COVID-19 complications still present a huge burden on healthcare systems and warrant predictive risk models to triage patients and inform early intervention. Here, we profile 893 plasma proteins from 50 severe and 50 mild-moderate COVID-19 patients, and 50 healthy controls, and show that 375 proteins are differentially expressed in the plasma of severe COVID-19 patients. These differentially expressed plasma proteins are implicated in the pathogenesis of COVID-19 and present targets for candidate drugs to prevent or treat severe complications. Based on the plasma proteomics and clinical lab tests, we also report a 12-plasma protein signature and a model of seven routine clinical tests that validate in an independent cohort as early risk predictors of COVID-19 severity and patient survival. The risk predictors and candidate drugs described in our study can be used and developed for personalized management of SARS-CoV-2 infected patients.
The rapid and widespread dissemination of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has pressured healthcare systems globally. To date, there have been over 60 million individuals infected worldwide, leading to over 1.5 million deaths due to severe complications from the Coronavirus disease 2019 (COVID-19). The International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC) released its latest comprehensive report on 14 July 2021 including data from 30 January 2020 to 25 May 2021 for 442,643 individuals with laboratory-confirmed SARS-CoV-2 infections from more than 1600 sites across 61 countries. Patients were split equally between males (221,591) and females (220,390), with a median age of 60 years. The most common comorbidities at admission were hypertension (41%), smoking (35%), diabetes mellitus (28%), cardiovascular disease (17%), and obesity (12%)1. The five most common symptoms at admission were shortness of breath, cough, history of fever, fatigue, and altered consciousness or confusion. Oxygen saturation (SpO2%) less than 94% was present in 34.8% and 25.3% of the patients who were and were not on oxygen therapy at admission, respectively. Admission to intensive care or high dependency units (ICU/HDU) at some point of illness, which could be defined as severe COVID-19, was reported for 70,476 (15.9%) patients with an estimated case-fatality ratio of 37.9%; the overall estimated case-fatality ratio is 24.9%1.
Several studies reported symptoms and comorbidities associated with severe COVID-19 complications; however, early prognostic tools to stratify the risk of developing complications are imperative. In this study, we hypothesized that changes in plasma proteins offer prognostic molecular profiles and can help identify the most informative clinical features presented at admission, which can predict the risk of developing complications. To address this, we used proteomic panel-profiling of plasma from patients with severe complications versus mild-moderate symptoms and control subjects to characterize biological processes and pathways associated with disease pathogenesis and severity. Then we evaluated the plasma proteins and associated routine clinical tests in an independent cohort and examined candidate FDA-approved drugs targeting multiple upregulated proteins and based on biological pathways specific for patients with severe complications.
Study cohort characteristics
Characteristics of the study groups, patients (severe and mild-moderate) and healthy controls, are summarized in Table 1. Most infected patients were males (n = 91, 91%). The median age [interquartile range (IQR)] of patients with severe COVID-19 disease defined by admission to ICU (47[35–55] years), but not mild-moderate patients, was higher than the control groups (vs. 38[33–42] years, p < 0.001). The ethnicity distribution in the severe and mild-moderate groups was not significantly different; however, the control group had a higher percentage of the Indian subcontinent ethnicity (p = 0.04). Patients with severe disease had a significantly higher BMI and were either overweight (n = 25, 50%) or obese (n = 18, 36%) (p < 0.001), and had a significantly higher heart rate and lower SpO2 (p < 0.001 for both). Moreover, diabetes and hypertension were significantly associated with severe complications in the lungs and kidneys, compared to mild-moderate disease (Supplementary Data 1).
High differential protein expression in plasma from patients with severe complications
Plasma from 50 severe and 50 with mild-moderate COVID-19 patients and 50 control subjects were analyzed using ten different Olink panels (Supplementary Data 2). For one patient, P064, Olink assays failed QC for seven panels; thus, was excluded. The number of differentially expressed proteins (DEPs) from single panels for samples that passed Olink’s QC (Supplementary Fig. 1) is summarized in Fig. 1a. Given the characteristics of our cohort, such as over-representation of males in all groups and younger age of controls, the DEP analyses were corrected for interaction between severity and obesity, sex, age, ethnicity, heart rate, and SpO2. Severe disease versus control identified a large numbers of DEPs; more than 40 out of 92 (>43%) per panel across all panels, whereas the number of DEPs was less in mild-moderate disease versus control. Receiver operating characteristic (ROC) curve analyses using the DEPs in each panel, calculated as a single score, found that all panels significantly classified severe cases versus mild-moderate cases and controls; high area under the curve (AUC, p < 0.01) (Fig. 1a).
For a comprehensive molecular view, we carried out the analysis on combined data from the ten Olink panels (893 unique proteins) as a single dataset (Supplementary Data 2). Unsupervised hierarchical clustering, before filtering, revealed that the ten panels could differentiate severe from mild-moderate diseases and controls (Fig. 1b). More DEPs were identified when comparing the severe disease to mild-moderate disease or controls than the mild-moderate disease to controls (Fig. 1c and Supplementary Data 3). Additionally, the DEPs in severe disease versus mild-moderate disease and controls were mainly upregulated, whereas DEPs in mild-moderate versus controls groups had an equal up-and-down-regulation distribution (Fig. 1c).
Functional analysis of the deregulated proteins in plasma of severe COVID-19 patients
The DEPs in severe versus mild-moderate, severe versus controls, and mild-moderate versus controls were subjected to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis. The statistical significance of enriched pathways should be treated cautiously since our proteomic assays were based on enriched panels; however, relative enrichment is warranted. Cytokine–cytokine receptor interactions were enriched gradually from control subjects to mild-moderate and then severe diseases groups (Supplementary Fig. 2a–c). Such gradual enrichments were observed for several pathways, mainly related to immune, inflammation, and infection and the associated cell signaling pathways.
While standard pathway analyses are informative, the function of proteins in the plasma, particularly during pathology, may not be accurately reflected in such analyses. To this end, we focused on the DEPs in the severe COVID-19 group to carry out functional annotation based on information from databases and literature and concerning their role in circulation and pathogenesis. In total, 375 DEPs were identified in plasma from severe versus mild-moderate groups (Supplementary Fig. 2d, Supplementary Data 3), of which 189 (50%) are secreted proteins based on the Human Protein Atlas secretome2 and 123 (33%) DEPs are released (secreted or shed) proteins according to literature (Supplementary Data 3, Supplementary Notes). There was no literature for the presence of 64 (17%) DEPs in blood; however, 62 of those were also detected in severe COVID-19 patients in the independent, longitudinal cohort of SARS-CoV-2 infected patients from the Massachusetts General Hospital (MGH cohort)3 (Supplementary Data 3). Among the 375 DEPs, 288 (77%) were classified into 11 functional groups based on the suggested relationship between functional annotation according to databases and literature searching and COVID-19 pathogenesis and immunity. The remaining 88 DEPs consisted of 64 intracellular or membrane proteins with no described function in the blood and 24 secreted proteins with an unclear role in COVID-19 pathogenesis (for details, refer to Supplementary Data 3, Supplementary Notes).
