COVID-19− cases to the analysis, the AUC and the cutoff for HSP70 remained the same.
In this study, we measured 57 inflammatory analytes in plasma obtained from ICU patients, both COVID-19+ and COVID-19−, as well as age- and sex-matched healthy controls. Given the number of analytes measured, we used two complimentary methods to analyze the data, conventional population statistics and machine learning. Our data indicate the presence of a unique inflammatory profile characterized by early and sustained elevations in circulating TNF, granzyme B, HSP70, and IL-18. Circulating levels of IP-10 increased transiently on ICU days 2–3 and elastase 2 was consistently elevated on ICU days 2–7. Finally, the plasma levels of HSP70 in COVID-19+ ICU patients were associated with mortality. Despite the exploratory nature of our study, the data generated suggest that these six inflammatory analytes could be considered for further investigation as potential biomarkers and/or therapeutic targets. While changes in some potentially useful inflammatory analytes were not identified in our study, larger cohorts will be necessary to elucidate their role in the host response (i.e., IL-6 required 47 patients per cohort to reach statistical significance based on 80% power and an alpha = 0.01).
Our COVID-19+ ICU patients were similar to those reported in earlier cohorts from China (4,5), Seattle (3), and Italy (6) with respect to age, comorbidities, and clinical presentation. In contrast to COVID-19− ICU patients, and in keeping with findings from the Seattle cohort, our COVID-19+ ICU patients had a higher prevalence of bilateral pneumonia. COVID-19− ICU patients had higher illness severity scores than COVID-19+ ICU patients, although these differences were not statistically significant due to our small sample size. Given that ventilated COVID-19+ ICU patients are reported to have higher mortality (18) than comparable ARDS cohorts (19), and that MODS and SOFA scores have not been validated in COVID-19+ ICU patients, the lower median MODS and SOFA scores in these patients may not accurately represent their illness severity relative to the COVID-19− ICU patients. Indeed, mortality was 40% in our COVID-19+ ICU patients, whereas all COVID-19− ICU patients survived to discharge.
Compared with healthy controls, COVID-19+ ICU patients exhibit clinical and laboratory evidence of systemic inflammation. Increased circulating cytokine levels (e.g., TNF, IL-6, IL-8, and IL-10), together with lymphopenia (in CD4+ and CD8+ T cells), characterize the purported “cytokine storm” associated with severe COVID-19. The mediator release pattern has been compared with that seen in secondary hemophagocytic lymphohistiocytosis, a hyperinflammatory syndrome commonly triggered by viral infections, and in a small percentage of severe sepsis patients, characterized by a fulminant and fatal hypercytokinaemia with multiple organ failure. However, when compared with COVID-19− ICU patients, the COVID-19+ ICU patients exhibited a pattern of cytokine elevation that was unique from previous reports in that elevations were sustained and dominated by TNF and the serine proteases granzyme B and elastase 2. These latter findings may be of particular clinical relevance as preclinical models suggest SARS-CoV-2 entry into cells may be blocked by protease inhibitors (20).
In contrast to other studies of sepsis and ARDS, we found persistently elevated levels of circulating TNF in our COVID-19+ ICU patients, a potent acute master regulator of the proinflammatory response. TNF is typically upregulated quickly and early following exposure to an invading pathogen or to tissue damage, after which secondary mediators propagate inflammation while circulating levels of TNF quickly normalize (21,22). The persistently elevated levels of TNF in COVID-19+ ICU patients could be a potential target for anti-TNF therapy with either neutralizing antibodies or small molecule inhibitors (10).
