key: cord-1028322-06gbt9t0 authors: Lucas, C.; Wong, P.; Klein, J.; Castro, T.; Silva, J.; Sundaram, M.; Ellingson, M.; Mao, T.; Oh, J.; Israelow, B.; Tokuyama, M.; Lu, P.; Venkataraman, A.; Park, A.; Mohanty, S.; Wang, H.; Wyllie, A. L.; Vogels, C. B. F.; Earnest, R.; Lapidus, S.; Ott, I.; Moore, A.; Muenker, C.; Fournier, J.; Campbell, M.; Odio, C.; Casanovas-Massana, A.; Yale IMPACT Team,; Herbst, R.; Shaw, A.; Medzhitov, R.; Schulz, W. L.; Grubaugh, N.; Dela Cruz, C.; Farhadian, S.; Ko, A.; Omer, S.; Iwasaki, A. title: Longitudinal immunological analyses reveal inflammatory misfiring in severe COVID-19 patients date: 2020-06-24 journal: nan DOI: 10.1101/2020.06.23.20138289 sha: e00c976a11d9cf8d03bb70c476666eace1e36cf9 doc_id: 1028322 cord_uid: 06gbt9t0 Recent studies have provided insights into the pathogenesis of coronavirus disease 2019 (COVID-19)1-4. Yet, longitudinal immunological correlates of disease outcome remain unclear. Here, we serially analysed immune responses in 113 COVID-19 patients with moderate (non-ICU) and severe (ICU) disease. Immune profiling revealed an overall increase in innate cell lineages with a concomitant reduction in T cell number. We identify an association between early, elevated cytokines and worse disease outcomes. Following an early increase in cytokines, COVID-19 patients with moderate disease displayed a progressive reduction in type-1 (antiviral) and type-3 (antifungal) responses. In contrast, patients with severe disease maintained these elevated responses throughout the course of disease. Moreover, severe disease was accompanied by an increase in multiple type 2 (anti-helminths) effectors including, IL-5, IL-13, IgE and eosinophils. Unsupervised clustering analysis of plasma and peripheral blood leukocyte data identified 4 immune signatures, representing (A) growth factors, (B) type-2/3 cytokines, (C) mixed type-1/2/3 cytokines, and (D) chemokines that correlated with three distinct disease trajectories of patients. The immune profile of patients who recovered with moderate disease was enriched in tissue reparative growth factor signature (A), while the profile for those with worsened disease trajectory had elevated levels of all four signatures. Thus, we identified development of a maladapted immune response profile associated with severe COVID-19 outcome and early immune signatures that correlate with divergent disease trajectories. four signatures. Thus, we identified development of a maladapted immune response profile associated with severe COVID-19 outcome and early immune signatures that correlate with divergent disease trajectories. Coronavirus Disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a highly infectious, zoonotic virus that exploits angiotensin-converting enzyme 2 (ACE2) 5,6 as a cell entry receptor. SARS-CoV-2 has generated an ongoing global pandemic that reaches almost every country, linked to hundreds of thousands of deaths in less than 6 months. Clinical presentation of COVID-19 involves a broad range of symptoms comprising fever, fatigue, diarrhoea, conjunctivitis and myalgia, in addition to the respiratory-specific symptoms including dry cough, shortness of breath and viral pneumonia 3, 7, 8 . In severe cases, patients develop critical disease characterized by pneumonia that may be exacerbated by pulmonary edema, respiratory failure, cardiac damage, pulmonary emboli, thromboses, systemic shock, and multi-organ failure 3, 9 . Understanding the nature of the immune response that leads to recovery over severe disease is key to developing effective treatment against COVID-19. Respiratory Syndrome (MERS), typically induce strong inflammatory responses and associated lymphopenia 10, 11 . Initial studies characterizing immune cells in the peripheral blood of COVID-19 patients have reported increases in inflammatory monocytes and neutrophils and a sharp decrease in lymphocytes [1] [2] [3] [4] . Moreover, in parallel with observations of infections with MERS and SARS, an inflammatory milieu containing IL-1β, IL-6, and TNF-α has been associated with worse disease outcomes 1, 2, 4, 8, 12 . Despite these analyses, immune response dynamics during the course of SARS-CoV-2 infection and its possible correlation with clinical trajectory remain unknown. IMPACT study, whose samples served as healthy controls (SARS-CoV-2 negative by RT-qPCR and serology). Basic demographic information stratified by disease severity is displayed in Extended Table 1 . Hospitalized patients were stratified into moderate and severe based on oxygen levels and intensive care unit (ICU) requirement (Fig.1a) . Among our cohort, patients who developed moderate or severe disease did not significantly differ with respect to age or sex. Body mass index (BMI) was generally increased among patients with severe disease, and extremes in BMI correlated with an increased relative risk of mortality (RR BMI Table 1 , Extended data Fig. 1a ,b). Exposure to select therapeutic regimens of interest was assessed in both moderate and severe disease severities (Extended Data Fig 1c) . Initial presenting symptoms demonstrated a preponderance of headache (54.55%), fever (64.47%), cough (74.03%), and dyspnoea (67.09%) with no significant difference in symptom presentation between moderate or those that eventually developed severe disease. As reported elsewhere, blunting of taste (hypogeusia) and smell (anosmia) were occasionally reported in both moderate and severe disease but demonstrated no significant difference between groups 16 . Finally, mortality was significantly increased in patients who were admitted to the ICU over those who were not ( We analysed PBMC and plasma samples from moderate and severe COVID-19 patients and healthy HCW donors (Fig. 1a) by flow cytometry and ELISA to quantify leukocytes and soluble mediators, respectively. An unsupervised heatmap was constructed from the main innate and adaptive circulating immune cell types; this analysis revealed marked changes in COVID-19 patients compared to uninfected HCW (Fig. 1b) . As recently reported [1] [2] [3] [4] , COVID-19 patients presented with marked reductions in T cell number and frequency in both CD4 + and CD8 + T cells, even after normalization for age as a possible confounder (Extended Data Fig.1d ). Granulocytes such as neutrophils and eosinophils are normally excluded from the PBMC fraction following density gradient separation. However, low density granulocytes are present in the PBMC layer from peripheral blood collections in patients with inflammatory diseases 17 . We observed an increases in monocytes, and low density neutrophils and eosinophils that correlated with the severity of disease (Fig. 2c , Extended Data Fig. 2a,b) . Additionally, we observed increased activation of T cells and a reduction in HLA-DR expression by circulating monocytes 1 (Extended Data Fig. 2c) . A complete overview of PBMC cells subsets is presented in Extended Data Fig. 2 . Severe disease caused by infections with MERS, SARS-CoV-1, and SARS-CoV-2 coronaviruses are associated with cytokine release syndrome (CRS) 1, 2, 8, 11, 12, 18, 19 . To gain insights into key differences in cytokines, chemokines, and additional immune markers between moderate and severe patients, we correlated the measurements of these soluble proteins across all patients' time-points that were collected ( Fig. 1d) . We observed a "core COVID-19 signature" shared by both moderate and severe groups of patients defined by the following inflammatory cytokines that positively correlated with each other; these include:IL-1α, IL-1β, IL-17A, IL-12 p70, and IFN-α (Fig. 1d) . In severe patients, we observed an additional inflammatory cluster defined by: TPO, IL-33, IL-16, IL-21, IL-23, IFN-λ, eotaxin and eotaxin 3 (Fig. 1d) . Most of the cytokines linked to CRS, such as IL-1α, IL-1β, IL-6, IL-10, IL-18 and TNF-α, showed increased positive associations in severe patients (Fig. 1d , e, f and Extended Data Fig. S3 ). These data highlight the broad inflammatory changes, involving concomitant release of type-1, type-2 and type-3 cytokines in severe COVID-19 patients. Our data presented above, as well as previous single-cell transcriptome and flow cytometry-based studies 2,4,19-21 , depicted an overt innate and adaptive immune activation in severe COVID-19 patients. We sought to determine the temporal dynamics of immune changes through longitudinal sample collection and analysis of key immunological markers in moderate versus severe patients. Longitudinal cytokines correlations, measured as days from symptom onset (DfSO), indicated major differences in immune phenotype between moderate and severe disease apparent after day 10 of infection ( Fig. 2a) . In the first 10 DfSo, severe and moderate patients displayed similar correlation intensity and markers, including the overall "core COVID-19 signature" described above (Fig. 2a) . However, after day 10 these markers steadily declined in patients with moderate disease; in contrast, severe patients maintained elevated levels of these core signature makers. Notably, additional correlations between cytokines emerged in patients with severe disease following day 10 (Fig. 2a) . These analyses strongly support the observation in the overall analysis described in Fig. 1 , in which TPO and IFN-α strongly associated with IFN-λ, IL-9, IL-18, IL-21, IL-23, and IL-33 (Fig. 2a) . These observations indicate sharp differences in the expression of inflammatory markers along disease progression between patients who exhibit moderate vs. severe COVID-19 symptoms. Temporal analyses of PBMC and soluble proteins in plasma, either by linear regression or grouped intervals, supported distinct courses in disease. IFN-α levels were sustained at higher levels in sever patients while they declined moderate patients (Fig. 