The 288 functionally annotated DEPs in severe versus mild-moderate cases included cytokines and chemokines (13 DEPs), markers of innate immunity (6 DEPs), and markers of T or NK cells-mediated immunity (6 DEPs), which are presumably initiated in the alveolus and the interstitium in response to infection before reaching to circulation (Fig. 2). A significant number of DEPs in the severe group were related to immune evasion (33 DEPs), including IL10, which may play a pathological role in COVID-19 severity proinflammation and T-cell exhaustion4. Another large functional network is connected to T helper (Th) cell dysfunction in the severe group (31 DEPs), including the inflammatory role of IL6 and the highly inflammatory trans-signaling through the soluble IL6 receptor5 that maintains local Th17 cells6. However, agonists of Th1/Th17 responses (labeled pos. in Fig. 2) are countered by a high level of antagonists in the severe group (labeled neg. in Fig. 2), including soluble IL17RA7,8, and IL17RB9, which act as decoy receptors to inhibit the functional effect of IL17 secreted by Th17 cells. Other examples of immune evasion and Th cell dysfunction are detailed in the “Discussion” section (Supplementary Data 3 and Supplementary Notes elaborate further on the function of DEPs).
Three additional functional networks were expressed in the severe group; inflammation (24 DEPs), coagulopathy (27 DEPs), and neutrophil activation and NETosis (program for the formation of neutrophil extracellular traps [NETs]) (24 DEPs). Finally, 124 DEPs are related to organ damage; lung damage (14 DEPs), endothelial and cardiovascular damage (66 DEPs), and other or multiple organs (44 DEPs) (Fig. 2). Of the 124 DEPs related to organ damage, 116 and 115 (93%) were significantly deregulated on day 0 (at admission) and day 3 in patients who were eventually intubated or died (severe group) by day 28 in the MGH cohort3 (Supplementary Data 3). Overall, 364 (97%) out of the 375 DEPs in severe versus mild-moderate and controls groups were also significantly deregulated in the MGH cohort in patients who eventually developed severe COVID-19 on admission (day 0) and day 3, respectively. This suggests that dysregulation of these proteins is an early event and not restricted to ICU-admitted patients, as in our cohort. Furthermore, 330 proteins were deregulated in the same direction in our cohort and the MGH cohort; 91% concordance (Supplementary Data 3).
Next, we analyzed the interactions between the 288 proteins (Fig. 2) using the STRING database10 (STRING-db, version: 11.0). We found that 587 interactions with a STRING-db confidence score of 0.7 or higher connected 213 (74%) of these proteins, suggesting a complex biological interplay between the 11 functional networks (Supplementary Fig. 3). Among the clinically available blood biomarkers in our cohort, C-reactive protein (CRP) concentration correlated with most DEPs (308, 82%), followed by creatinine (242, 64%), urea (210, 56%), and glucose levels (194, 52%) (Fig. 3). In respect to blood cells, white blood cells (WBC) counts had the highest frequency of correlations with DEPs (179, 48%), followed by neutrophils (92, 24%), platelets (74, 20%), and eosinophils (33, 9%), whereas lymphocyte and monocyte count correlated only with 20 (5%) and 7 (2%), respectively (Fig. 3). At the individual functional groups level (Fig. 2), CRP, followed by creatinine, showed the highest frequency of correlations with DEPs in all groups. WBC followed by neutrophils showed the highest frequency of correlations with DEPs in all functional groups (Supplementary Fig. 3), but it was notable that blood cell counts generally had lower frequency of correlations with DEPs compared to biochemical markers.
Potential drugs to target deregulated proteins in COVID-19 patients with severe complication
In addition to targeting the enriched KEGG pathways (Supplementary Fig. 2) such as TNFα, coagulation, or JAK-STAT, an analysis of protein-drug interaction (PDI) was carried out based on the upregulated proteins summarized in Fig. 2 in patients with severe versus mild-moderate disease groups. The Drug-Gene Interaction database11 (DGIdb, v4.2.0) was screened for FDA-approved drugs which interact with the significantly upregulated proteins (>1.5-fold) in severe group versus both the mild-moderate and control groups. We identified 215 FDA-approved drugs that targeted 74 proteins and were grouped according to drug classes (sheet a of Supplementary Data 4). Briefly, the FDA-approved drugs list included anti-viral drugs (e.g., ribavirin and ritonavir), anticoagulant and thrombolytic drugs (e.g., tenecteplase), corticosteroids and glucocorticoids (e.g., dexamethasone, methylprednisolone and prednisone), nonsteroidal anti-inflammatory (e.g., aspirin and indomethacin), nonsteroidal antiandrogen (e.g., flutamide), immunosuppressive and immunomodulatory drugs (e.g., anakinra, methotrexate, tocilizumab, atezolizumab, and nivolumab).
A larger number of drugs (113 drugs) target single proteins, which were upregulated by greater than or equal to 2-fold in severe versus mild-moderate groups (Fig. 4a), compared to the 38 drugs that target single proteins which were upregulated by 1.5 to 2-fold in severe COVID-19 (Supplementary Fig. 4). A notable feature of the PDI networks is the large interactions between the proteins. This may suggest that the benefit from many single-target drugs might be compromised, whereas drugs targeting several proteins may be more promising. To this end, we focused on 66 drugs that target multiple proteins with 2-fold or more (Fig. 4b) or 1.5- to 2-fold (Supplementary Fig. 4) upregulation in severe versus mild-moderate groups. Twenty-five of these 66 drugs target three or more upregulated proteins in severe versus mild-moderate groups (sheet b Supplementary Data 4). The drug atorvastatin targets the highest number of proteins, CXCL10, LDLR, PLAT, TFPI, IL2RA, FAS, and LEPR with 56 interactions. The anti-viral drug ribavirin targets four proteins with more overall interactions (68 interactions); IL6, LDLR, VWF, and CST3. The two forms of a glucocorticoid, prednisone and methylprednisolone, target four proteins with 43 interactions, CALCA, VWF, CXCL10, and IL2RA. The two anti-TNF drugs, etanercept, and infliximab, target the same four proteins (IL6, TNFRSF1A, TNFRSF1B, and KLRD1) with 74 interactions. We deduced drug combinations that would target the highest number of the total 265 interactions (detailed in sheet b of Supplementary Data 4), which included ribavirin + infliximab or etanercept without (7 targets-96 interactions) or with methylprednisolone (10 targets-127 interactions), ribavirin + methylprednisolone without (7 targets-99 interactions) or with cyclosporine (11 targets-167 interactions), and ribavirin + sirolimus without (9 targets-127 interactions) or with methylprednisolone (12 targets-158 interactions). Combining ribavirin and methylprednisolone with sirolimus or cyclosporine targeted the largest fraction (60%) of the 265 interactions between the upregulated proteins in the severe COVID-19 patients.
