Abstract
Objectives
Complement activation has been implicated in COVID-19 pathogenesis. This study aimed to assess the levels of complement activation products and full-length proteins in hospitalized patients with COVID-19, and evaluated whether complement pathway markers are associated with outcomes.
Methods
Longitudinal measurements of complement biomarkers from 89 hospitalized adult patients, grouped by baseline disease severity, enrolled in an adaptive, phase 2/3, randomized, double-blind, placebo-controlled trial and treated with intravenous sarilumab (200 mg or 400 mg) or placebo (NCT04315298), were performed. These measurements were then correlated with clinical and laboratory parameters.
Results
All complement pathways were activated in hospitalized patients with COVID-19. Alternative pathway activation was predominant earlier in the disease course. Complement biomarkers correlated with multiple variables of multi-organ dysfunction and inflammatory injury. High plasma sC5b-9, C3a, factor Bb levels, and low mannan-binding lectin levels were associated with increased mortality. Sarilumab treatment showed a modest inhibitory effect on complement activation. Moreover, sera from patients spontaneously deposited C5b-9 complex on the endothelial surface ex vivo, suggesting a microvascular thrombotic potential.
Conclusion
These results advance our understanding of COVID-19 disease pathophysiology and demonstrate the importance of specific complement pathway components as prognostic biomarkers in COVID-19.
Keywords: Complement activation, COVID-19, Respiratory insufficiency, SARS-CoV-2
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), results in heterogeneous symptoms, ranging from mild disease to severe viral pneumonia with acute respiratory distress syndrome, which may require mechanical ventilatory support in some patients and can be fatal [1,2]. The exact pathogenesis of COVID-19 is not entirely understood but may include direct damage to vascular and epithelial tissues, activation of inflammatory cytokine cascades, and activation of thrombosis [3,4].
Evidence, including from murine studies on the related Middle East respiratory syndrome and severe acute respiratory syndrome, identified the role of coronaviruses in complement activation, which could be a key contributor to COVID-19 pathogenesis [[5], [6], [7], [8]]. SARS-CoV-2 and the resulting COVID-19 infection also display specific features, including multi-organ failure [6].
Complement activation plays a key role in innate immunity, recognizing and eliminating invading pathogens. There are three independently activated complement pathways, namely the classical pathway (CP), alternative pathway (AP), and lectin pathway (LP), which result in proteolytic processing of different complement components, including C3, C4, and C5 [9]. This stimulates phagocytic cells to clear pathogenic microorganisms and damaged cells, promotes inflammation, and activates the membrane attack complex (MAC) or C5b-9 complex, which can result in cell death and tissue damage. Dysregulation of immune responses, as well as complement and coagulation pathways, leads to inflammation and is implicated in the tissue damage observed in acute respiratory distress syndrome [5,[10], [11], [12]]. Furthermore, studies have shown that patients with COVID-19 demonstrated activation of the complement pathway, which was related to disease severity [13], a generalized thrombotic microvascular injury [10], and decreased levels of C3 plus high sC5b-9 levels, which were associated with poor prognosis and respiratory failure, respectively [14,15].
Complement pathway components may therefore provide potential prognostic biomarkers to identify the patients most likely to have severe COVID-19 with poor clinical outcomes. Furthermore, blockade of specific complement pathway components may be a potential therapeutic strategy for COVID-19 [16]. Recent therapeutic options in hospitalized patients with COVID-19 have included blocking cytokine interleukin (IL-) 6 to prevent the “cytokine storm” seen in some patients by using sarilumab or tocilizumab [17,18]. The complement pathway components targeted include C5 inhibitors (e.g., eculizumab and pozelimab), C3 inhibitors (e.g., AMY-101), and LP inhibitors (e.g., narsoplimab) [[19], [20], [21], [22], [23]].
This study comprehensively assessed the levels of complement activation products and full-length proteins in hospitalized patients with varying COVID-19 disease severity. The study also evaluated whether complement pathway markers are associated with poor outcomes in hospitalized COVID-19 patients, including mortality and the requirement for prolonged supplemental oxygen. Moreover, we assessed the thrombotic potential of COVID-19 patient sera ex vivo using pulmonary arterial endothelial cells [24].
1. Methods
1.1. Study design and participants
We included 89 hospitalized patients grouped by baseline disease severity who enrolled in an adaptive, phase 2/3, randomized, double-blind, placebo-controlled trial. Subjects aged ≥18 years, hospitalized with laboratory-confirmed SARS-CoV-2 infection (within 2 weeks of randomization) and COVID-19 pneumonia requiring supplemental oxygen or assisted ventilation, were treated with intravenous (IV) sarilumab (200 mg or 400 mg) or placebo (NCT04315298), as previously described [25]. Local institutional review boards or ethics committees at each center oversaw trial conduct and documentation. All patients provided written informed consent. We also studied 56 healthy control subjects who consented to research, enrolled in a phase 1 healthy volunteer study (NCT03115996).
Sera were collected from control and hospitalized patients with COVID-19 for cell-based deposition assays. Plasma was evaluated for complement pathway protein analysis at baseline, Day 4, and Day 7, with limited samples available on Days 15 and 29.
1.2. Complement analyses
Multiple complement biomarkers were measured in serum in controls and patients with COVID-19 using commercially available enzyme-linked immunosorbent assay kits (C1Q, C3, C4, C5 complement factor B [CFB], complement factor H [CFH], C4BP [Abcam], C3a, C4a, C5a, sC5-9, AP biomarker factor Bb [Bb; Quidel, San Diego, USA], MBL [Thermo Fisher Scientific, Waltham, USA], and MASP2 [Hycult Biotech, Wayne, PA, USA]), following the manufacturer's instructions (Fig. 1 ).
Fig. 1.
(a) Schematic showing the measured complement components. Boxplots of 14 complement biomarkers from (b) the common pathway, (c) the CP/LP pathway, and (d) the alternative pathway for COVID-19 patients on study Day 1 (pre-dose), Day 4, and Day 7, and control subjects (n = 56) for reference, versus disease severity. Disease severity is noted by dot color and shape. Median values and samples sizes are shown above and below each boxplot, respectively. AP, alternative pathway; Bb, complement factor Bb; C1q, complement component 1q; C3, complement component 3; C4, complement component 4; C4BP, complement component 4 binding protein; C5, complement component 5; CFB, complement factor B; CFH, complement factor H; COVID-19, coronavirus disease 2019; CP, classical pathway; D, day; IV, intravenous; LP, lectin pathway; MASP2, mannan-binding lectin serine protease 2; MBL, mannan-binding lectin; MSOD, multisystem organ dysfunction; PRE, pre-dose; sC5b-9, soluble complement component 5b-9.
1.3. C5b-9 deposition assay and immunostaining
Serum-complement deposition assay was initially performed using 20 COVID-19 patient samples and 20 healthy donor samples. Eighty-eight serum samples (average age 58 years; 16 severe, 51 critical, 21 multiple system organ dysfunction [MSOD]) from patients with COVID-19 and 20 healthy donors (average age 55 years) were used as additional samples to confirm the observed increase in C5b-9 deposition in COVID-19 when compared to healthy controls.