Granzyme B is expressed specifically in the cytolytic granules of natural killer cells and cytotoxic T lymphocytes, and functions as a targeted cell death mediator traditionally considered to cause apoptosis of tumor and virally infected cells (23). Extracellular soluble granzyme B levels are elevated in autoimmune diseases and infections including the human endotoxemia model and in those with severe sepsis (24). Furthermore, granzyme B retains much of its proteolytic activity when exposed to plasma. Granzyme B can degrade several extracellular membrane components and is involved in the production, release, and/or processing of proinflammatory cytokines (25,26). Granzyme B activates IL-18 through cleavage of pro-IL-18 (27,28), which in turn promotes cellular apoptosis via induction of granzyme B. Activation of IL-18 also induces synthesis and release of the antiviral response mediator’s interferon-gamma (IFNγ). Moreover, IL-18 is known to increase adhesion molecule expression in endothelial cells, both ICAM1 and VCAM1, thereby increasing microvascular leukocyte adhesion (29).
HSP70 was elevated in our COVID-19 ICU patients and associated with mortality. As a chaperone protein that is induced in response to environmental, physical, and chemical stresses, HSP70 is usually cytoprotective by limiting the consequences of damage and by facilitating cellular recovery via caspase inhibition. Conversely, HSP70 can also exacerbate the stress response, signaling tissue destruction, and aid in immunosurveillance by transporting intracellular peptides to distant immune cells (30). Extracellular HSP70 also promotes inflammation by activating Toll-like receptors and promoting entry of granzyme B into the cells initiating cellular apoptosis (23,31). Interestingly, HSP70 is part of the receptor complex that interacts with the binding domain of the spike protein of infectious bronchitis virus, a member of the family Coronaviridae, enabling viral entry into lung and kidney cells (32).
IP-10 is an inflammatory chemokine released by monocytes and endothelial cells, which aids recruitment of activated T cells into sites of tissue inflammation. Either protecting or promoting infection, the actions of IP-10 depend on host immune status and genetic background (33). Previous studies suggest that IP-10 is protective in coronavirus-induced severe acute respiratory syndrome (SARS) (34,35), whereas others have shown improved infectious disease outcomes after blocking IP-10 with neutralizing antibodies (33).
Elastase 2 is also a serine protease that slowly increases in COVID-19+ ICU patients over 7 ICU days. Neutrophil azurophilic granules, as well as monocytes/macrophages and mast cells, contain elastase 2 (36). Upon degranulation, elastase 2 is either released into circulation or mobilized to the leukocyte plasma membranes and subsequently deposited to vascular endothelium or subendothelial spaces (37,38). In COVID-19+ ICU patients, elevated elastase 2 levels may contribute to increased pulmonary vascular permeability and injury. Elevated elastase 2 has been demonstrated under various severe inflammatory conditions and can contribute to the development of ARDS (39).
Our exploratory study has identified a unique pattern of inflammation in COVID-19+ ICU patients that could be considered for further study as biomarkers and/or therapeutic targeting (hypothesis-generating data); however, our study also has several limitations. First, we only studied critically ill patients and we cannot determine the inflammatory changes contributing to ICU admissions. Second, given the limited number of patients available for study, we used two complimentary methods to independently analyze our data and both methods arrived at similar conclusions. Third, our COVID-19 study population was relatively small; however, we still generated strongly significant data (e.g., true positives) and fulfilled an urgent need for exploratory data to focus future hypothesis-driven studies on larger cohorts. Finally, we report only mortality as a clinical outcome. Future studies with larger sample sizes can explore whether reported changes in inflammatory analytes correlate with additional clinical outcomes such as functional status in survivors.
In summary, we report sustained elevations in a unique combination of inflammatory analytes in COVID-19+ ICU patients. Our exploratory data are consistent with the slow, or absent improvement in COVID-19+ patients despite state-of-the-art ICU care, and could aid future hypothesis-driven research using larger ICU cohorts.
We thank Ms. Shannon Seney for performing multiplex measurements and the entire Lawson COVID-19 Study Team for their support (Dr. Robert Arntfield, Dr. Ian Ball, Mr. Gordon Barkwell, Ms. Tracey Bentall, Dr. Karen Bosma, Ms. Saoirse Cameron, Ms. Eileen Campbell, Mr. David Carter, Dr. Carolina Gillio-Meina, Dr. Robert Hegele, Ms. Natalya Odoardi, Mr. Maitray Patel, Dr. Ram Singh, Dr. Kelly Summers, and Ms. Sue Tereschyn). We are grateful for the enthusiastic assistance of the frontline Critical Care Nursing Staff at London Health Sciences Centre.