The molecular severity score: a 12-protein signature for COVID-19 severity
To develop blood protein signatures, we used the MUVR tool12 for variable selection and validation in multivariate modeling to identify the most stable DEPs that can differentiate all groups’ status (severe versus mild-moderate, severe versus controls, and mild-moderate versus controls). We also used Boruta, a wrapper algorithm for all relevant feature selection13. By overlapping the features selected by MUVR and Boruta, 35 common proteins were identified (Fig. 5a), of which 12 were selected in 100% of individual 500 variable selection runs (Fig. 5b). These 12 proteins were combined to develop the COVID-19 molecular severity score, which classified severe versus the mild-moderate and controls groups with 100% specificity and 98% sensitivity (AUC under ROC curve 0.999) (Fig. 5c).
We validated the COVID-19 molecular severity score in the independent Massachusetts General Hospital (MGH) cohort3 (Supplementary Data 5). The score was significantly higher in patients with severe (Acuity 1—death [A1] and Acuity 2—intubated, ventilated but survived 28 days [A2]) compared to non-severe disease (Acuity 3–5, A3–5) (Fig. 6a, b, Supplementary Data 6 for statistical comparisons). The severity scores from plasma collected on days 0 and 3 were highly predictive of COVID-19 severity using the maximum acuity data (A1 or A2 versus the rest, Fig. 6c). The MGH cohort included 57 patients who were intubated at admission but survived for the 28 days, thus, these patients were excluded from the severity analysis to better evaluate the prognostic value of the molecular severity score. The score using day 0 data predicted severity (AUC 0.836) and death (AUC 0.872) between day 3 and day 28 post-admission (Fig. 6d). The score using day 3 data also predicted severity and death (AUC 0.941) between days 7 and 28 post-admission (Fig. 6e). The COVID-19 molecular severity score on day 3 may still be prognostically useful since most of the severity (34 out 52 [60%]) and death (32 out of 42 [76%]) events occurred between days 7 and 28 post-admission. Finally, the molecular severity score in the MGH cohort was significantly higher in severe COVID-19 patients compared to symptomatic SARS-CoV-2 negative patients (n = 78) but not different between non-severe virus positive and virus negative patients (Supplementary Fig. 5).
A molecularly trained clinical score to predict COVID-19 severity
We hypothesized that the molecular severity score could be used to identify informative clinical parameters and provide scoring system for each parameter to combine them into a single score to predict COVID-19 severity. Eleven out of the 24 clinical parameters available in our cohort were significantly associated with the molecular severity score (Supplementary Fig. 6a), 10 of which significantly classified severe versus mild-moderate disease by ROC curve analysis (Supplementary Fig. 6b). MUVR determined that 7 were most informative (Fig. 7a, Supplementary Fig. 6c, and Supplementary Data 6 for statistical comparisons), which were combined to develop a molecularly trained 7-marker Clinical Score where each clinical measure was weighted according to its molecular severity score. The 7-marker clinical score differentiated severe and mild-moderate groups with 94.7% sensitivity and 85.7% specificity and outperformed each of the single parameters in our cohort (Fig. 7b). Adding the remaining clinical markers (diabetes, SpO2 and/or eosinophils) to the 7-marker clinical score did not improve classification (Supplementary Fig. 6d).
Four out of the 7 markers were also available in the MGH cohort; neutrophil counts, lymphocyte counts, CRP and creatinine levels (Supplementary Data 5). A 4-marker clinical score discriminated severe from mild-moderate groups in our cohort (Fig. 7c), predicted severity in the MGH cohort using data from days 0 (AUC 0.77) and 3 (AUC 0.85), and was more prognostic than the single markers (Fig. 7d). The 4-marker score in the MGH cohort predicted 70% (95% CI 63–77%) and 81% (95% CI 72–90%) risk of severity for patients with a score higher than 6 compared to 16% (95% CI 12–20%) and 8% (95% CI 4–12%) severity risk for patients with a score less than 4 based on days 0 and 3 data, respectively (Fig. 7d). While the 4-marker clinical score may be more readily available for clinical utility, the molecular severity score was significantly more predictive in the MGH cohort (AUC pairwise comparison; P = 0.0009 for day 0 and P = 0.0004 for day 3 data). The molecular severity score was more predictive than every clinical feature in the MGH cohort (P < 0.0001 from AUC pairwise comparisons), which included age, BMI, pre-existing conditions (kidney, heart, and lung diseases, diabetes, hypertension and immunocompromised conditions), symptoms at presentation (respiratory, fever or gastrointestinal), or blood markers (lymphocytes, monocytes and neutrophils counts, CRP, creatinine, D-dimers, and LDH concentrations).
The population of Qatar with SARS-CoV-2 infection during the early first wave of the COVID-19 pandemic is demographically unique14 (predominantly males of younger age) when compared to other populations such as that described in the ISARIC1. The cohort of SARS-CoV-2 infected patients in this study was collected early in the pandemic and in the same time frame as the nationwide cohort of the first consecutive 5000 patients with COVID-19 in Qatar14, thus had similar characteristics of most of the SARS-CoV-2 infected patients (88.7%) being males, with a median age of 35 and the majority (65%) under 45 years old14. Between March and April 2020, hospitalizations were predominately under 65 years of age; 95.7% for non-ICU and 85.2% for ICU patients. ICU patients in Qatar were further enriched for males (92.6%), and only 16 patients (14.8%) in the ICU were above 65 years or older14. Our study selection for patients aged between 18 and 65 and being mainly males with only 9% females was based on the local COVID-19 demographics during the early first wave of the pandemic.
Various differentially expressed plasma proteins were identified in severe COVID-19 patients compared to mild-moderate disease and controls in our study. Typical KEGG pathway enrichment analysis found relative enrichment of pathways in severe versus mild-moderate COVID-19 disease, which have been previously reported in COVID-19. These included cytokine–cytokine receptor interactions and viral interaction cytokine/cytokine receptor interactions related to the cytokine storm15,16,17, immune, inflammation, and infection pathways such as TNF and JAK-STAT signaling pathways15,17, and complement and coagulation cascades15,18,19.