Human pulmonary microvascular endothelial cells (PromoCell, Heidelberg, Germany) were plated at 20,000 cells per well in 96-well plates in PromoCell C22020 growth media and incubated overnight at 37 °C, 5% CO2. The complete growth medium was discarded and replaced with 50% COVID-19 patient serum or healthy donor serum diluted in 0.5% bovine serum albumin-Dulbecco's modified Eagle's medium in the presence or absence of 1 mg/mL REGN3918 (pozelimab, a C5 blocking fully human monoclonal antibody) or isotype control. Human C5 depleted serum (Quidel) was used as a negative control. After 4 h of incubation at 37 °C, 5% CO2 supernatants were removed from each well. The cells were fixed with 4% paraformaldehyde for 30 min at room temperature, then immunostained with primary rabbit polyclonal anti-human C5b-9 antibody (Abcam: dilution, 1:100) at 4 °C overnight. Cells were washed three times with phosphate-buffered saline with Tween-20, then incubated with secondary donkey anti-rabbit conjugated to Alexa Fluor 488 (Thermo Fisher Scientific); nuclei were counterstained with 4′,6-diamidino-2-phenylindole. Images were captured on an Opera Phenix® High-Content Screening System (PerkinElmer, Waltham, USA) using a 20x objective, with 49 sites per well, and fluorescence was quantified using the Harmony® high-content analysis software (PerkinElmer).
1.4. Statistical analyses
Descriptive statistics are reported as median (interquartile range) for continuous variables and frequency (%) for categorical variables. Baseline complement biomarker levels across disease strata (severe, critical, and MSOD) were compared using Kruskal Wallis and post-hoc Dunn tests. Wilcoxon rank-sum tests were used to compare control subjects and patients with COVID-19. Spearman rank correlation (rho; ρ) and Wilcoxon rank-sum tests were used to assess relationships between one complement biomarker and another and demographic, laboratory, and clinical outcomes on Days 1, 4, and 7 (limited data on Day 29) in patients with COVID-19.
Pharmacodynamic effects of sarilumab (200 mg IV [n = 34] or 400 mg IV [n = 40]) relative to placebo (n = 15) on complement biomarkers were assessed using Wilcoxon rank-sum tests of percent change from baseline on Days 4 and 7. Due to limited data, the sarilumab 200 mg (n = 34) and 400 mg (n = 40) IV treatment arms were pooled.
A Type-I error rate of α = 0.05 was used as the threshold for all statistical tests, with false discovery rate (FDR) correction using the Benjamini-Hochberg method. Nominal p-values are reported and indicated when a test meets the FDR-adjusted threshold (¶). Analyses were conducted using R version 3.6.1.
2. Results
2.1. Study patient demographics, disease severity, and outcomes
Details of the original study population have been previously reported (NCT04315298) [25]. In summary, the adaptive, phase 2/3, randomized, double-blind, placebo-controlled trial enrolled patients hospitalized with COVID-19 who were stratified by disease severity: severe, critical, and MSOD. Patients with severe COVID-19 required low-flow supplemental oxygen. Patients with critical COVID-19 required supplemental oxygen by nonrebreather mask or high-flow nasal device, noninvasive ventilation, or invasive mechanical ventilation and were admitted to the intensive care unit (ICU). Patients with evidence of MSOD required the use of vasopressors, extracorporeal life support, or renal replacement therapy.
The post-hoc complement biomarker analysis included 89 patients hospitalized with COVID-19 (17 severe, 51 critical, 21 MSOD) from the original trial population; 74.2% were male, 40.4% were White, and the median age was 59 years. In this sample, a median time of 2 days elapsed between a positive test result and study enrollment, and a median time of 7 days elapsed between the onset of pneumonia and study enrollment. Overall, 60.7% of patients required invasive mechanical ventilation, with use highest in the MSOD (95.2%) and critical (64.7%) groups. These two groups also had higher concentrations of inflammatory markers (IL-6 and c-reactive protein [CRP]) and viral load relative to the severe group. A comprehensive summary of baseline clinical and laboratory data and patient clinical outcomes is summarized in Supplementary Table 1.
We also studied 56 control subjects enrolled in a phase 1 healthy volunteer study (NCT03115996); 45% were male, 80% were White, and the median age was 34.5 years.
2.2. Complement biomarkers in healthy controls versus patients with COVID-19
Multiple complement activation products were robustly elevated in patients with COVID-19 compared with healthy controls, suggesting activation of all three complement pathways. A few full-length proteins were also differentially modulated in patients with COVID-19 (Fig. 1, Supplementary Fig. 1 and Supplementary Table 2). Nominal p-values are reported, and it is indicated when a test meets the FDR-adjusted threshold (¶).
There was a significant elevation in complement activation products and full-length proteins in patients with COVID-19 compared with controls, in the common pathway (C3, C3a, and C5a; p < 0.0001¶ at Days 1, 4, 7, and 29, sC5b-9 and C5; p < 0.0001¶ at Days 1, 4, and 7), CP (C1q; p = 0.0072 at Day 4, and p = 0.0003¶ at Day 7), LP (mannan-binding lectin [MBL]; p < 0.03 at Days 1, 4, 7, and 29, mannan-binding lectin serine protease 2 [MASP2] and C4a; p < 0.0001¶ at Days 1, 4, and 7, and p < 0.0001¶ at Day 29 for C4a only), AP, (Bb; p < 0.0001¶ at Days 1, 4, and 7), and pathway inhibitors (CFH; p < 0.0001¶ at Days 1, 4, and 7 and p = 0.0131 at Day 29, and C4BP; p < 0.03 at Days 4, 7, and 29) (Supplementary Table 2). The levels of AP-specific full-length protein, CFB, were significantly lower in patients with COVID-19 compared with controls on Days 4, 7, and 29. However, there was no significant difference in the CP/LP-specific full-length protein C4 on Days 1, 4, and 7. The ratios of split product to full-length proteins were still significantly higher in patients with COVID-19, suggesting disproportionately higher activation.
There were no significant differences in complement activation biomarkers among the three COVID-19 severity categories, except for C4a/C4, CFB, and C3 (p < 0.05), which were nominally associated with increasing disease severity (Supplementary Table 3, Fig. 1, and Supplementary Fig. 1). These tests did not reach FDR-adjusted thresholds. Nonsignificant trends also emerged for elevated C4a (p = 0.07) and MASP2 (p = 0.06) with increasing disease severity.
2.3. Correlation of complement biomarkers with each other and with demographic, clinical, and laboratory variables
Correlations between complement activation biomarkers for control subjects and patients with COVID-19 on Days 1, 4, and 7 are shown in Fig. 2 . The AP biomarker Bb was strongly correlated with common pathways split products C3a (ρ = 0.62; p < 0.0001¶), C5a (ρ = 0.44; p < 0.0001), and sC5b-9 (ρ = 0.33; p < 0.01) at Day 1. At Days 4 and 7, both AP biomarker Bb and CP/LP biomarker C4a were correlated with the common pathway split products. Therefore, AP activation could be predominant in the earlier course of the disease. Interestingly, both negative regulators, CFH and C4BP, were correlated with each other at Days 1, 4, and 7.