1. Johns Hopkins Coronavirus Resource Center: COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). 2020. Available at: https://coronavirus.jhu.edu/map.html
. Accessed June 16, 2020
2. World Health Organization: WHO Director-General's opening remarks at the media briefing on COVID-19. 2020. Available at: https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-mediabriefing-on-covid-19---3-march-2020
. Accessed June 16, 2020
3. Bhatraju PK, Ghassemieh BJ, Nichols M, et al. Covid-19 in critically ill patients in the Seattle region—case series. N Engl J Med. 2020; 382:2012–2022
4. Zhou F, Yu T, Du R, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020; 395:1054–1062
5. Wu C, Chen X, Cai Y, et al. Risk factors associated with acute respiratory distress syndrome and death in patients with coronavirus disease 2019 pneumonia in Wuhan, China. JAMA Intern Med. 2020e200994
6. Grasselli G, Zangrillo A, Zanella A, et al. Baseline characteristics and outcomes of 1591 patients infected with SARS-CoV-2 admitted to ICUs of the lombardy region, Italy. JAMA. 2020; 323:1574–1581
7. Tisoncik JR, Korth MJ, Simmons CP, et al. Into the eye of the cytokine storm. Microbiol Mol Biol Rev. 2012; 76:16–32
8. Mehta P, McAuley DF, Brown M, et al. HLH Across Speciality Collaboration, UK. COVID-19: consider cytokine storm syndromes and immunosuppression. Lancet. 2020; 395:1033–1034
9. Huang C, Wang Y, Li X, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020; 395:497–506
10. Feldmann M, Maini RN, Woody JN, et al. Trials of anti-tumour necrosis factor therapy for COVID-19 are urgently needed. Lancet. 2020; 395:1407–1409
11. Ritchie AI, Singanayagam A. Immunosuppression for hyperinflammation in COVID-19: a double-edged sword? Lancet. 2020; 395:1111
12. Mokra D, Mikolka P, Kosutova P, et al. Corticosteroids in acute lung injury: the dilemma continues. Int J Mol Sci. 2019; 20:4765
13. Evaluating and Testing Persons for Coronavirus Disease 2019 (COVID-19). Available at: https://www.cdc.gov/coronavirus/2019-nCoV/hcp/clinical-criteria.html
14. CDC 2019-Novel Coronavirus (2019-nCoV) Real-Time RT-PCR Diagnostic Panel. Available at: https://www.fda.gov/media/134922/download
15. Brisson AR, Matsui D, Rieder MJ, et al. Translational research in pediatrics: tissue sampling and biobanking. Pediatrics. 2012; 129:153–162
16. Gillio-Meina C, Cepinskas G, Cecchini EL, et al. Translational research in pediatrics II: blood collection, processing, shipping, and storage. Pediatrics. 2013; 131:754–766
17. van der Maaten L, Hinton G. Visualizing data using t-SNE. J Mach Learn Res. 2008; 9:2579–2605
18. Richardson S, Hirsch JS, Narasimhan M, et al. Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City Area. JAMA. 2020; 323:2052–2059
19. Bellani G, Laffey JG, Pham T, et al. LUNG SAFE Investigators; ESICM Trials Group. Epidemiology, patterns of care, and mortality for patients with acute respiratory distress syndrome in intensive care units in 50 countries. JAMA. 2016; 315:788–800
20. Hoffmann M, Kleine-Weber H, Schroeder S, et al. SARS-CoV-2 cell entry depends on ACE2 and TMPRSS2 and is blocked by a clinically proven protease inhibitor. Cell. 2020; 181:271–280.e8