Patients with severe disease in our cohort had lower lymphocyte counts and the COVID-19 molecular severity score significantly associated with lymphopenia, a prominent feature of SARS-CoV-2 infection and disease severity20. Another feature of severe COVID-19 disease is apparent immunity, particularly T cell responses20. While our study did not directly investigate immune cells, the detailed analysis of the 375 dysregulation plasma proteins, particularly for functional role in circulation, shed light on the pathogenesis of severe COVID-19 disease. This functional annotation outlined 11 functional networks including cytokines and chemokines and markers of innate and T and NK cell-mediated immunity which were counteracted by larger networks of immune evasion and T helper (Th) cell dysfunction. For example, severe COVID-19 patients had higher levels of the soluble form of ULBP2, a ligand for the NKG2D receptor on NK cells that mediates mediating cytotoxicity, which inhibits NK-cells as a mechanism to evade immunosurveillance by NK cells21,22. Other examples include circulating PD-L1, which induces immune suppression and damage, and associates with COVID-19 pathogenesis and mortality23, soluble LILRB4 (sLILRB4), which can be produced by a splice variant24 and suppresses T cell responses and elicits T cell anergy or activation of Treg or T suppressor cells24,25,26, and PVR (CD155) which is a ligand for CD226 (DNAM-1) and TIGIT expressed on NK cells and a subset of T cells27, but the soluble form inhibits NK cells CD226-mediated cytokine production28. NECTEN2 (also called PVRL2), a ligand for DNAM-1 and PVRIG on NK and T cells27, has a soluble form (sNECTIN2) which is inhibitory of function29,30 and was elevated in plasma of severe COVID-19 patients in our cohort. Both TIM-1 (HAVCR1) and its ligand TIM-4 (TIMD4) were upregulated in the plasma of severe COVID-19 patients. The soluble form of TIM-1 may be inhibitory of cellular function31 including its role in regulating Th2 responses32. Likewise, the soluble form of TIM-4 may be inhibitory of cellular function TIM-4 in Th2 development31. Another duo of a ligand and its receptor that was upregulated in plasma of severe COVID-19 is IFNL1 (INF lambda 1, type-III INF) and one of its receptors, IFNLR1. IFNL1 is released by epithelial tissues to bind to IL10RB/IFNLR1 dimers and is involved in antiviral host defense and inhibits Th2 polarization towards Th133,34,35. However, soluble IFNLR1 (sIFNLR1/sIFN-λR1) inhibits the antiviral and immune effects of type III INF signaling and the induction of IFN-stimulated genes36. Further indicators of immune evasion include ST3GAL1 which is carried in circulation by platelets and released upon activation37 and ST3GAL1-mediated O-linked sialylation of CD55 act as CD55-mediated immune evasion38 and the dramatic early proinflammatory IL10 elevation which may play a pathological role in COVID-19 severity proinflammation and T-cell exhaustion4. Finally, the T and NK cell-specific serine protease granzyme A (GZMA) for lysis of target cells was specifically downregulated in plasma from severe versus mild-moderate COVID-19 patients in our cohort in line with previously reported reduced levels in COVID-19 severe patients associate with impaired NK- and cytotoxic T cell functions39,40.
Increased neutrophil counts (neutrophilia)41,42 and eosinophils counts42 are known features of severe COVID-19 and were replicated in our study. In contrast CD4+ and CD8+ T cells are significantly reduced in severe COVID-1941,42 but these were not investigated in our study. Nonetheless, a large functional network deduced from the dysregulated plasma proteins in our study pointed to Th cell dysfunction, which is an emerging area in severe COVID-19 immunopathology17,20,43,44. Both agonist and antagonist plasma proteins of Th1, Th2, and Th17 were identified. For example, the highly inflammatory IL6 trans-signaling through the soluble IL6 receptor5 which maintains local Th17 cells6 is challenged by the upregulated levels of IL27 that potentiates the early phase of Th1 response and suppresses Th2 and Th17 differentiation45. Other agonists of Th1 and Th17 responses including soluble IL17RA7,8, and IL17RB9 which act as decoy receptors to inhibit the functional effect of IL17 secreted by Th17 cells. Other examples of upregulated soluble, decoy receptors which may skew Th cell responses include IL1R1 and IL1R2 (inhibit IL1B46), IL1RL1 (inhibits IL3347), IL1RL2 (inhibits IL3648), and IL18R1 and IL18BP (inhibit IL1849,50,51). Other functional networks identified in the severe COVID-19 group in our cohort have been previously characterized, including inflammation, coagulopathy, neutrophil activation and NETosis, and endothelial damage52.
In addition to the high concordance with the MGH study, the plasma proteins identified in our study also confirmed the findings from several studies that identified deregulated plasma proteins associated with COVID-19 severity, ICU admission and mortality53,54. Importantly, one study reported a unique neutrophil activation signature composed of neutrophil activators (G-CSF, IL8) and effectors (RETN, LCN2, and HGF), with a strong predictive value to identify critically ill patients whereby the effector proteins strongly correlated with absolute neutrophil count53. Our study not only identified those components of the neutrophil activation signature but also found that the COVID-19 molecular severity score also correlated with absolute neutrophil counts. Besides Olink technology, mass spectroscopy has also been used to identify deregulated proteins in sera from SARS-CoV-2 infected patients (e.g., ref. 55,56). In addition to pathway analysis, these two examples of mass spectroscopy-based proteomics developed predictors of COVID-19 severity. Although none of their serum biomarkers were identified in our study, the biological functions reported in these studies were also captured in our analysis, including complement factors and the coagulation system, inflammation modulators, and pro-inflammatory factors upstream and downstream of IL6. We cannot exclude that the mass spectroscopy-based studies are more comprehensive and less biased than the panel profiling used in our study. However, it should be noted that there was a small overlap between all the proteins detected by mass spectroscopy in sera (before statistical analysis). A more comprehensive comparison between the several published serum or plasma proteomics of COVID-19 patients using the Olink platform and different mass spectroscopy methods is warranted but is beyond the scope of our study.