Fig. 2.
Spearman correlation matrices of complement biomarkers for (a) control subjects on study Day 1, (b) COVID-19 patients on study Day 1, (c) percent change from baseline on study Day 4, and (d) percent change from baseline on study Day 7. Spearman rho (ρ) correlation coefficients are shown. AP, alternative pathway; Bb, complement factor Bb; C1q, complement component 1q; C3, complement component 3; C4, complement component 4; C4BP, complement component 4 binding protein; C5, complement component 5; CFB, complement factor B; CFH, complement factor H; COVID-19, coronavirus disease 2019; CP, classical pathway; FDR, false discovery rate; LP, lectin pathway; MASP2, mannan-binding lectin serine protease 2; MBL, mannan-binding lectin; sC5b-9, soluble complement component 5b-9. Values shown are Spearman rho (ρ) correlation coefficients. ∗ indicates nominal statistical significance (p < 0.05) and † indicates statistical significance remains after FDR correction.
2.3.1. Demographic variables
CFB protein was negatively correlated with age at Day 1 (ρ = −0.32; p < 0.01), and the split products C3a (ρ = 0.35; p < 0.001) and sC5b-9 (ρ = 0.30; p < 0.01) were positively correlated with age at Day 7 (Supplementary Table 4). The C5a and C5a/C5 ratios were consistently higher in Black or African American patients compared with White patients at Days 1, 4, and 7 (p < 0.0001¶).
2.3.2. Clinical variables
Complement activation biomarkers were correlated with multiple variables associated with clinical outcomes, as well as organ injury and inflammation.
The net C5 and CFB activation negatively correlated with days from positive test or symptom duration (Table 1 ). The sC5b-9 levels were significantly higher at Day 4 (p < 0.01) and Day 7 (p < 0.001) in patients who died compared with those who survived, and were consistently higher across all time points in patients who did not show clinical improvement compared with those who did. Patients who died had significantly lower MBL levels (p < 0.05) across all time points compared with those who survived, and MBL levels were also inversely correlated with time to oxygen improvement (Fig. 3 , Table 1, and Supplementary Fig. 2). The levels of C3a and Bb were significantly higher at Day 7 (p < 0.001 and p < 0.05, respectively) in patients who died compared with those who survived (Table 1 and Supplementary Fig. 2).
Table 1.
Association of complement biomarkers with clinical variables in patients with COVID-19 on study Days 1, 4, and 7.
| Common pathway |
CP/LP-specific |
AP-specific |
CP-specific |
LP-specific |
Inhibitors |
||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| C5 | sC5b-9 | C5a | C3 | C3a | C4 | C4a | CFB | Bb | C1q | MBL | MASP2 | CFH | C4BP | ||
| Day 1 | |||||||||||||||
| Symptoms | Days between positive test and enrollment | 0.25a | −0.11 | −0.27a | 0.04 | 0.17 | −0.21 | 0.13 | 0.04 | 0.01 | 0.17 | 0.08 | 0.12 | 0.15 | 0.30a |
| Duration of symptoms prior to enrollment | 0.10 | −0.14 | −0.30b | −0.12 | −0.03 | −0.18 | −0.15 | −0.23a | −0.22a | 0.20 | 0.14 | −0.06 | −0.01 | 0.16 | |
| Recovery | All-cause mortalityˆ | a | |||||||||||||
| Clinical improvementˆ | a | ||||||||||||||
| Lung function | Days of hypoxemia | −0.01 | 0.08 | 0.19 | 0.06 | 0.23a | −0.22a | 0.21 | 0.02 | 0.17 | −0.14 | 0.01 | 0.06 | 0.08 | −0.19 |
| Time to clinical improvement | −0.04 | 0.19 | 0.09 | 0.16 | 0.27a | 0.02 | 0.12 | 0.01 | 0.11 | −0.05 | −0.21 | 0.17 | 0.16 | −0.10 | |
| ICU treatmentˆ | a | b | |||||||||||||
| Duration of ventilation | 0.04 | 0.18 | 0.14 | 0.09 | 0.32b | −0.15 | 0.30b | 0.13 | 0.24a | −0.18 | −0.08 | 0.13 | 0.07 | −0.10 | |
| Invasive ventilationˆ | b | ||||||||||||||
| Time to O2 improvement | −0.02 | 0.19 | 0.04 | 0.16 | 0.07 | 0.01 | 0.09 | 0.15 | 0.12 | −0.02 | −0.22a | 0.23a | 0.14 | −0.01 | |
| Day 4 | |||||||||||||||
| Symptoms | Days between positive test and enrollment | 0.14 | −0.23a | −0.15 | 0.30a | −0.07 | −0.14 | 0.07 | 0.12 | −0.26a | 0.16 | 0.08 | −0.03 | −0.03 | 0.04 |
| Duration of symptoms prior to enrollment | 0.01 | −0.15 | −0.19 | 0.17 | 0.16 | −0.11 | 0.01 | −0.08 | −0.05 | 0.13 | 0.15 | −0.07 | −0.13 | 0.07 | |
| Recovery | All-cause mortalityˆ | b | a | a | a | ||||||||||
| Clinical improvementˆ | c | a | |||||||||||||
| Lung function | Days of hypoxemia | 0.00 | 0.18 | 0.35c | 0.01 | 0.07 | −0.16 | 0.24a | 0.21a | 0.29b | −0.02 | 0.00 | 0.05 | 0.14 | −0.14 |
| Time to clinical improvement | 0.09 | 0.33b | 0.25a | −0.09 | 0.13 | −0.01 | 0.21 | 0.02 | 0.30b | 0.19 | −0.24a | 0.23a | 0.10 | −0.02 | |
| ICU treatmentˆ | |||||||||||||||
| Duration of ventilation | 0.05 | 0.25a | 0.34b | 0.04 | 0.00 | −0.20 | 0.27a | 0.20 | 0.32b | 0.08 | −0.12 | 0.08a | 0.14 | −0.08 | |
| Invasive ventilationˆ | a | ||||||||||||||
| Time to O2 improvement | 0.03 | 0.24a | 0.19 | −0.18 | 0.05 | 0.07 | 0.08 | 0.01 | 0.25a | 0.19 | −0.35b | 0.24 | 0.02 | 0.04 | |
| Day 7 | |||||||||||||||
| Symptoms | Days between positive test and enrollment | 0.20 | −0.02 | −0.05 | 0.46d | −0.04 | −0.07 | 0.12 | 0.12 | −0.05 | 0.05 | 0.09 | 0.07 | 0.15 | −0.17 |
