| Literature DB >> 35034207 |
Edward L G Pryzdial1,2,3, Edward M Conway4,5,6, Alexander Leatherdale1,7, Sophie Stukas2,8, Victor Lei1,7, Henry E West1,2, Christopher J Campbell9, Ryan L Hoiland10,11, Jennifer Cooper2,8, Cheryl L Wellington2,8, Mypinder S Sekhon12.
Abstract
Mechanisms underlying the SARS-CoV-2-triggered hyperacute thrombo-inflammatory response that causes multi-organ damage in coronavirus disease 2019 (COVID-19) are poorly understood. Several lines of evidence implicate overactivation of complement. To delineate the involvement of complement in COVID-19, we prospectively studied 25 ICU-hospitalized patients for up to 21 days. Complement biomarkers in patient sera and healthy controls were quantified by enzyme-linked immunosorbent assays. Correlations with respiratory function and mortality were analyzed. Activation of complement via the classical/lectin pathways was variably increased. Strikingly, all patients had increased activation of the alternative pathway (AP) with elevated levels of activation fragments, Ba and Bb. This was associated with a reduction of the AP negative regulator, factor (F) H. Correspondingly, terminal pathway biomarkers of complement activation, C5a and sC5b-9, were significantly elevated in all COVID-19 patient sera. C5a and AP constituents Ba and Bb, were significantly associated with hypoxemia. Ba and FD at the time of ICU admission were strong independent predictors of mortality in the following 30 days. Levels of all complement activation markers were sustained throughout the patients' ICU stays, contrasting with the varying serum levels of IL-6, C-reactive protein, and ferritin. Severely ill COVID-19 patients have increased and persistent activation of complement, mediated strongly via the AP. Complement activation biomarkers may be valuable measures of severity of lung disease and the risk of mortality. Large-scale studies will reveal the relevance of these findings to thrombo-inflammation in acute and post-acute COVID-19.Entities:
Keywords: Alternative pathway; COVID-19; Complement; Factor B; Hypoxemia; Innate immunity
Mesh:
Substances:
Year: 2022 PMID: 35034207 PMCID: PMC8761108 DOI: 10.1007/s00430-021-00725-2
Source DB: PubMed Journal: Med Microbiol Immunol ISSN: 0300-8584 Impact factor: 4.148
Fig. 1In vitro complement pathway activity in serum from critically ill COVID-19 patients and healthy general population controls. Wieslab solid-phase screening assays were conducted to compare the residual complement pathway activity in healthy control (●) and COVID-19 patient (○) sera. All samples were tested in duplicate to obtain a mean
Fig. 2Markers of complement pathway activation or complement proteins in serum from healthy general population controls and critically ill COVID-19 patients admitted to the ICU. In all experiments, samples were tested in duplicate to obtain a mean. Prior to calculating protein concentrations, absorbance values were corrected for background absorbance of the detection system. Closed (●) and open (○) circles represent data for healthy controls and COVID-19 patients, respectively. Statistical significance was determined using the Mann–Whitney U test. p values were calculated automatically by Prism 8