Our study developed a weighted scoring method to use clinical markers available at admission to predict severity. Several large cohort studies have identified clinical features associated with severity, such as certain comorbidities and older age. These associations, or risk factors, require integration in a model (scoring system) to be used as prognostic tools. A large study by the US National COVID Cohort Collaborative (N3C) based on 174,568 adults with SARS-CoV-2 developed a machine learning model to predict clinical severity using 64 inputs available on the first hospital day with an AUC of 0.8757. Another recent study compiled prediction models for COVID-19 severity from 41 studies where more than 60% of the models were from China, the remaining were from Europe or the US, and two were multinational58. The compiled models included eight which could be evaluated in the study’s independent cohort (University of Illinois Hospital [UIH] Cohort, n = 516) and found that the AUCs from these ‘external’ models ranged from 0.69 to 0.89, while their ‘internal’ models had AUCs of 0.84 for mortality and 0.83 for criticality58. Compared to the N3C study (AUC 0.87), the simpler 7- and 4-marker clinical scores developed in our study had AUCs of 0.93 and 0.90, respectively, and the AUCs from the 4-marker clinical score in the MGH cohort was 0.77 and 0.85 on day 0 and day 3 of admission. Similarly, the AUCs in our study were at least comparable to those from the ‘external’ and ‘internal’ models from the UIH cohort study.
In terms of the specific clinical variables, the seven markers in our model were ranked high in terms of importance in the 64-input model from the N3C study (importance ranks using random forest and XG Boost methods were 4 and 23 for glucose, 5 and 3 for respiratory rate, 7 and 10 for WBC, 8 and 53 for creatinine, 11 and 27 for the neutrophil count, 14 and 20 for lymphocyte count, and 16 and 14 for CRP)57. The UIH internal models included all the markers of the 4-marker clinical score (CRP, creatinine, neutrophils, lymphocytes) and 5 out of the 7-marker clinical score in our study (WBC in addition to the 4 markers). Interestingly, the UIH study noted that the features used in the tested models were “surprisingly diverse” and the number of variables in each model ranged from 2 through 1158. However, the study also noted that three external models (from China) performed well in their cohort from the US, demonstrating that prediction is possible despite geographical and ethnic differences and variations in health systems and during different times of the pandemic58. Our study agrees with this conclusion since the 4-marker clinical score from the patients in Qatar performed well in the US MGH cohort. Importantly, the 12-protein COVID-19 molecular severity score reported here was cross-validated in an independent, ethnically different, larger cohort from the Massachusetts General Hospital.
In addition to their potential biomarker value, proteomic profiles can also be used to predict potential drugs for intervention. Our drug–protein interaction analyses shortlisted several FDA-approved drugs that can target the upregulated proteins in severe COVID-19 cases. The classes of potential drugs identified in our analysis is in line with those summarized in the living systematic review and metanalysis of drug treatments for COVID-1959. These include the glucocorticoids methylprednisolone and dexamethasone which have been shown to reduce the risk of mortality, mechanical ventilation requirement and length of hospital stay59. Other examples identified in our analysis include tocilizumab and sarilumab which target the IL6R and found to reduce the need for mechanical ventilation and the length of hospital stay59, the anti-IL6 antibody siltuximab which has been recently shown to improve survival in hospitalized COVID-19 patients60, and the JAK inhibitor ruxolitinib which has been shown to reduce the risk of mechanical ventilation and its duration59. Infliximab and adalimumab were two examples of anti-TNF targets identified in our study; an approach suggested for treating COVID-1961 and are still until trials in the UK (AVID-CC trial62) and the US (ACTIV-1 IM trial, NCT04593940). While aspirin was proposed in our analysis, its effect against COVID-19 mortality is supported in a metaanalysis63, but only associated with a small increase in the rate of being discharged alive64. Guided by drug–protein interactions, our analysis proposed combinations that target the larger number of proteins and interactions where ribavirin with methylprednisolone can be used in combination with infliximab, cyclosporine or sirolimus. Cyclosporine was recently shown to associate with a significant decrease in COVID-19 mortality in a cohort study65. Sirolimus (rapamycin) has been proposed to be used against COVID-19 based on preclinical and clinical evidence66.
In conclusion, our study identified deregulated proteins in the plasma of patients with severe COVID-19 complications that may inform therapeutic interventions. The 12-protein signature identified in our study was developed as the COVID-19 molecular severity score and used to stratify patients according to COVID-19 severity in an independent cohort. The COVID-19 molecular severity score could predict outcomes up to 28 days post-admission and from as early as 3 days of admission. The clinical risk scores, based on 7 or 4 clinical markers, developed in this study uses a simple scoring system of clinical parameters available at the time of admission. The molecular severity and the clinical risk scores developed here have the potential to stratify SARS-CoV-2 infected patients at early stages according to their risk of developing complications to prospectively inform healthcare management and clinical decision-making to prevent complications and mortality.
The study received IRB approval from the Hamad Medical Corporation (HMC, Doha, Qatar) and was supported by a grant from HMC-Medical Research Council (MRC); approval and fund number MRC-05-003. Written informed consent was obtained from all the participants in the study. Participants were not compensated for participation. The conduct of this study was in accordance with the International Council for Harmonization’s Guideline for Good Clinical Practice (ICH-GCP) and the Declaration of Helsinki. A cohort of 100 patients (mild-moderate and severe) affected by COVID-19 disease and admitted to Hamad Medical Corporation (HMC) hospitals; tertiary level hospitals in Doha, Qatar, were recruited. Infection was confirmed by positive RT-PCR assays for SARS-CoV-2 from sputum and throat swab with Ct values around 30. Patients with severe COVID-19 were defined as those requiring ICU admissions due to COVID-19 disease or disease complications, while patients with mild-moderate COVID-19 were admitted to community hospitals but did not requiring ICU care. Fifty control subjects were recruited at the Clinical Research Center of the Anti-Doping Laboratory Qatar from volunteers identified by Qatar Red Crescent Society, according to the criteria of being healthy, without prior history of confirmed COVID-19 infection diagnosis, normal SpO2%, and vital signs. Individuals with poor cognitive ability, or any past or present medical disease or were not able to consent were excluded.
Samples collection and processing
Peripheral blood was collected within 5 to 7 days of admission into commercially available EDTA-treated tubes, and plasma and peripheral blood mononuclear cell (PBMC) fractions were separated using Ficoll. Plasma was stored at −80 °C until further analysis.
Olink proteomic assays
Plasma samples were profiled in-house using the proximity extension assays (PEA), 96-plex immunoassay developed by Olink Proteomics (Uppsala, Sweden)67 following the standard protocol at Qatar Biomedical Research Institute’s (QBRI) Olink certified proteomics core facility. Quality control and data normalization according to internal and external controls were carried out using the Normalized Protein eXpression (NPX) software, Olink NPX Manager (version 126.96.36.199), and every run was checked and validated by the Olink support team in Uppsala. Ten different panels, each focused on a specific disease or biological process, were used in our study; panel names are stated in the results.