| Duration of symptoms prior to enrollment | 0.11 | 0.05 | −0.09 | 0.09 | 0.23a | −0.06 | −0.04 | 0.10 | −0.06 | −0.16 | 0.22a | −0.03 | −0.05 | −0.09 | |
| Recovery | All-cause mortalityˆ | c | c | a | ∗ | ||||||||||
| Clinical improvementˆ | c | b | b | ∗ | |||||||||||
| Lung function | Days of hypoxemia | 0.16 | 0.23a | 0.25a | 0.02 | 0.06 | −0.20 | 0.23a | 0.26a | 0.25a | 0.04 | 0.02 | 0.03 | 0.13 | −0.27a |
| Time to clinical improvement | 0.08 | 0.49d | 0.24∗ | 0.03 | 0.36c | 0.04 | 0.24a | 0.24a | 0.41d | −0.01 | −0.21 | 0.14 | −0.02 | −0.27a | |
| ICU treatmentˆ | a | a | a | ||||||||||||
| Duration of ventilation | 0.11 | 0.36c | 0.29b | 0.11 | 0.11 | −0.15 | 0.32b | 0.31b | 0.34b | 0.10 | −0.10 | 0.06 | 0.12 | −0.27a | |
| Invasive ventilationˆ | a | b | |||||||||||||
| Time to O2 improvement | −0.10 | 0.29b | 0.06 | 0.02 | 0.16 | 0.07 | 0.15 | 0.10 | 0.20 | 0.06 | −0.32b | 0.15 | −0.07 | −0.12 | |
| Ratios |
||||||
|---|---|---|---|---|---|---|
| C3a/C3 | C5a/C5 | sC5b-9/C5 | Bb/CFB | C4a/C4 | ||
| Day 1 | ||||||
| Symptoms | Days between positive test and enrollment | 0.09 | −0.38c | −0.26a | 0.00 | 0.21 |
| Duration of symptoms prior to enrollment | −0.01 | −0.35b | −0.23a | −0.15 | −0.05 | |
| Recovery | All-cause mortalityˆ | |||||
| Clinical improvementˆ | ||||||
| Lung function | Days of hypoxemia | 0.18 | 0.16 | 0.04 | 0.15 | 0.30a |
| Time to clinical improvement | 0.21 | 0.08 | 0.20 | 0.09 | 0.05 | |
| ICU treatmentˆ | c | |||||
| Duration of ventilation | 0.25a | 0.11 | 0.12 | 0.19 | 0.36 | |
| Invasive ventilationˆ | d | |||||
| Time to O2 improvement | 0.01 | 0.05 | 0.19 | 0.05 | 0.01 | |
| Day 4 | ||||||
| Symptoms | Days between positive test and enrollment | −0.22a | −0.20 | −0.37c | −0.29b | 0.17 |
| Duration of symptoms prior to enrollment | 0.05 | −0.20 | −0.15 | 0.02 | 0.03 | |
| Recovery | All-cause mortalityˆ | a | ||||
| Clinical improvementˆ | c | a | ||||
| Lung function | Days of hypoxemia | 0.07 | 0.37c | 0.24a | 0.11 | 0.23a |
| Time to clinical improvement | 0.19 | 0.23a | 0.34b | 0.23a | 0.17 | |
| ICU treatmentˆ | ||||||
| Duration of ventilation | −0.02 | 0.32b | 0.28b | 0.15 | 0.30b | |
| Invasive ventilationˆ | ∗ | |||||
| Time to O2 improvement | 0.14 | 0.15 | 0.22a | 0.20 | 0.03 | |
| Day 7 | ||||||
| Symptoms | Days between positive test and enrollment | −0.25a | −0.09 | −0.14 | −0.12 | 0.17 |
| Duration of symptoms prior to enrollment | 0.16 | −0.12 | −0.01 | −0.13 | 0.04 | |
| Recovery | All-cause mortalityˆ | b | c | |||
| Clinical improvementˆ | b | c | a | |||
| Lung function | Days of hypoxemia | 0.07 | 0.23a | 0.15 | 0.11 | 0.30b |
| Time to clinical improvement | 0.36c | 0.24a | 0.43d | 0.29b | 0.22a | |
| ICU treatmentˆ | a | |||||
| Duration of ventilation | 0.08 | 0.28b | 0.29b | 0.19 | 0.37c | |
| Invasive ventilationˆ | ||||||
| Time to O2 improvement | 0.16 | 0.10 | 0.30b | 0.17 | 0.10 | |
For continuous variables, spearman rho (ρ) and nominal significance level are shown for each correlation. For categorical variables (ˆ), only nominal significance levels are shown for Wilcoxon rank-sum tests. ap < 0.05, bp < 0.01, cp < 0.001, dp < 0.0001.
AP, alternative pathway; Bb, complement factor Bb; C1q, complement component 1q; C3, complement component 3; C4, complement component 4; C4BP, complement component 4 binding protein; C5, complement component 5; CFB, complement factor B; CFH, complement factor H; COVID-19, coronavirus disease 2019; CP, classical pathway; CRP, C-reactive protein; ICU, intensive care unit; IL-6, interleukin-6; LDH, lactate dehydrogenase; LP, lectin pathway; MASP2, mannan-binding lectin serine protease 2; MBL, mannan-binding lectin; NLR, neutrophil-lymphocyte ratio; sC5b-9, soluble complement component 5b-9.
Fig. 3.
Clinical outcomes for COVID-19 patients on study Day 1 (pre-dose), Day 4, and Day 7, and control subjects (n = 56) for reference, for complement factor levels of (a) sC5b-9 and (b) MBL. COVID-19, coronavirus disease 2019; D, day; IV, intravenous; MBL, mannan-binding lectin; PRE, pre-dose; sC5b-9, soluble complement component 5b-9. P-values are shown for biomarker comparisons at Day 1, Day 4, and Day 7 between patients who survived and died using Wilcoxon rank-sum tests.
Patients who required treatment in the ICU had high C3a (p < 0.05) and C4a (p < 0.01) levels at Day 1. Patients who required invasive ventilation had high C4a (p < 0.01) and C4a/C4 levels (p < 0.0001¶) at Day 1 (Table 1). The time to clinical improvement and duration of ventilation was positively correlated with Bb, C4a, and sC5b-9 at Day 7 (Table 1 and Supplementary Fig. 3).
2.3.3. Laboratory variables
The common pathway activation marker C3a and the CP/LP activation marker C4a were positively correlated with the renal function marker blood urea nitrogen (ρ = 0.48; p < 0.01 and ρ = 0.37; p < 0.05, respectively; Table 2 ), and serum creatinine (ρ = 0.35; p < 0.001 and ρ = 0.31; p < 0.05, respectively) at Day 1. At later time points (Days 4 and 7), the AP-specific biomarker Bb showed a positive correlation with serum creatinine (ρ = 0.51; p < 0.0001¶ and ρ = 0.40; p < 0.001, respectively).
Table 2.