21. Pinsky MR, Vincent JL, Deviere J, et al. Serum cytokine levels in human septic shock. Relation to multiple-system organ failure and mortality. Chest. 1993; 103:565–575
22. Abraham E, Anzueto A, Gutierrez G, et al. Double-blind randomised controlled trial of monoclonal antibody to human tumour necrosis factor in treatment of septic shock. NORASEPT II Study Group. Lancet. 1998; 351:929–933
23. Cullen SP, Brunet M, Martin SJ. Granzymes in cancer and immunity. Cell Death Differ. 2010; 17:616–623
24. Lauw FN, Simpson AJ, Hack CE, et al. Soluble granzymes are released during human endotoxemia and in patients with severe infection due to gram-negative bacteria. J Infect Dis. 2000; 182:206–213
25. Wensink AC, Hack CE, Bovenschen N. Granzymes regulate proinflammatory cytokine responses. J Immunol. 2015; 194:491–497
26. Buzza MS, Zamurs L, Sun J, et al. Extracellular matrix remodeling by human granzyme B via cleavage of vitronectin, fibronectin, and laminin. J Biol Chem. 2005; 280:23549–23558
27. Omoto Y, Yamanaka K, Tokime K, et al. Granzyme B is a novel interleukin-18 converting enzyme. J Dermatol Sci. 2010; 59:129–135
28. Akeda T, Yamanaka K, Tsuda K, et al. CD8+ T cell granzyme B activates keratinocyte endogenous IL-18. Arch Dermatol Res. 2014; 306:125–130
29. Morel JC, Park CC, Woods JM, et al. A novel role for interleukin-18 in adhesion molecule induction through NF kappa B and phosphatidylinositol (PI) 3-kinase-dependent signal transduction pathways. J Biol Chem. 2001; 276:37069–37075
30. Calderwood SK, Mambula SS, Gray PJ Jr, et al. Extracellular heat shock proteins in cell signaling. FEBS Lett. 2007; 581:3689–3694
31. Tamura Y, Torigoe T, Kutomi G, et al. New paradigm for intrinsic function of heat shock proteins as endogenous ligands in inflammation and innate immunity. Curr Mol Med. 2012; 12:1198–1206
32. Zhang Z, Yang X, Xu P, et al. Heat shock protein 70 in lung and kidney of specific-pathogen-free chickens is a receptor-associated protein that interacts with the binding domain of the spike protein of infectious bronchitis virus. Arch Virol. 2017; 162:1625–1631
33. Liu M, Guo S, Hibbert JM, et al. CXCL10/IP-10 in infectious diseases pathogenesis and potential therapeutic implications. Cytokine Growth Factor Rev. 2011; 22:121–130
34. Chen J, Subbarao K.. The Immunobiology of SARS*. Annu Rev Immunol. 2007; 25:443–472
35. Hsieh YH, Chen CW, Schmitz SF, et al. Candidate genes associated with susceptibility for SARS-coronavirus. Bull Math Biol. 2010; 72:122–132
36. Korkmaz B, Horwitz MS, Jenne DE, et al. Neutrophil elastase, proteinase 3, and cathepsin G as therapeutic targets in human diseases. Pharmacol Rev. 2010; 62:726–759
37. Young RE, Voisin MB, Wang S, et al. Role of neutrophil elastase in LTB4-induced neutrophil transmigration in vivo
assessed with a specific inhibitor and neutrophil elastase deficient mice. Br J Pharmacol. 2007; 151:628–637
38. Voisin MB, Leoni G, Woodfin A, et al. Neutrophil elastase plays a non-redundant role in remodeling the venular basement membrane and neutrophil diapedesis post-ischemia/reperfusion injury. J Pathol. 2019; 248:88–102
39. Suzuki T, Yamashita C, Zemans RL, et al. Leukocyte elastase induces lung epithelial apoptosis via a PAR-1-, NF-kappaB-, and p53-dependent pathway. Am J Respir Cell Mol Biol. 2009; 41:742–755