For the analysis of Olink assays, the protein expression values, as log2 of Normalized Protein eXpression (NPX), were used. Two approaches were used in the analysis; single-panel and combined-panels analyses before confirming the overlap between the two approaches. Olink data that did not pass quality control was excluded from the analyses. R packages for hierarchical clustering (heatmap.2 in version 3.1.1 of the gplots package), principal component analysis (PCA, prcomp in the Stats R package version 4.1.1), differential expression analysis (Linear Models for Microarray Data, limma version 3.28.14), volcano plots, gene-ontology biological process (GO-PB) and KEGG pathways enrichment analyses were used through the standalone version of iDEP.92 (version .92)68 installed in RStudio (version 1.2.5). Severe COVID-19 is associated with age and other factors, and infections in Qatar and our cohort had unique characteristics such as enrichment for males and younger age. Thus, the differential expression analyses between the three study groups accounted for interaction between severity as the main effect and other variables, including obesity, sex, age, ethnicity, heart rate, and SpO2. The median and interquartile range (IQR) for blood collection from severe (5 [IQR 4–7] days) versus mild-moderate groups (4 [IQR 2–6] days) approached significance (t-test p = 0.061). Thus, for more accuracy in the differential expression analysis between severe and mild-moderate groups, we also accounted for the interaction between severity as the main effect and the number of days between admission to blood collection.
For variable selection and validation, we used two algorithms; MUVR12 (multivariate modeling with minimally biased variable selection in R, version 0.0.975), a statistical validation framework, incorporating a recursive variable selection procedure within a repeated double cross-validation (rdCV) scheme, and Boruta13 (version 7.0.0), a wrapper algorithm for all relevant feature selection that reports the importance of features and the number of times a feature is selected from repeated runs compared to all other features. Differentially expressed proteins selected by MUVR were used to develop protein signatures represented as meta-protein scores calculated as the ratio of average expression of NPX values of upregulated proteins to the average expression of NPX values of downregulated proteins. Upregulated and downregulated proteins were defined according to the score. For example, if the score was from the comparison of severe versus mild-moderate COVID-19 patients, we used the upregulated or downregulated proteins in the severe versus mild-moderate groups. Scores were evaluated using receiver operating characteristic (ROC) curve analyses to determine the area under the ROC curve (AUC), sensitivity, specificity, and significance (p < 0.05) using MedCalc® (version 12.7, MedCalc Software Ltd., Belgium).
Protein-protein interaction (PPI) was analyzed and visualized using the STRING database (STRING-db version 11.0)10 accessed through Cytoscape (version 3.7.2)69. Protein-drug interaction (PDI) was analyzed using the Drug-Gene Interaction database (DGIdb, version 4.2.0)11, only using FDA-approved drugs, and interaction networks were visualized in Cytoscape.
Validation of the COVID-19 molecular severity score in the MGH cohort
To validate the COVID-19 molecular severity score (the 12-protein signature) developed here, we used the Massachusetts General Hospital (MGH) cohort. The MGH cohort enrolled 384 acutely ill patients, 18 years or older patients, with a clinical concern for COVID-19 upon arrival in the emergency department as described previously3. SARS-CoV-2 positivity was reported for 306 patients (80%) while the remaining 78 patients were SARS-CoV-2 negative. The COVID-19 molecular severity score was calculated as described above (meta-protein score) for each patient. The performance of the COVID-19 molecular severity scores in the MGH cohort was evaluated with ROC curve analysis using MedCalc® (version 12.7, MedCalc Software Ltd., Belgium).
Statistics & Reproducibility No statistical method was used to predetermine sample size. No data were excluded from the analyses except for one patient, P064, whose Olink assays failed the internal Olink QC in seven out of the 10 panels used; thus, was excluded in the initial analysis for Fig. 1 and Supplementary Figs. 1 and 2. As per the manufacturer instructions, the plasma samples from the three groups of our cohort were randomized across the Olink plates to minimize bias. The Investigators were not blinded to allocation during experiments and outcome assessment; however, the sample acquisition and processing, Olink data acquisition and internal Olink QC, and the subsequent data analysis and bioinformatics were carried out by independent groups of the Investigators. Patient clinical data analysis was performed using Statistical Package for Social Sciences (SPSS version 26, Chicago IL, USA). Groups were compared using the chi-square test, and Fisher’s exact test (two-tailed) replaced the chi-square in the case of a small sample size where the expected frequency is less than 5 in any group. The results were presented as mean ± SD for normally distributed data or median (IQR) for skewed results and/or number and percentage of participants as appropriate. The level of statistical significance was set at p < 0.05. GraphPad Prism (version 8.4.3, GraphPad Software LLC, CA, USA) was used to compare protein signature scores across clinical subgroups using unpaired, two-tailed t-tests or one-way ANOVA with Dunnett’s multiple testing correction.
Further information on research design is available in the Nature Research Reporting Summary linked to this article.
All raw data relating to the cohort in this study are supplied in Supplementary Data 2 and were used to generate Fig. 1, Supplementary Fig. 1, Fig. 5c, Fig. 7a–c, and Supplementary Fig. 6. Supplementary Data 3 contains the data used for Fig. 2, Fig. 3, Fig. 5a, b, Supplementary Fig. 2, and Supplementary Fig. 3. Supplementary Data 4 contains the data for Fig. 4 and Supplementary Fig. 4. Supplementary Data 5 includes the data extracted from the publicly available Massachusetts General Hospital (MGH) cohort published by Filbin et al.3 and used to generate the graphs in Fig. 6, Supplementary Fig. 5, and Fig. 7d. Supplementary Data 6 details the statistical comparisons related to Fig. 6a, b, Fig. 7a and Supplementary Fig. 6a. The two databases used in this study are available online; String-db (version 11) at https://string-db.org/ and DGIdb (version 4.2.0) at https://www.dgidb.org/.
ISARIC Clinical Characterisation Group, et al. ISARIC Clinical Data Report issued: 14 July 2021. medRxiv, 2020.2007.2017.20155218. Preprint at https://doi.org/10.1101/2020.07.17.20155218 (2021).
Uhlen, M. et al. Proteomics. Tissue-based map of the human proteome. Science 347, 1260419 (2015).
Filbin, M. R. et al. Longitudinal proteomic analysis of severe COVID-19 reveals survival-associated signatures, tissue-specific cell death, and cell-cell interactions. Cell Rep. Med. 2, 100287 (2021).
Lu, L., Zhang, H., Dauphars, D. J. & He, Y. W. A potential role of interleukin 10 in COVID-19 pathogenesis. Trends Immunol. 42, 3–5 (2021).