Association of complement biomarkers with laboratory variables in patients with COVID-19 on study Days 1, 4, and 7.
| Common pathway |
CP/LP-specific |
AP-specific |
CP-specific |
LP-specific |
Inhibitors |
||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| C5 | sC5b-9 | C5a | C3 | C3a | C4 | C4a | CFB | Bb | C1q | MBL | MASP2 | CFH | C4BP | ||
| Day 1 | |||||||||||||||
| Kidney function | Urea nitrogen | 0.06 | 0.09 | 0.02 | 0.33a | 0.48b | −0.09 | 0.37a | −0.08 | 0.13 | 0.17 | 0.02 | 0.28 | 0.12 | 0.10 |
| Creatinine | −0.01 | −0.01 | 0.15 | 0.20 | 0.35c | 0.04 | 0.31b | −0.04 | 0.20 | 0.06 | −0.12 | 0.27a | 0.04 | 0.06 | |
| Coagulation | Platelets | 0.29b | −0.15 | −0.12 | 0.20 | 0.08 | −0.02 | 0.05 | 0.02 | −0.16 | 0.31b | 0.14 | 0.13 | 0.20 | 0.23a |
| Injury & inflammation | IL-6 | −0.13 | 0.20 | 0.21 | −0.08 | 0.38c | −0.27a | 0.13 | 0.06 | 0.41c | −0.33b | −0.09 | −0.06 | −0.25a | −0.23a |
| CRP | −0.10 | 0.16 | 0.18 | 0.06 | 0.52d | −0.20 | 0.27a | 0.07 | 0.35b | −0.30b | −0.03 | 0.14 | −0.18 | −0.12 | |
| NLR | 0.27a | 0.27a | −0.18 | −0.03 | 0.15 | −0.14 | 0.16 | −0.07 | −0.20 | −0.01 | −0.20 | 0.48d | −0.07 | 0.09 | |
| LDH | 0.13 | 0.45c | 0.31a | 0.00 | 0.03 | −0.17 | −0.10 | 0.20 | 0.11 | −0.10 | −0.10 | 0.05 | 0.01 | −0.19 | |
| Virology | Viral load | −0.04 | −0.05 | 0.03 | 0.12 | −0.16 | 0.33b | 0.26a | −0.04 | −0.08 | −0.09 | −0.20 | 0.26a | 0.01 | −0.15 |
| Day 4 | |||||||||||||||
| Kidney function | Urea nitrogen | 0.35a | 0.10 | 0.18 | 0.35a | 0.19 | 0.09 | 0.12 | −0.02 | 0.08 | 0.38a | −0.11 | 0.11 | 0.29 | −0.05 |
| Creatinine | 0.11 | 0.19 | 0.22a | 0.15 | 0.24a | 0.01 | 0.27a | 0.00 | 0.51d | 0.28b | −0.22a | 0.12 | 0.18 | 0.06 | |
| Coagulation | Platelets | 0.12 | −0.18 | −0.13 | −0.05 | −0.10 | 0.07 | −0.20 | 0.06 | −0.13 | 0.16 | 0.28b | 0.02 | 0.09 | 0.22a |
| Injury & inflammation | IL-6 | −0.16 | 0.09 | 0.41c | 0.05 | 0.15 | −0.20 | −0.10 | −0.12 | 0.28a | 0.19 | −0.12 | 0.18 | −0.05 | −0.15 |
| CRP | 0.05 | 0.12 | 0.33b | 0.20 | 0.32b | −0.07 | 0.26a | 0.19 | 0.40c | −0.09 | 0.00 | 0.02 | 0.00 | −0.17 | |
| LDH | 0.20 | 0.41c | 0.44c | −0.22 | −0.01 | −0.16 | 0.11 | 0.02 | 0.12 | 0.13 | 0.04 | −0.07 | 0.01 | 0.19 | |
| Virology | Viral load | −0.08 | −0.05 | −0.14 | 0.22 | −0.06 | 0.12 | 0.12 | −0.09 | −0.01 | 0.04 | 0.01 | −0.03 | 0.01 | −0.28a |
| Day 7 | |||||||||||||||
| Kidney function | Urea nitrogen | 0.09 | 0.26 | 0.13 | 0.05 | 0.08 | 0.01 | −0.07 | −0.32a | 0.28 | 0.09 | 0.02 | −0.22 | 0.06 | 0.15 |
| Creatinine | 0.03 | 0.21a | 0.19 | 0.05 | 0.16 | 0.09 | 0.14 | −0.10 | 0.40c | 0.14 | −0.22a | 0.00 | −0.04 | 0.03 | |
| Coagulation | Platelets | 0.16 | −0.47d | −0.22a | 0.17 | −0.27a | 0.06 | −0.26a | −0.15 | −0.25a | 0.21 | 0.18 | 0.09 | 0.08 | 0.17 |
| Injury & inflammation | IL-6 | 0.01 | 0.18 | 0.28a | −0.02 | 0.22 | 0.05 | 0.16 | 0.00 | 0.33b | 0.11 | −0.17 | −0.05 | −0.05 | −0.20 |
| CRP | 0.20 | 0.34b | 0.39c | 0.34b | 0.57d | 0.04 | 0.38c | 0.42c | 0.56d | −0.09 | −0.02 | 0.32b | 0.20 | −0.05 | |
| LDH | −0.01 | 0.24a | 0.23 | −0.10 | 0.25a | −0.03 | 0.20 | 0.14 | 0.30a | −0.04 | −0.08 | 0.02 | −0.11 | −0.04 | |
| Virology | Viral load | 0.11 | 0.04 | 0.07 | 0.03 | 0.12 | 0.22 | 0.18 | 0.17 | 0.19 | 0.02 | −0.15 | 0.18 | 0.36a | −0.06 |
| Ratios |
||||||
|---|---|---|---|---|---|---|
| C3a/C3 | C5a/C5 | sC5b-9/C5 | Bb/CFB | C4a/C4 | ||
| Day 1 | ||||||
| Kidney function | Urea nitrogen | 0.39a | 0.02 | −0.01 | 0.20 | 0.42b |
| Creatinine | 0.28b | 0.15 | 0.02 | 0.23a | 0.16 | |
| Coagulation | Platelets | −0.06 | −0.23a | −0.32b | −0.18 | 0.10 |
| Injury & inflammation | IL-6 | 0.38c | 0.22a | 0.23a | 0.37c | 0.29b |
| CRP | 0.47d | 0.19 | 0.22 | 0.29b | 0.45d | |
| NLR | 0.14 | −0.26a | 0.10 | −0.20 | 0.21 | |
| LDH | 0.02 | 0.24 | 0.32a | 0.02 | 0.12 | |
| Virology | Viral load | −0.18 | 0.03 | 0.03 | −0.08 | −0.04 |
| Day 4 | ||||||
| Kidney function | Urea nitrogen | −0.03 | 0.08 | −0.14 | 0.08 | −0.07 |
| Creatinine | 0.11 | 0.21 | 0.16 | 0.48d | 0.18 | |
| Coagulation | Platelets | −0.04 | −0.15 | −0.28b | −0.15 | −0.23a |
| Injury & inflammation | IL-6 | 0.13 | 0.45c | 0.26a | 0.38b | 0.04 |
| CRP | 0.21 | 0.33b | 0.13 | 0.26a | 0.31b | |
| LDH | 0.17 | 0.41c | 0.34b | 0.12 | 0.26a | |
| Virology | Viral load | −0.24 | −0.14 | 0.01 | −0.01 | 0.00 |
| Day 7 | ||||||
| Kidney function | Urea nitrogen | 0.06 | 0.13 | 0.24 | 0.36a | −0.15 |
| Creatinine | 0.17 | 0.18 | 0.20 | 0.43d | 0.00 | |
| Coagulation | Platelets | −0.34b | −0.27a | −0.55d | −0.17 | −0.28b |
| Injury & inflammation | IL-6 | 0.30a | 0.30b | 0.20 | 0.32b | 0.10 |
| CRP | 0.39c | 0.35b | 0.23 | 0.31b | 0.37b | |
| LDH | 0.29a | 0.28a | 0.25a | 0.30a | 0.20 | |
| Virology | Viral load | 0.01 | 0.02 | −0.05 | 0.15 | 0.13 |
For continuous variables, spearman rho (ρ) and nominal significance level are shown for each correlation. ap < 0.05, bp < 0.01, cp < 0.001, dp < 0.0001.