2b) . Plasma IFN-λ levels increased during the first week of symptom onset in ICU patients and remained elevated in later phases (Fig. 2b) . Additionally, inflammasome-induced cytokines, such as IL-1β and IL-18 were also elevated in severe patients compared to patients with moderate disease at most time-points analysed (Fig. 2c) . Consistently, IL-1 receptor antagonist (IL-1Ra), induced by IL-1R signalling as a negative feedback regulator 22 , also showed increased levels in ICU patients from day 10 of disease onset (Extended Data Fig. 4 ). With respect to type-1 immunity, an increased number of monocytes was observed at approximately 14 DfSo in severe but not in moderate COVID-19 patients (Fig. 2d) . The innate cytokine IL-12, a key inducer of type-1 immunity 13, 14 , displayed a similar pattern to IFN-γ; increasing over time in severe patients but steadily declining in moderate patients (Fig. 2d) . IFN-γ can be secreted by ILC1, NK, and Th1 cells. By intracellular cytokine staining, CD4 + and CD8 + T cells from patients with moderate disease secreted comparable amounts of IFN-γ to those from severe patients. Together with the severe T cell depletion in severe patients ( Fig. 1) , our data suggested that secretion of IFN-γ by non-T cells (ILC1, NK), or non-circulating T cells in tissues were the primary contributors to the enhanced levels observed in severe patients (Extended Data Fig. 5 ). Type-2 immune markers continued to increase in severe patients over time, as indicated by strong correlations observed in late time points from severe patients (Fig. 2a) . Eosinophils and eotaxin-2 increased in severe patients and remained higher than levels measured in moderate patients (Fig. 2e ). Type-2 innate immune cytokines, including TSLP and IL-33, did not exhibit significant differences between severe and moderate patients (Fig. 2e) . Hallmark type-2 cytokines, including IL-5 (associated with eosinophilia) and IL-13 ( Fig. 2e) , were enhanced in patients with severe over moderate disease. In contrast, IL-4, was not significantly different. However, IL-4, similar to IL-5 and IL-13, exhibited an upward trend over the course of disease in severe patients (Fig. 2e) . A type-2 antibody isotype was also increased; IgE levels were significantly higher in severe patients and continued to increase during the disease course (Fig. 2e ). IL-6 linked to CRS was significantly elevated in severe patients, although circulating neutrophils did not show a significant increase in our longitudinal analysis (Fig. 2f) . Hallmarks of type-3 responses were observed in severe patients, including increased plasma IL-17A and IL-22, as well as IL-17 secretion by circulating CD4 T cells as assessed by intracellular cytokine staining (Fig. 2f, Extended Data Fig. 5 ). These data identify broad elevation of type-1, type-2 and type-3 signatures in severe cases of COVID-19, with distinct temporal dynamics and quantities between severe and moderate patients. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted June 24, 2020. . https://doi.org/10.1101/2020.06. 23.20138289 doi: medRxiv preprint We next asked which early immunological profile was correlated with worse disease trajectory and whether these parameters were influenced by the viral load. We first measured viral load kinetics by serial nasopharyngeal swabs. While viral RNA load was not significantly different at any specific time point analysed post symptom onset between severe and moderate patients, moderate patients showed a steady decline in viral load over the course of disease, and severe patients did not (Fig. 3a) . Regardless of whether the patients exhibited moderate or severe disease, viral load significantly correlated with the levels of IFN-α, IFN-γ, TNF-α and TRAIL (Fig. 3b) . Additionally, several chemokines responsible for monocyte recruitment significantly correlated with viral load only in patients with severe disease (Extended Data Fig. 6a ,b). These data indicated that nasopharyngeal viral load positively correlates with plasma levels of interferons and cytokines. Next, we examined whether specific early cytokine responses are associated with severe COVID-19. To this end, we conducted an unsupervised clustering analysis using patients' baseline measurements, Cluster 1 was comprised primarily of patients with moderate disease who experienced low occurrences of coagulopathy, shorter lengths of hospital stay, and no mortality (Fig. 3c, d) . The main characteristics in this cluster were low levels of inflammatory markers and similar or increased levels of parameters in signature "A" containing tissue reparative growth factors (Fig. 3c) . Clusters 2 and 3 were characterized by the rise in inflammatory markers, and patients belonging to these clusters had higher incidence of coagulopathy and mortality, which was more pronounced in cluster 3 (Fig. 3c,d) . Cluster 2 showed higher levels of markers in signatures "C and D", which included IFN-α, IL-1Ra and several hallmark type-1, type-2 and type-3 cytokines, than patients in cluster 1, but lower expression of markers in signatures "B, C and D" than in Cluster 3 ( Fig. 3c,d) . Cluster 3 displayed heightened expression of markers in signatures "B, C and D" than other clusters. Cluster 3 showed particular enrichment in expression of markers in signature "B", which include several innate cytokines including IFN-λ, TGF-α, TSLP, IL-16, IL-23 and IL-33, and markers linked to coagulopathy, such as TPO (Fig. 3c, d) . We next ranked these parameters obtained at early time points as predictors of severe disease outcomes (Fig. 3e, Extended Data Fig. 6c ). These analyses identify specific immunological markers that appear early in the disease that strongly correlate with worse outcomes and death. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted June 24, 2020. Clusters 2 and 3 were driven by a set of inflammatory markers falling into signatures B', C' and D' to some extent, which highly overlap with the "core signature" cytokines and chemokines identified in Fig. 1 as well as the signatures "B and C" identified in Fig. 3c . These include type-1 immunity markers, including IL-12, chemokines linked to monocyte recruitment and IFN-γ, type-2 responses, such as TSLP, chemokines linked to eosinophil recruitment, IL-4, IL-5 and IL-13, and type-3 responses, including IL-23, IL-17A and IL-22. Additionally, most CRS and inflammasome-associated cytokines were enriched in these clusters, including IL-1α, IL-1β, IL-6, IL-18 and TNF-α (Fig. 4a) . These findings were consistent with generalized estimating equations that identified relationships between biomarkers death over time (Extended Data Fig. 8 ). Together, these results identify groups of inflammatory, as well as potentially protective, markers that correlated with COVID-19 trajectory. The immune signatures that correlate with recovery (cluster 1) and worsening diseases (cluster 2 < cluster 3) were remarkably similar whether we took prospective (Fig. 3) vs. retrospective (Fig. 4) approaches. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted June 24, 2020. . https://doi.org/10.1101/2020.06.23.20138289 doi: medRxiv preprint Our longitudinal analyses of hospitalized COVID-19 patients revealed key temporal features of viral load and immune responses that distinguish disease trajectories during hospitalization. Using patients' immune response readouts, unsupervised clustering revealed 3 distinct profiles that influence the evolution and severity of COVID-19. Cluster 1, characterized by low expression of proinflammatory cytokines and enrichment in tissue repair genes, followed a disease trajectory that remained moderate leading to eventual recovery. Clusters 2 and 3 were characterized with highly elevated proinflammatory cytokines (cluster 3 being more intense), developed worse disease and many died of COVID-19. Thus, in addition to the well-appreciated CRS-related pro-inflammatory cytokines, we propose four signatures of immune response profiles that more accurately subset patients into distinct COVID-19 disease course. In addition to dynamic changes in leukocyte subsets, such as prolonged lymphopenia and elevation of monocytes and neutrophils in severe disease, we observed an increase in eosinophils in patients admitted to the ICU. By focusing on hallmark cytokines of type 1, type 2 and type 3 immunity, we found that while all are higher in severe disease than moderate disease, type 2 signatures, including eotaxin 2, IgE, eosinophils, IL-5 and IL-13, continue to become elevated during disease course in the severe patients. While nasopharyngeal viral RNA levels were not significantly different between moderate and severe patients at the specific time points examine post symptom onset, linear regression analyses showed slower decline in viral load in patients admitted to the ICU. Viral load was highly correlated with IFN-α, IFN-γ and TNF-α, suggesting that viral load may drive these cytokines, and that interferons do not successfully control the virus. Moreover, many interferons, cytokines, and chemokines were elevated early in disease for patients who ultimately died of COVID-19. This suggests possible pathological roles associated with these host defence factors. Our comprehensive analysis of soluble plasma factors revealed a broad misfiring of immune effectors in COVID-19 patients, with early predictive markers, distinct dynamics between types of immune . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted June 24, 2020. . https://doi.org/10.1101/2020.06.23.20138289 doi: medRxiv preprint responses, among moderate and severe disease outcomes. Unsupervised clustering identified a set of inflammatory markers highly enriched in severe patients. By following these patients over time ( Figure 2 ), we observed a mixed response of type-1, -2 and -3-associated cytokines. A preprint study assessing inflammatory cytokines increased in COVID-19 in comparison to influenza patients only observed heightened IL-6 and IL-8 (Ref. 24 ); the latter was not enriched in clusters 2 or 3 in our analysis. Interestingly, this study also observed high circulating levels of IL-1Ra in severe patients, which we also identified. We likewise observed that IL-6 may be an important clinical target in COVID-19. It was both highly enriched in patients with severe disease in clusters 2 and 3. In fact, all of our ICU patients, including the ones who succumbed to the disease, received Tocilizumab, an IL-6R blocking antibody, according to a protocol uniquely instituted at Yale New Haven Hospital COVID-19. Recent studies reported positive outcomes with this treatment, including a reduction in an inflammatory-monocyte population associated with worse outcomes 25 . This highlights the need for combination therapy to block other cytokines highly represented by these clusters, including inflammasome-dependent cytokines and type-2 cytokines. Our data, suggest a pathological role for type-2 immune responses to COVID-19 (Ref. 26 ). Previous studies have proposed a pathogenic role for type-2 immunity in influenza, as well as SARS-CoV infection 27, 28 . In addition to key cytokines associated with type-2 immunity, including IL-5 and IL-13, we observed an early and persistent increase in IgE levels in severe COVID-19 patients. Interestingly, we also observed transient increases in circulating eosinophil levels -paralleled by simultaneous elevations in Type II cytokines -among severe COVID-19 patients despite widespread use of glucocorticoids among this population (Extended Data Fig. 1c ). Given the potent eosinopenia induced by glucocorticoids, further study exploring the extent of margination of Type II effector cells into COVID-19 affected tissues, and their role in disease progression, is warranted 29 . A retrospective cohort study, also deposited as preprint, reported positive clinical outcomes upon administration of famotidine, a histamine antagonist, to COVID-19 patients 30 . Similarly, a preprint publication analysing regulatory networks downstream of ACE2 and . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted June 24, 2020. . the spike protein activator, TMPRSS2, which is part of the mucus secretory pathway, speculated that upregulation of type-2 immunity and IFN-responses, could increase ACE2 expression thus aggravating disease 31, 32 . Another notable observation from this study is the overwhelming association between antiviral IFNs and disease. While type I and III IFNs are generally required for viral clearance, during persistent viral infection, or under high viral loads, conditions generally observed in elderly patients, IFN responses can lead to pathological outcomes 33 . Our study found that IFN-α is elevated in patients with severe disease, and higher levels correlate with death. The correlation between increased viral load and increased secretion of type I IFN suggest that persistence of virus drives type I IFN secretion, but the prolonged secretion and failure to control virus instead leads to a proinflammatory cytokine storm. This vicious cycle can lead to the worsening of disease symptoms, ventilator use and ultimately death. In addition to CRS-associated cytokines, we observed a correlation with cytokines linked to the inflammasome pathway, which partially overlap with CRS, including IL-1β and IL-18. Indeed, it is plausible that inflammasome activation , along with a sepsis-like CRS, triggers vascular insults or tissue pathology observed in severe COVID-19 patients 34 . The immune dysfunction observed in severe COVID-19 patients included hallmarks of type-3 cytokines, IL-17 and IL-22. The primary function of IL-17 function is tissue neutrophil recruitment, which is associated with COVID-19 severity. Additionally, IL-17 is known to enhance the production of inflammatory cytokines associated with COVID-19, as well as lung pathology, including TNF-α, IL-1β and IL-6 35 . Our results suggest that a multi-faceted inflammatory response is associated with late COVID-19 severity. This raises the possibility that early immunological interventions that target inflammatory markers predictive of worse disease outcome are preferred to blocking late-appearing cytokines. Overall, our analyses provide a comprehensive examination of the diverse inflammatory dynamics during COVID-19 and possible contributions by . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted June 24, 2020. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted June 24, 2020. . Longitudinal data was also plotted over time continuously according to days following symptom onset. is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted June 24, 2020. . https://doi.org/10.1101/2020.06.23.20138289 doi: medRxiv preprint inhibition of IL-6Rα-mediated degradation. Analysis of our cohort indicate higher plasma levels of IL-6 in both moderate and severe patients that received tocilizumab treatment (Extended data Fig. 1d ). For all patients, days from symptom onset were estimated according to the following scheme: (1) highest priority was given explicit onset dates provided by patients; (2) next highest priority was given to the earliest reported symptom by a patient, and (3) in the absence of direct information regarding symptom onset, we estimated a date through manual assessment of the electronic medical record (EMRs) by an independent clinician. Demographic information was aggregated through a systematic and retrospective review of patient EMRs and was used to construct Extended Table 1 . Symptom onset and etiology was recorded through standardized interview with patients or patient surrogates upon enrollment in our study, or alternatively through manual EMR review if no interview was possible due to clinical status. RNA concentrations were measured from nasopharyngeal samples by RT-qPCR as previously described 36 . Briefly, total nucleic acid was extracted from 300 μ l of viral transport media (nasopharyngeal swab) using the MagMAX Viral/Pathogen Nucleic Acid Isolation kit (ThermoFisher Scientific) using a modified protocol and eluted into 75 μ l of elution buffer. For SARS-CoV-2 RNA detection, 5 μ l of RNA 371 template was tested as previously described 37 , using the US CDC real-time RT-qPCR primer/probe sets for 2019-nCoV_N1, 2019-nCoV_N2, and the human RNase P (RP) as an extraction control. Virus RNA copies were quantified using a 10-fold dilution standard curve of RNA transcripts that we previously generated 37 . The lower limit of detection for SARS-CoV-2 genomes assayed by qPCR in nasopharyngeal specimens was established as recently described 37 . In addition to a technical detection threshold, we also utilized a clinical referral threshold (detection limit) to either: (1) refer asymptomatic HCWs for diagnostic testing at a CLIA-approved laboratory, or (2) cross-validate results from a CLIA-approved laboratory for SARS-CoV-2 qPCR+ individuals upon study . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted June 24, 2020. . https://doi.org/10.1101/2020.06.23.20138289 doi: medRxiv preprint enrollment. Individuals above the technical detection threshold, but below the clinical referral threshold, are considered SARS-CoV-2 positive for the purposes of our research study. Plasma samples were collected after whole blood centrifugation at 400 g for 10 minutes at RT without brake. The undiluted serum was then transferred to 15 ml polypropylene conical tubes, and aliquoted and stored at -80 °C for subsequent analysis. Patient serum was isolated as before and aliquots were stored in -80°C. Sera were shipped to Eve Technologies (Calgary, Alberta, Canada) on dry ice, and levels of cytokines and chemokines were measured with Human Cytokine Array/Chemokine Array 71-403 Plex Panel (HD71). All the samples were measured upon the first thaw. Peripheral blood mononuclear cells (PBMCs) were isolated from heparinized whole blood using Histopaque (Sigma-Aldrich, #10771-500ML) density gradient centrifugation in a biosafety level 2+ facility. After isolation of undiluted serum, blood was 1:1 diluted in room temperature PBS and layered over Histopaque in a SepMate tube (Stemcell Technologies; #85460) and centrifuged for 10 minutes at 1200g. The PBMC layer was isolated according to manufacturer's instructions. Cells were washed twice with PBS prior to counting. Pelleted cells were briefly treated with ACK lysis buffer for 2 minutes and then counted. Percentage viability was estimated using standard Trypan blue staining and an automated cell counter (Thermo-Fisher, #AMQAX1000). . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted June 24, 2020. Table 8 . The patients and its measurements were clustered using the kmeans algorithm available within the ComplexHeatmap package 38 calculated the incidence risk ratio (IRR) in the same way as for non-GEE GLM models, assuming an independent correlation structure. All models controlled for participant sex and age. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted June 24, 2020. The authors declare no competing financial interests. The data that support the findings of this study will be made openly available in public repositories. Extended Table1: Basic Demographics for COVID-19 Cohort. Unless otherwise noted, listed relative risks for mortality were not statistically significant. Moderate (Clinical Score1 3) and severe (Clinical Score 4 5) disease status were assigned as described in Methods. Percentages of sub group (moderate or severe) are shown for each category with respective counts in parenthesis. Average age was calculated with accompanying sample standard deviation. Ethnicity and BMI were extracted from most recent electronic medical record (EMR) data. Select COVID 19 Risk Factors were scored by a clinical infectious disease physician. Presenting symptoms were recorded primarily through direct interview with patient or surrogate. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted June 24, 2020. . https://doi.org/10.1101/2020.06.23.20138289 doi: medRxiv preprint Extended Data Fig.1 : Age and BMI cohort distributions and Select Medications distributions: Aggregated ages (a) and BMIs (b) were collected for moderate, severe, and fatal patients with COVID 19 and relative frequency histograms generated for comparison across disease sub-groups. Gaussian and lognormal distributions were fit through least squares regression and compared for goodness of fit through differential Akaike information criterion (AICc) comparison. All distributions were best described by a Gaussian model except for age in the "Severe" disease category, which was best modeled by a lognormal distribution. Significance of comparisons were determined by Wilcoxon Rank-Sum Test and indicated as such: * p < 0.05, ** p< 0.01, *** p < 0.001, and **** p <0.0001. Fig. 4: Longitudinal cytokines and chemokines of COVID-19 patients. (a) . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted June 24, 2020. N a2 IL 3 F LT 3L IL 5 IL 27 T S LP IL 23 IL 16 IL 21 IL 9 C C L3 S C F VEGFA EGF PDGFAA sCD40L PDGFABAndPDGFBB CXCL1 IL7 CCL4 IL8 TGFa Eotaxin2 CCL17 CXCL5 IL17F TPO IL33 SDF1aAndSDF1B IL20 LIF IFNL IL18 CCL5 IgE CCL27 CCL22 CXCL13 TRAIL CCL15 IL1RA CXCL9 CCL2 IL6 CCL1 CXCL1 TNFa IL15 MCSF CCL8 IL10 CXCL10 CCL21 GCSF IL12p40 IL1a IL17A FGF2 IL1b IL22 IL4 IL17E/IL25 IL2 IL12p70 GMCSF IL13 TNFb IFNy CCL7 CCL13 Eotaxin Eotaxin3 IFNa2 IL3 FLT3L IL5 IL27 TSLP IL23 IL16 IL21 IL9 CCL3 SCF PDGFABAndPDGFBB CXCL1 IL7 CCL4 IL8 TGFa Eotaxin2 CCL17 CXCL5 IL17F TPO IL33 SDF1aAndSDF1B IL20 LIF IFNL IL18 CCL5 IgE CCL27 CCL22 CXCL13 TRAIL CCL15 IL1RA CXCL9 CCL2 IL6 CCL1 CX3CL1 TNFa IL15 MCSF CCL8 IL10 CXCL10 CCL21 GCSF IL12p40 IL1a IL17A FGF2 IL1b IL22 IL4 IL17E/IL25 IL2 IL12p70 GMCSF IL13 TNFb IFNy CCL7 CCL13 Eotaxin Eotaxin3 IFNa2 IL3 FLT3L IL5 IL27 TSLP IL23 IL16 IL21 IL9 CCL3 VEGFA EGF PDGFAA sCD40L PDGFABAndPDGFBB CXCL1 IL7 CCL4orMIP1b IL8 TGFa Eotaxin2 CCL17 CXCL5 IL17F TPO IL33 SDF1aAndSDF1B IL20 LIF IFNL IL18 CCL5 IgE CCL27 CCL22 CXCL13 TRAIL CCL15 IL1RA CXCL9 CCL2 IL6 CCL1 CX3CL1 TNFa IL15 MCSF CCL8 IL10 CXCL10 CCL21 GCSF IL12p40 IL1a IL17A FGF2 IL1b IL22 IL4 IL17E/IL25 IL2 IL12p70 GMCSF IL13 TNFb IFNy CCL7 CCL13 Eotaxin Eotaxin3 IFNa2 IL3 FLT3L IL5 IL27 TSLP IL23 IL16 IL21 IL9 CCL3 SCF VEGFA EGF DGFABAndPDGFBB sCD40L PDGFABA CXCL1 IL7 CCL4 IL8 TGFa Eotaxin2 CCL17 CXCL5 IL17F TPO IL33 SDF1aAndSDF1b IL20 LIF IFNL2 IL18 CCL5 IgE CCL27 CCL22 CXCL13 TRAIL CCL15 IL1RA CXCL9 CCL2 IL6 CCL1 CX3CL1 TNFa IL15 MCSF CCL8 IL10 CXCL10 CCL21 GCSF IL12p40 IL1a IL17A FGF2 IL1b IL22 IL4 IL17E/IL25 IL2 IL12p70 GMCSF IL13 TNFb IFNy CCL7 CCL13 Eotaxin Eotaxin3 IFNa2 IL3 FLT3L IL5 IL27 TSLP IL23 IL16 IL21 IL9 CCL3 SCF VEGFA EGF PDGFAA sCD40L PDGFABAndPDGFBB CXCL1 IL7 CCL4 IL8 TGFa Eotaxin2 CCL17 CXCL5 IL17F TPO IL33 SDF1aAndSDF1B IL20 LIF IFNL2 IL18 CCL5 IgE CCL27 CCL22 CXCL13 TRAIL CCL15 IL1RA CXCL9 CCL2 IL6 CCL1 CX3CR1 TNFa IL15 MCSF CCL8 IL10 CXCL10 CCL21 GCSF IL12p40 IL1a IL17A FGF2 IL1b IL22 IL4 IL17E/IL25 IL2 IL12p70 GMCSF IL13 TNFb IFNy CCL7 CCL13 Eotaxin Eotaxin3 IFNa2 IL3 FLT3L IL5 IL27 TSLP IL23 IL16 IL21 IL9 CCL3 SCF Early Late A B D E F C VEGFA EGF PDGFAA sCD40L PDGFABAndPDGFBB CXCL1 IL7 CCL4 IL8 TGFa Eotaxin2 CCL17 CXCL5 IL17F TPO IL33 SDF1aAndSDF1B IL20 LIF IFNL IL18 CCL5 IgE CCL27 CCL22 CXCL13 TRAIL CCL15 IL1RA CXCL9 CCL2 IL6 CCL1 CX3CL1 TNFa IL15 MCSF CCL8 IL10 CXCL10 CCL21 GCSF IL12p40 IL1a IL17A FGF2 IL1b IL22 IL4 IL17E/IL25 IL2 IL12p70 GMCSF IL13 TNFb IFNy CCL7 CCL13 Eotaxin Eotaxin3 IFNa2 IL3 FLT3L IL5 IL27 TSLP IL23 IL16 IL21 IL9 CCL3 SCF VEGFA EGF PDGFAA sCD40L PDGFABAndPDGFBB CXCL1 IL7 CCL4 IL8 TGFa Eotaxin2 CCL17 CXCL5 IL17F TPO IL33 SDF1aAndSDF1B IL20 LIF IFNL IL18 CCL5 IgE CCL27 CCL22 CXCL13 TRAIL CCL15 IL1RA CXCL9 CCL2 IL6 CCL1 CX3CL1 TNFa IL15 MCSF CCL8 IL10 CXCL10 CCL21 GCSF IL12p40 IL1a IL17A FGF2 IL1b IL22 IL4 IL17E/IL25 IL2 IL12p70 GMCSF IL13 TNFb IFNy CCL7 CCL13 Eotaxin Eotaxin3 IFNa2 IL3 FLT3L IL5 IL27 TSLP IL23 IL16 IL21 IL9 CXCL1 IL7 CCL4 IL8 TGFa Eotaxin2 CCL17 CXCL5 IL17F TPO IL33 SDF1aAndSDF1B IL20 LIF IFNL2 IL18 CCL5 IgE CCL27 CCL22 CXCL13 TRAIL CCL15 IL1RA CXCL9 CCL2 IL6 CCL1 CX3CL1 TNFa IL15 MCSF CCL8 IL10 CXCL10 CCL21 GCSF IL12p40 IL1a IL17A FGF2 IL1b IL22 IL4 IL17E/IL25 IL2 IL12p70 GMCSF IL13 TNFb IFNy CCL7 CCL13 Eotaxin Eotaxin3 IFNa2 IL3 FLT3L IL5 IL27 TSLP IL23 IL16 IL21 IL9 CCL3 SCF VEGFA EGF PDGFAA sCD40L PDGFABAndPDGFBB CXCL1 IL7 CCL4 IL8 TGFa Eotaxin2 CCL17 CXCL5 IL17F TPO IL33 SDF1aAndSDF1B IL20 LIF IFNL2 IL18 CCL5 IgE CCL27 CCL22 CXCL13 TRAIL CCL15 IL1RA CXCL9 CCL2 IL6 CCL1 CX3CL1 TNFa IL15 MCSF CCL8 IL10 CXCL10 CCL21 GCSF IL12p40 IL1a IL17A FGF2 IL1b IL22 IL4 IL17E/IL25 IL2 IL12p70 GMCSF IL13 TNFb IFNy CCL7 CCL13 Eotaxin Eotaxin3 IFNa2 IL3 FLT3L IL5 IL27 TSLP IL23 IL16 IL21 IL9 CCL3 SCF VEGFA EGF PDGFAA sCD40L PDGFABAndPDGFBB CXCL1 IL7 CCL4 IL8 TGFa Eotaxin2 CCL17 CXCL5 IL17F TPO IL33 SDF1aAndSDF1B IL20 LIF IFNL2 IL18 CCL5 IgE CCL27 CCL22 CXCL13 TRAIL CCL15 IL1RA CXCL9 CCL2 IL6 CCL1 CX3CL1 TNFa IL15 MCSF CCL8 IL10 CXCL10 CCL21 GCSF IL12p40 IL1a IL17A FGF2 IL1b IL22 IL4 IL17E/IL25 IL2 IL12p70 GMCSF IL13 TNFb IFNy CCL7 CCL13 Eotaxin Eotaxin3 IFNa2 IL3 FLT3L IL5 IL27 TSLP IL23 IL16 IL21 IL9 CCL3 SCF Moderate Severe Moderate Severe . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted June 24, 2020. Eotaxin CCL13 IL7 VEGFA Eotaxin2 CXCL1 CXCL5 CCL4 IL8 CCL17 Eotaxin3 LIF IL20 SCF IFNL IL16 IL21 TPO IL33 TSLP IL23 IL17F TGFa IL27 IFNy IL15 IFNa2 FLT3L FGF2 IL2 IL17A IL1b IL12p70 IL9 CCL3 IL1a IL17E/IL25 IL22 IL4 GMCSF TNFa CX3CL1 CCL7 IL13 TNFb IL5 IL3 CCL5 CCL22 CCL8 TRAIL CXCL10 CCL1 IL6 IL10 GCSF MCSF IL18 CCL2 IL1RA CXCL9 CCL27 CCL21 IL12p40 SDF1aAndSDF1B CCL15 PDGFABAndPDGFBB sCD40L PDGFAA EGF CXCL1 VEGFA IL7 IL8 CCL4 Eotaxin2 CCL17 CXCL5 CCL22 CCL5 CXCL13 IgE CCL27 CCL15 CCL8 TRAIL IL10 CXCL10 GCSF MCSF CXCL9 IL6 CCL1 CCL2 TNFa CX3CL1 IL1RA IL18 IL27 TGFa IL23 TSLP IL33 TPO IL17F Eotaxin3 CCL13 IL20 LIF SCF IL21 IFNL IL16 CCL21 SDF1aAndSDF1B Eotaxin IL9 IL22 IL4 IL17E/IL25 CCL7 IFNy TNFb IL13 GMCSF IL15 IFNa2 IL1a IL1b IL17A IL2 FGF2 CCL3 IL12p70 IL3 IL12p40 IL5 Complex Immune Dysregulation in COVID-19 Patients with Severe Respiratory Failure Heightened Innate Immune Responses in the Respiratory Tract of COVID-19 Patients Clinical features of patients infected with 2019 novel coronavirus in Wuhan Deep immune profiling of COVID-19 patients reveals patient heterogeneity and distinct immunotypes with implications for therapeutic interventions. bioRxiv SARS-CoV-2 Cell Entry Depends on ACE2 and TMPRSS2 and Is Blocked by a Clinically Proven Protease Inhibitor Structural basis for the recognition of SARS-CoV-2 by full-length human ACE2 Pathological findings of COVID-19 associated with acute respiratory distress syndrome Clinical and immunologic features in severe and moderate Coronavirus Disease Risk Factors Associated With Acute Respiratory Distress Syndrome and Death in Patients With Coronavirus Disease The Immunobiology of SARS COVID-19 cytokine storm: the interplay between inflammation and coagulation Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study The 3 major types of innate and adaptive cellmediated effector immunity Control of adaptive immunity by the innate immune system Mechanisms underlying lineage commitment and plasticity of helper CD4+ T cells Anosmia and Ageusia: Common Findings in COVID-19 Patients Low-Density Granulocytes Are a Novel Immunopathological Feature in Both Multiple Sclerosis and Neuromyelitis Optica Spectrum Disorder Cytokine release syndrome in severe COVID-19 Imbalanced Host Response to SARS-CoV-2 Drives Development of COVID-19 T-cell hyperactivation and paralysis in severe COVID-19 infection revealed by single-cell analysis. bioRxiv Immunologic perturbations in severe COVID-19/SARS-CoV-2 infection. bioRxiv IL-1 pathways in inflammation and human diseases Host responses in tissue repair and fibrosis Targeted Immunosuppression Distinguishes COVID-19 from Influenza in Moderate and Severe Disease. medRxiv Tocilizumab treatment in severe COVID-19 patients attenuates the inflammatory storm incited by monocyte centric immune interactions revealed by single-cell analysis. bioRxiv Systems-level immunomonitoring from acute to recovery phase of severe COVID-19. medRxiv Th2 predominance and CD8+ memory T cell depletion in patients with severe acute respiratory syndrome Influenza virus-specific CD4+ T helper type 2 T lymphocytes do not promote recovery from experimental virus infection Glucocorticoid-induced eosinopenia in humans can be linked to early transcriptional events Famotidine Use is Associated with Improved Clinical Outcomes in Hospitalized COVID-19 Patients: A Propensity Score Matched Retrospective Cohort Study. medRxiv Type 2 and interferon inflammation strongly regulate SARS-CoV-2 related gene expression in the airway epithelium. bioRxiv SARS-CoV-2 Receptor ACE2 Is an Interferon-Stimulated Gene in Human Airway Epithelial Cells and Is Detected in Specific Cell Subsets across Tissues Type I and Type III Interferons -Induction, Signaling, Evasion, and Application to Combat COVID-19 Inflammasomes and Pyroptosis as Therapeutic Targets for COVID-19 COVID-19: a case for inhibiting IL-17? Saliva is more sensitive for SARS-CoV-2 detection in COVID-19 patients than nasopharyngeal swabs. medRxiv Analytical sensitivity and efficiency comparisons of SARS-COV-2 qRT-PCR primer-probe sets. medRxiv Complex heatmaps reveal patterns and correlations in multidimensional genomic data We thank Melissa Linehan for technical and logistical assistance, and thank helpful discussions with Drs.