Rose-John, S. IL-6 trans-signaling via the soluble IL-6 receptor: importance for the pro-inflammatory activities of IL-6. Int. J. Biol. Sci. 8, 1237–1247 (2012).
Jones, G. W. et al. Loss of CD4+ T cell IL-6R expression during inflammation underlines a role for IL-6 trans signaling in the local maintenance of Th17 cells. J. Immunol. 184, 2130–2139 (2010).
Zaretsky, M., Etzyoni, R., Kaye, J., Sklair-Tavron, L. & Aharoni, A. Directed evolution of a soluble human IL-17A receptor for the inhibition of psoriasis plaque formation in a mouse model. Chem. Biol. 20, 202–211 (2013).
Sohda, M. et al. Identification of a soluble isoform of human IL-17RA generated by alternative splicing. Cytokine 64, 642–645 (2013).
Gratchev, A. et al. The receptor for interleukin-17E is induced by Th2 cytokines in antigen-presenting cells. Scand. J. Immunol. 60, 233–237 (2004).
Szklarczyk, D. et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 47, D607–D613 (2019).
Freshour, S. L. et al. Integration of the drug-gene interaction database (DGIdb 4.0) with open crowdsource efforts. Nucleic Acids Res. 49, D1144–D1151 (2021).
Shi, L., Westerhuis, J. A., Rosen, J., Landberg, R. & Brunius, C. Variable selection and validation in multivariate modelling. Bioinformatics 35, 972–980 (2019).
Kursa M. B. & Rudnicki W. R. Feature selection with the boruta package. J. Stat. Softw. 36, 1–13 (2010).
Omrani, A. S. et al. The first consecutive 5000 patients with coronavirus disease 2019 from Qatar; a nation-wide cohort study. BMC Infect. Dis. 20, 777 (2020).
Hou, X. et al. Serum protein profiling reveals a landscape of inflammation and immune signaling in early-stage COVID-19 Infection. Mol. Cell Proteom. 19, 1749–1759 (2020).
Costela-Ruiz, V. J., Illescas-Montes, R., Puerta-Puerta, J. M., Ruiz, C. & Melguizo-Rodriguez, L. SARS-CoV-2 infection: The role of cytokines in COVID-19 disease. Cytokine Growth Factor Rev. 54, 62–75 (2020).
Yang, L. et al. The signal pathways and treatment of cytokine storm in COVID-19. Signal Transduct. Target Ther. 6, 255 (2021).
Ramlall V. et al. Immune complement and coagulation dysfunction in adverse outcomes of SARS-CoV-2 infection. Nat. Med. 26, 1609–1615 (2020).
Ma, L. et al. Increased complement activation is a distinctive feature of severe SARS-CoV-2 infection. Sci. Immunol. 6, eabh2259 (2021).
Chen, Z., John & Wherry, E. T cell responses in patients with COVID-19. Nat. Rev. Immunol. 20, 529–536 (2020).
Waldhauer, I. & Steinle, A. Proteolytic release of soluble UL16-binding protein 2 from tumor cells. Cancer Res. 66, 2520–2526 (2006).
Lopez-Soto, A., Huergo-Zapico, L., Acebes-Huerta, A., Villa-Alvarez, M. & Gonzalez, S. NKG2D signaling in cancer immunosurveillance. Int J. Cancer 136, 1741–1750 (2015).
Sabbatino, F. et al. PD-L1 Dysregulation in COVID-19 patients. Front Immunol. 12, 695242 (2021).
Jones, D. C. et al. Alternative mRNA splicing creates transcripts encoding soluble proteins from most LILR genes. Eur. J. Immunol. 39, 3195–3206 (2009).
Suciu-Foca, N. et al. Soluble Ig-like transcript 3 inhibits tumor allograft rejection in humanized SCID mice and T cell responses in cancer patients. J. Immunol. 178, 7432–7441 (2007).
Vlad, G. et al. Immunoglobulin-like transcript 3-Fc suppresses T-cell responses to allogeneic human islet transplants in hu-NOD/SCID mice. Diabetes 57, 1878–1886 (2008).
Stamm, H., Wellbrock, J. & Fiedler, W. Interaction of PVR/PVRL2 with TIGIT/DNAM-1 as a novel immune checkpoint axis and therapeutic target in cancer. Mamm. Genome 29, 694–702 (2018).
Okumura G. et al. Tumor-derived soluble CD155 inhibits DNAM-1-mediated antitumor activity of natural killer cells. J. Exp. Med. 217, e20191290 (2020).
Karabulut, M. et al. Serum nectin-2 levels are diagnostic and prognostic in patients with colorectal carcinoma. Clin. Transl. Oncol. 18, 160–171 (2016).
Erturk, K. et al. Serum nectin-2 and nectin-4 are diagnostic in lung cancer: which is superior? Wien. Klin. Wochenschr. 131, 419–426 (2019).
Schweigert, O. et al. Soluble T cell immunoglobulin and mucin domain (TIM)-1 and -4 generated by a disintegrin and metalloprotease (ADAM)-10 and -17 bind to phosphatidylserine. Biochim. Biophys. Acta 1843, 275–287 (2014).
Curtiss, M. & Colgan, J. The role of the T-cell costimulatory molecule Tim-1 in the immune response. Immunol. Res. 39, 52–61 (2007).
Kelm, N. E. et al. The role of IL-29 in immunity and cancer. Crit. Rev. Oncol. Hematol. 106, 91–98 (2016).
Lazear, H. M., Nice, T. J. & Diamond, M. S. Interferon-lambda: immune functions at barrier surfaces and beyond. Immunity 43, 15–28 (2015).
Wang, J. M., Huang, A. F., Xu, W. D. & Su, L. C. Insights into IL-29: emerging role in inflammatory autoimmune diseases. J. Cell Mol. Med 23, 7926–7932 (2019).
Santer, D. M. et al. Differential expression of interferon-lambda receptor 1 splice variants determines the magnitude of the antiviral response induced by interferon-lambda 3 in human immune cells. PLoS Pathog. 16, e1008515 (2020).
Lee-Sundlov, M. M. et al. Circulating blood and platelets supply glycosyltransferases that enable extrinsic extracellular glycosylation. Glycobiology 27, 188–198 (2017).
Lin, W. D. et al. Sialylation of CD55 by ST3GAL1 facilitates immune evasion in cancer. Cancer Immunol. Res. 9, 113–122 (2021).
Li, M. et al. Elevated exhaustion levels of NK and CD8(+) T cells as indicators for progression and prognosis of COVID-19 disease. Front. Immunol. 11, 580237 (2020).
Mazzoni, A. et al. Impaired immune cell cytotoxicity in severe COVID-19 is IL-6 dependent. J. Clin. Invest. 130, 4694–4703 (2020).