AP, alternative pathway; Bb, complement factor Bb; C1q, complement component 1q; C3, complement component 3; C4, complement component 4; C4BP, complement component 4 binding protein; C5, complement component 5; CFB, complement factor B; CFH, complement factor H; COVID-19, coronavirus disease 2019; CP, classical pathway; CRP, c-reactive protein; IL-6, interleukin-6; LDH, lactate dehydrogenase; LP, lectin pathway; MASP2, mannan-binding lectin serine protease 2; MBL, mannan-binding lectin; NLR, neutrophil-lymphocyte ratio; sC5b-9, soluble complement component 5b-9.
Platelet number was used to determine the correlation between complement activation components and coagulation. Platelet number was negatively correlated with activation biomarkers from all complement pathways at Day 7, particularly sC5b-9 (ρ = −0.47; p < 0.0001¶; Table 2). Similarly, the inflammatory markers CRP, neutrophil-lymphocyte ratio, and IL-6 were associated with all activation biomarkers specifically at later time-points (Day 7; Table 2 and Supplementary Figs 4, 5, and 6). Common pathway sC5b-9 levels were consistently positively correlated with injury biomarker lactate dehydrogenase (LDH) at all time points. CP/LP-specific C4 levels at Day 1 (ρ = 0.33; p < 0.01) and pathway inhibitor CFH levels at Day 7 (ρ = 0.36; p < 0.05) were positively correlated with viral load.
2.4. Impact of sarilumab treatment and steroid use on complement pathway
The effect of sarilumab treatment on complement activation was evaluated at Days 4 and 7. Sarilumab treatment significantly reduced C3a and C3 levels, but not C3a/C3 ratio relative to placebo (Supplementary Table 5). A significant effect of sarilumab on CFB, C5a, Bb, and MASP2 levels was also observed (Supplementary Table 5 and Supplementary Fig. 7). Although the levels of C3a, C3, C5a, CFB, Bb, and MASP2 decreased with sarilumab treatment, they did not reach the levels observed in healthy controls. Levels of Bb were significantly lower at Day 1 in patients with steroid use after sarilumab treatment was initiated (Supplementary Table 6 and Supplementary Fig. 8). The effect of steroid use in placebo- and sarilumab-treated groups did not have any observed effects on specific complement pathway components (Supplementary Table 6 and Supplementary Fig. 8). Levels of MBL appeared to be lower in those using steroids in the sarilumab treatment group.
2.5. MAC deposition
Levels of MAC deposition on pulmonary endothelial cells ex vivo were determined. Serum from patients with COVID-19 induced higher MAC deposition compared to that from healthy controls (Fig. 4 and Supplementary Fig. 9). However, there was no difference in MAC deposition with sera from patients with different disease severity. Treatment with pozelimab, an anti-C5 monoclonal antibody, significantly reduced MAC deposition induced by COVID-19 or healthy serum.
Fig. 4.
Increased membrane attack complex deposition from COVID-19 serum versus healthy control serum on primary human pulmonary endothelial cells. C5, complement component 5; COVID-19, coronavirus disease 2019; Ctl, control; depl, depleted.
3. Discussion
Complement pathway activation plays a vital role in the pathogenesis of COVID-19, with dysregulation leading to inflammation and tissue damage [3,5,6]. In this study, we comprehensively evaluated the levels of complement activation products and full-length proteins from the three pathways (CP, LP, and AP) in hospitalized patients with varying COVID-19 severity.
Robust complement activation via all pathways was observed in patients with COVID-19, with multiple biomarkers elevated compared to healthy controls. There were no significant differences in complement activation biomarkers among the three COVID-19 severity categories, except for C4a/C4, CFB, and C3. The elevated C4a and C4a/C4 ratios in patients in the critical and MSOD categories suggest higher CP/LP activation in these groups. The high C5a levels were associated with inflammation in COVID-19 patients with acute respiratory distress syndrome, suggesting a C5a-C5aR1 axis in myeloid cell infiltration, and subsequent lung inflammation and endothelialitis [26]. We observed significantly higher C5a levels in hospitalized COVID-19 patients compared with healthy controls. COVID-19 disproportionally affects Black/African American people [27]. Interestingly, in our study, we observed consistently higher C5a levels in Black or African American people compared with White people.
We investigated whether complement pathway markers are associated with poor outcomes in hospitalized COVID-19 patients, including a requirement for prolonged supplemental oxygen, and mortality. Complementary to the above findings, patients requiring treatment in the ICU or invasive ventilation also had higher C4a levels. Here, for the first time, we showed that patients with COVID-19 who died had lower MBL levels. MBL has been shown to inhibit SARS-CoV-1 entry into cells and serves as a first line of defense [28]. Whether MBL is required to inhibit SARS-COV2 entry is unknown. Further studies to investigate MBL polymorphisms in these patients are warranted to understand whether there is any association, as low MBL levels have previously been associated with a specific polymorphism [28]. Low MBL levels in our study may also reflect net consumption due to the activation of LP. Recent data showed that circulatory properdin protein levels were lower in patients with severe COVID-19, whereas properdin gene expression was significantly increased [29]. The authors hypothesized that the tissue deposition of properdin could contribute to lower circulating levels of properdin. Hence, it is also possible that the lower levels of MBL in our study could result from tissue deposition. Interestingly, patients who died also had higher sC5b-9 and C3a. However, Bb levels, but not C4a levels, were higher in patients who died. C3 has been shown to play a role in the recovery of patients with COVID-19 [30] with low levels of C3 and C4 linked to increased mortality risk [31,32]. In this study, we observed increased levels of C3a and sC5b-9, but not C4, and there was no correlation between C4 and all-cause mortality. C3a and C5a can recruit monocytes and macrophages that secrete cytokines like IL-6, contributing to the cytokine storm in some patients with COVID-19 [33].
Complement activation biomarkers (such as C4a and Bb) were correlated with variables/markers of multi-organ dysfunction and injury inflammatory biomarkers. Early in infection, the CP/LP activation marker C4a was positively correlated with kidney function markers, while the AP-specific biomarker Bb was associated at later time points. Additionally, patient serum induced higher MAC deposition, and the sC5b-9 biomarker was consistently associated with LDH level, suggesting MAC-mediated cell injury and a hyper-functional complement system in patients. The higher MAC deposition on the endothelial cells ex vivo with patient sera could also reflect the thrombotic potential [24]. The inflammatory markers IL-6 and CRP were associated with all the complement activation biomarkers, particularly at later time-points. Elevated plasma levels of IL-6 and CRP are associated with clinical worsening and mortality [34,35,25].