Giamarellos-Bourboulis, E. J. et al. Complex immune dysregulation in COVID-19 patients with severe respiratory failure. Cell Host Microbe 27, 992–1000 e1003 (2020).
Kuri-Cervantes L. et al. Comprehensive mapping of immune perturbations associated with severe COVID-19. Sci. Immunol 5, eabd7114 (2020).
De Biasi, S. et al. Marked T cell activation, senescence, exhaustion and skewing towards TH17 in patients with COVID-19 pneumonia. Nat. Commun. 11, 3434 (2020).
Gil-Etayo, F. J. et al. T-helper cell subset response is a determining factor in COVID-19 progression. Front Cell Infect. Microbiol 11, 624483 (2021).
Yoshimura, T. et al. Two-sided roles of IL-27: induction of Th1 differentiation on naive CD4+ T cells versus suppression of proinflammatory cytokine production including IL-23-induced IL-17 on activated CD4+ T cells partially through STAT3-dependent mechanism. J. Immunol. 177, 5377–5385 (2006).
Vasilyev, F. F., Silkov, A. N. & Sennikov, S. V. Relationship between interleukin-1 type 1 and 2 receptor gene polymorphisms and the expression level of membrane-bound receptors. Cell Mol. Immunol. 12, 222–230 (2015).
Altara, R. et al. Conflicting vascular and metabolic impact of the IL-33/sST2 axis. Cardiovasc. Res. 114, 1578–1594 (2018).
Yi, G. et al. Structural and functional attributes of the interleukin-36 receptor. J. Biol. Chem. 291, 16597–16609 (2016).
Novick, D. et al. Interleukin-18 binding protein: a novel modulator of the Th1 cytokine response. Immunity 10, 127–136 (1999).
Reznikov, L. L. et al. The combination of soluble IL-18Ralpha and IL-18Rbeta chains inhibits IL-18-induced IFN-gamma. J. Interferon Cytokine Res. 22, 593–601 (2002).
Takei, S. et al. Soluble interleukin-18 receptor complex is a novel biomarker in rheumatoid arthritis. Arthritis Res. Ther. 13, R52 (2011).
Bonaventura, A. et al. Endothelial dysfunction and immunothrombosis as key pathogenic mechanisms in COVID-19. Nat. Rev. Immunol. 21, 319–329 (2021).
Meizlish, M. L. et al. A neutrophil activation signature predicts critical illness and mortality in COVID-19. Blood Advances 5, 1164–1177 (2021).
Fraser, D. D. et al. Novel outcome biomarkers identified with targeted proteomic analyses of plasma from critically Ill coronavirus disease 2019 patients. Crit. Care Explor. 2, e0189 (2020).
Messner, C. B. et al. Ultra-high-throughput clinical proteomics reveals classifiers of COVID-19 infection. Cell Syst. 11, 11–24 e14 (2020).
Shen, B. et al. Proteomic and metabolomic characterization of COVID-19 patient sera. Cell 182, 59–72 e15 (2020).
Bennett, T. D. et al. Clinical characterization and prediction of clinical severity of SARS-CoV-2 infection among US adults using data from the US National COVID Cohort Collaborative. JAMA Netw. Open 4, e2116901 (2021).
Galanter, W. et al. Predicting clinical outcomes among hospitalized COVID-19 patients using both local and published models. BMC Med. Inf. Decis. Mak. 21, 224 (2021).
Siemieniuk, R. A. et al. Drug treatments for covid-19: living systematic review and network meta-analysis. BMJ 370, m2980 (2020).
Gritti, G. et al. Siltuximab downregulates interleukin-8 and pentraxin 3 to improve ventilatory status and survival in severe COVID-19. Leukemia 35, 2710–2714 (2021).
Robinson, P. C. et al. The potential for repurposing anti-TNF as a therapy for the treatment of COVID-19. Med (N. Y) 1, 90–102 (2020).
Mahase, E. Covid-19: Anti-TNF drug adalimumab to be trialled for patients in the community. BMJ 371, m3847 (2020).
Savarapu P. et al. Aspirin use is associated with decreased mortality in patients with COVID-19: a systematic review and meta-analysis. medRxiv, 2021.2007.2008.21260236. Preprint at https://doi.org/10.1101/2021.07.08.21260236 (2021).
RECOVERY Collaborative Group, et al. Aspirin in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial. Lancet.399, 143–151 (2022).
Guisado-Vasco, P. et al. Clinical characteristics and outcomes among hospitalized adults with severe COVID-19 admitted to a tertiary medical center and receiving antiviral, antimalarials, glucocorticoids, or immunomodulation with tocilizumab or cyclosporine: A retrospective observational study (COQUIMA cohort). EClinicalMedicine 28, 100591 (2020).
Bischof, E., Siow, R. C., Zhavoronkov, A. & Kaeberlein, M. The potential of rapalogs to enhance resilience against SARS-CoV-2 infection and reduce the severity of COVID-19. Lancet Healthy Longev. 2, e105–e111 (2021).
Assarsson, E. et al. Homogenous 96-plex PEA immunoassay exhibiting high sensitivity, specificity, and excellent scalability. PLoS ONE 9, e95192 (2014).
Ge, S. X., Son, E. W. & Yao, R. iDEP: an integrated web application for differential expression and pathway analysis of RNA-Seq data. BMC Bioinforma. 19, 534 (2018).
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).
DeLong, E. R., DeLong, D. M. & Clarke-Pearson, D. L. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44, 837–845 (1988).
The authors would like to thank all the patients, volunteers, and the healthcare co-workers from Allergy and Immunology Section-HMC, and Dr. Mohamed G.H. Mohamedali, Mr. Hassen Maatoug, and Mr. Ahmed Soliman from Hezm Mebairek General Hospital-HMC for developing disposable racks for samples transportation, tubes labeling, blood collection, and handling. We thank the support provided by Qatar University Biomedical Research Centre, Biosafety Level 3, and Associate Professor Hadi M. Yassine (M.Sc., Ph.D.). We also acknowledge the help of the Anti-Doping Lab-Qatar (ADLQ) and Qatar Red Crescent (QRC) for recruiting control samples. This work was supported by a grant fund from Hamad Medical Corporation (fund number MRC-05-003) and core funding from Qatar Biomedical Research Institute (QBRI).
The authors declare no competing interests.
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Al-Nesf, M.A.Y., Abdesselem, H.B., Bensmail, I. et al. Prognostic tools and candidate drugs based on plasma proteomics of patients with severe COVID-19 complications. Nat Commun 13, 946 (2022). https://doi.org/10.1038/s41467-022-28639-4
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