No major impact of sarilumab treatment on complement activation was observed. Patients who were on sarilumab treatment showed significantly lower C3a and C3 levels, but not C3a/C3 ratio levels, suggesting that the decrease in C3a levels could be secondary to decreased C3 expression and may not be a direct effect on C3 activation [36]. Our data also demonstrated significant, yet modest, decreases in C5a, CFB, Bb, and MASP2 levels. However, it remains unclear how sarilumab impacts complement activation and this requires further investigation. A previous study has shown that COVID-19 symptoms improved following treatment with sarilumab, corresponding to a rapid decrease in CRP levels [37]. Taken together, the beneficial effect of sarilumab could partly be attributed to mitigating C3a and C5a-induced inflammation [37].
There are a few limitations to our study. Patient numbers were low for each of the COVID-19 disease severity categories. We also did not evaluate mild/moderate patients in our cohort to compare the complement activation with severe categories of patients. Though we could get some insight through correlations with the symptom duration, more studies need to be conducted at the proximal time points to understand the peak complement activation status in the disease course.
In conclusion, our analyses confirm the activation of multiple complement pathways in patients with COVID-19. While no significant differences were observed in complement activation biomarkers across severity (except for C4a/C4, CFB, and C3), these biomarkers were correlated with multiple variables of multi-organ dysfunction and inflammatory injury. The observation that the patients who died had lower MBL levels is exciting and warrants further investigation. As we have shown in this study, serial measurements are required to comprehensively understand the status of complement activation and its association with clinical outcomes and laboratory variables. These results further advance understanding of the disease pathophysiology and may potentially help develop prognostic biomarkers and new therapeutic strategies for severe COVID-19.
Author contributions
KD and LM conceived the concept. KD, LM, AB, SH, PJE, SM, and MW contributed to the study design, analysis plan, and implementation of the research. CH and QR contributed to sample preparation and laboratory testing. AB contributed to primary data acquisition and cleaning for the COVID-19 clinical trial and study samples. PJE, MW, and SCH had access to all data and verified the data and statistical analysis. All authors participated in data analysis, interpretation, manuscript review, and editing.
Funding
Regeneron and Sanofi supported the collection of COVID-19 samples and clinical data. Regeneron and Sanofi were also involved in the study design; collection, analysis, and interpretation of data; the writing of this article; and in the decision to submit this article for publication. Certain aspects of this project have been funded in whole or in part with federal funds from the Department of Health and Human Services, Office of the Assistant Secretary for Preparedness and Response, Biomedical Advanced Research and Development Authority, under OT number: HHSO100201700020C.
Data sharing
Qualified researchers can submit a proposal for access to individual patient or aggregate level data from a Regeneron-sponsored clinical trial through Vivli (https://vivli.org/).
Ethical approval statement
Local institutional review boards or ethics committees at each center oversaw trial conduct and documentation. All patients provided written informed consent. We also studied 56 healthy control subjects who consented to research, enrolled in phase 1 healthy volunteer study (NCT03115996).
Declaration of competing interest
All the authors are employees of Regeneron Pharmaceuticals, Inc. and own stock or stock options.
Acknowledgments
We thank the study participants, their families, and the investigational site members involved in this trial; Georgia Bellingham and Lisa Boersma for operational support for virology testing; the Biomedical Advanced Research and Development Authority; Sanofi; the members of the independent data and safety monitoring committee; Brian Head, PhD, and Caryn Trbovic, PhD, from Regeneron Pharmaceuticals; and Prime, Knutsford, UK, for assistance with the development of the manuscript.
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.micinf.2022.105081.
Appendix A. Supplementary data
The following is the Supplementary data to this article:
References
- 1.Guan W.J., Ni Z.Y., Hu Y., Liang W.H., Ou C.Q., He J.X., et al. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med. 2020;382:1708–1720. doi: 10.1056/NEJMoa2002032. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Huang C., Wang Y., Li X., Ren L., Zhao J., Hu Y., et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395:497–506. doi: 10.1016/S0140-6736(20)30183-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Blanco-Melo D., Nilsson-Payant B.E., Liu W.C., Uhl S., Hoagland D., Moller R., et al. Imbalanced host response to SARS-CoV-2 drives development of COVID-19. Cell. 2020;181:1036–1045. doi: 10.1016/j.cell.2020.04.026. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Loo J., Spittle D.A., Newnham M. COVID-19, immunothrombosis and venous thromboembolism: biological mechanisms. Thorax. 2021;76:412–420. doi: 10.1136/thoraxjnl-2020-216243. [DOI] [PubMed] [Google Scholar]
- 5.Gralinski L.E., Sheahan T.P., Morrison T.E., Menachery V.D., Jensen K., Leist S.R., et al. Complement activation contributes to severe acute respiratory syndrome coronavirus pathogenesis. mBio. 2018;9:e01753–e01818. doi: 10.1128/mBio.01753-18. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Java A., Apicelli A.J., Liszewski M.K., Coler-Reilly A., Atkinson J.P., Kim A.H., et al. The complement system in COVID-19: friend and foe? JCI Insight. 2020;5 doi: 10.1172/jci.insight.140711. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Jiang Y., Zhao G., Song N., Li P., Chen Y., Guo Y., et al. Blockade of the C5a-C5aR axis alleviates lung damage in hDPP4-transgenic mice infected with MERS-CoV. Emerg Microb Infect. 2018;7:77. doi: 10.1038/s41426-018-0063-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Perico L., Benigni A., Casiraghi F., Ng L.F.P., Renia L., Remuzzi G. Immunity, endothelial injury and complement-induced coagulopathy in COVID-19. Nat Rev Nephrol. 2021;17:46–64. doi: 10.1038/s41581-020-00357-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Merle N.S., Church S.E., Fremeaux-Bacchi V., Roumenina L.T. Complement system part I - molecular mechanisms of activation and regulation. Front Immunol. 2015;6:262. doi: 10.3389/fimmu.2015.00262. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Magro C., Mulvey J.J., Berlin D., Nuovo G., Salvatore S., Harp J., et al. Complement associated microvascular injury and thrombosis in the pathogenesis of severe COVID-19 infection: a report of five cases. Transl Res. 2020;220:1–13. doi: 10.1016/j.trsl.2020.04.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Tang N., Li D., Wang X., Sun Z. Abnormal coagulation parameters are associated with poor prognosis in patients with novel coronavirus pneumonia. J Thromb Haemostasis. 2020;18:844–847. doi: 10.1111/jth.14768. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Zhou F., Yu T., Du R., Fan G., Liu Y., Liu Z., et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020;395:1054–1062. doi: 10.1016/S0140-6736(20)30566-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.de Nooijer A.H., Grondman I., Janssen N.A.F., Netea M.G., Willems L., van de Veerdonk F.L., et al. Complement activation in the disease course of coronavirus disease 2019 and its effects on clinical outcomes. J Infect Dis. 2021;223:214–224. doi: 10.1093/infdis/jiaa646. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Fang S., Wang H., Lu L., Jia Y., Xia Z. Decreased complement C3 levels are associated with poor prognosis in patients with COVID-19: a retrospective cohort study. Int Immunopharm. 2020;89 doi: 10.1016/j.intimp.2020.107070. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Holter J.C., Pischke S.E., de Boer E., Lind A., Jenum S., Holten A.R., et al. Systemic complement activation is associated with respiratory failure in COVID-19 hospitalized patients. Proc Natl Acad Sci U S A. 2020;117:25018–25025. doi: 10.1073/pnas.2010540117. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Risitano A.M., Mastellos D.C., Huber-Lang M., Yancopoulou D., Garlanda C., Ciceri F., et al. Complement as a target in COVID-19? Nat Rev Immunol. 2020;20:343–344. doi: 10.1038/s41577-020-0320-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Lescure F.-X., Honda H., Fowler R.A., Lazar J.S., Shi G., Wung P., et al. Sarilumab in patients admitted to hospital with severe or critical COVID-19: a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet Respir Med. 2021;9:522–532. doi: 10.1016/S2213-2600(21)00099-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Luo P., Liu Y., Qiu L., Liu X., Liu D., Li J. Tocilizumab treatment in COVID-19: a single center experience. J Med Virol. 2020;92:814–818. doi: 10.1002/jmv.25801. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Araten D.J., Belmont H.M., Schaefer-Cutillo J., Iyengar A., Mattoo A., Reddy R. Mild clinical course of COVID-19 in 3 patients receiving therapeutic monoclonal antibodies targeting C5 complement for hematologic disorders. Am J Case Rep. 2020;21 doi: 10.12659/AJCR.927418. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Mahajan R., Lipton M., Broglie L., Jain N.G., Uy N.S. Eculizumab treatment for renal failure in a pediatric patient with COVID-19. J Nephrol. 2020;33:1373–1376. doi: 10.1007/s40620-020-00858-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Mastellos D.C., Pires da Silva B.G.P., Fonseca B.A.L., Fonseca N.P., Auxiliadora-Martins M., Mastaglio S., et al. Complement C3 vs C5 inhibition in severe COVID-19: early clinical findings reveal differential biological efficacy. Clinical immunology (Orlando, Fla. 2020;220 doi: 10.1016/j.clim.2020.108598. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Rambaldi A., Gritti G., Mico M.C., Frigeni M., Borleri G., Salvi A., et al. Endothelial injury and thrombotic microangiopathy in COVID-19: treatment with the lectin-pathway inhibitor narsoplimab. Immunobiology. 2020;225 doi: 10.1016/j.imbio.2020.152001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Latuszek A., Liu Y., Olsen O., Foster R., Cao M., Lovric I., et al. Inhibition of complement pathway activation with pozelimab, a fully human antibody to complement component C5. PLoS One. 2020;15 doi: 10.1371/journal.pone.0231892. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Bettoni S., Galbusera M., Gastoldi S., Donadelli R., Tentori C., Sparta G., et al. Interaction between multimeric von Willebrand factor and complement: a fresh look to the pathophysiology of microvascular thrombosis. J Immunol. 2017;199:1021–1040. doi: 10.4049/jimmunol.1601121. [DOI] [PubMed] [Google Scholar]
- 25.Sivapalasingam S., Lederer D.J., Bhore R., Hajizadeh N., Criner G., Hosain R., et al. Efficacy and safety of sarilumab in hospitalized patients with COVID-19: a randomized clinical trial. Clin Infect Dis. 2022;75:e380–e388. doi: 10.1093/cid/ciac153. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Carvelli J., Demaria O., Vely F., Batista L., Chouaki Benmansour N., Fares J., et al. Association of COVID-19 inflammation with activation of the C5a-C5aR1 axis. Nature. 2020;588:146–150. doi: 10.1038/s41586-020-2600-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Centers for Disease Control and Prevention. Hospitalization and death by race/ethnicity. https://www.cdc.gov/coronavirus/2019-ncov/covid-data/investigations-discovery/hospitalization-death-by-race-ethnicity.html. (Accessed August 16, 2022).
- 28.Ip W.K., Chan K.H., Law H.K., Tso G.H., Kong E.K., Wong W.H., et al. Mannose-binding lectin in severe acute respiratory syndrome coronavirus infection. J Infect Dis. 2005;191:1697–1704. doi: 10.1086/429631. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Boussier J., Yatim N., Marchal A., Hadjadj J., Charbit B., El Sissy C., et al. Severe COVID-19 is associated with hyperactivation of the alternative complement pathway. J Allergy Clin Immunol. 2022;149:550–556 e2. doi: 10.1016/j.jaci.2021.11.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Xiao Y., Shi X., She Q., Chen Q., Pan H., Zhang J., et al. Exploration of turn-positive RT-PCR results and factors related to treatment outcome in COVID-19: a retrospective cohort study. Virulence. 2020;11:1250–1256. doi: 10.1080/21505594.2020.1816076. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Zhao Y., Nie H.X., Hu K., Wu X.J., Zhang Y.T., Wang M.M., et al. Abnormal immunity of non-survivors with COVID-19: predictors for mortality. Infect Dis Poverty. 2020;9:108. doi: 10.1186/s40249-020-00723-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Zinellu A., Mangoni A.A. Serum complement C3 and C4 and COVID-19 severity and mortality: a systematic review and meta-analysis with meta-regression. Front Immunol. 2021;12 doi: 10.3389/fimmu.2021.696085. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Chouaki Benmansour N., Carvelli J., Vivier E. Complement cascade in severe forms of COVID-19: recent advances in therapy. Eur J Immunol. 2021;51:1652–1659. doi: 10.1002/eji.202048959. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Lavillegrand J.R., Garnier M., Spaeth A., Mario N., Hariri G., Pilon A., et al. Elevated plasma IL-6 and CRP levels are associated with adverse clinical outcomes and death in critically ill SARS-CoV-2 patients: inflammatory response of SARS-CoV-2 patients. Ann Intensive Care. 2021;11:9. doi: 10.1186/s13613-020-00798-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Boyapati A., Wipperman M.F., Ehmann P.J., Hamon S., Lederer D.J., Waldron A., et al. Baseline severe acute respiratory syndrome viral load is associated with coronavirus disease 2019 severity and clinical outcomes: post hoc analyses of a phase 2/3 trial. J Infect Dis. 2021;224:1830–1838. doi: 10.1093/infdis/jiab445. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Katz Y., Revel M., Strunk R.C. Interleukin 6 stimulates synthesis of complement proteins factor B and C3 in human skin fibroblasts. Eur J Immunol. 1989;19:983–988. doi: 10.1002/eji.1830190605. [DOI] [PubMed] [Google Scholar]
- 37.Montesarchio V., Parrela R., Iommelli C., Bianco A., Manzillo E., Fraganza F., et al. Outcomes and biomarker analyses among patients with COVID-19 treated with interleukin 6 (IL-6) receptor antagonist sarilumab at a single institution in Italy. J Immunotherapy Cancer. 2020;8 doi: 10.1136/jitc-2020-001089. [DOI] [PMC free article] [PubMed] [Google Scholar]
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