Fig. 3Serum Ba fragment and factor D in critically ill COVID-19 patients at the time of ICU admission separated by serum creatinine levels. Ba fragment (A) and factor D (B) concentrations in healthy general population controls compared to critically ill COVID-19 patients with clinically normal (≤ 100 mg/dL) or elevated (> 100 mg/dL) serum creatinine. Closed (●) circles, open (○) circles, and open diamonds (◊) represent data for healthy controls, COVID-19 patients, and patients with serum Cr > 100, respectively. Statistical significance was determined using the Mann–Whitney U test. P values were calculated automatically by Prism 8
Logistic regression model characteristics
| Odds ratio | Model performance | Goodness of fit | ||
|---|---|---|---|---|
| ROC AUC | Classification rate | Pseudo-R squared* | ||
| Complement analytes | ||||
| Ba | 4.90 (1.23–54.05) | 0.85 (0.69–1) | 79.17% | 0.250 |
| FD | 4.07 (0.97–24.85) | 0.88 (0.74–1) | 79.17% | 0.171 |
| C5a | 0.94 (0.84–0.99) | 0.88 (0.75–1) | 75% | 0.284 |
| sC5b-9 | 0.77 (0.51–1.02) | 0.74 (0.44–1) | 87.50% | 0.147 |
| C4d | 1.02 (0.99–1.05) | 0.75 (0.53–0.97) | 83.33% | 0.102 |
| Bb | 0.78 (0.38–1.29) | 0.60 (0.33–0.87) | 83.33% | 0.041 |
| FH | 1.00 (0.99–1.01) | 0.56 (0.20–0.96) | 83.33% | 0.022 |
| Reported biomarkers | ||||
| D-dimer | 0.99 (0.99–1.00) | 0.67 (0.35–0.98) | 83.33 | 0.008 |
| Cr | 1.01 (0.99–1.02) | 0.89 (0.75–1) | 83.33% | 0.154 |
| Inflammatory analytes | ||||
| TNF-α | 1.10 (0.90–1.37) | 0.66 (0.41–0.91) | 83.33% | 0.044 |
| IL-6 | 0.99 (0.98–1.00) | 0.5 (0.21–0.79) | 16.67% | 0.015 |
| CRP ( | 0.99 (0.96–1.01) | 0.65 (0.40–0.89) | 84.21% | 0.084 |
| Ferritin ( | 0.99 (0.99–1.00) | 0.55 (0.18–0.90) | 85% | 0.004 |
| Demographics | ||||
| Age | 1.06 (0.98–1.17) | 0.69 (0.47–0.91) | 83.33% | 0.094 |
| Sex | 0.50 (0.02–4.74) | 0.58 (0.27–0.88) | 83.33% | 0.016 |
| Respiratory status | ||||
| PaO2/FIO2 | 0.99 (0.98–1.01) | 0.5 (0.23–0.78) | 83.33% | 0.006 |
| Known comorbidities | ||||
| Hypertension† | 7.44 (0.35–156.29) | – | – | – |
| Diabetes | 9.00 (0.92–207.10) | 0.75 (0.48–1) | 83.33% | 0.165 |
| Obesity | 6.33 (0.21–194.40) | 0.60 (0.26–0.94) | 83.33% | 0.061 |
| Dyslipidemia | 1.22 (0.13–11.92) | 0.53 (0.21–0.84) | 83.33% | 0.002 |
| CKD | 1.33 (0.57–14.24) | 0.53 (0.20–0.85) | 83.33% | 0.002 |
Values in brackets are the 95% CI for the indicated measurement
CKD chronic kidney disease
*McFadden’s Pseudo-R squared was selected to calculate goodness of fit
†Could not be tested in logistic regression due to complete separation. Odds ratio was calculated using a 2 × 2 contingency table, and 1 was added to each cell to eliminate cells containing zeros
PaO2/FIO2 = partial pressure of arterial oxygen divided by the fraction of inspired oxygen
Fig. 4Serum concentrations of AP analytes FH, Ba, Bb, and FD throughout ICU stay for patients in which at least four time-points were available for testing. Patients who received the IL-6 receptor antagonist tocilizumab (B, C, E, F) were given a single dose of 400 mg on the day indicated. Shaded areas indicate the duration of daily corticosteroid administration for each patient where applicable. A Patient was treated with hydrocortisone on days 8–11, received 50 mg on day 13, and 25 mg on days 14–17. B Patient received 40 mg methylprednisolone daily from days 12–17 post-ICU admission. D patient received 100 mg hydrocortisone from days 12–16, 50 mg on day 17, and 100 mg from days 18–23. E Patient received hydrocortisone from days 2–6, day 8, and days 19–20. F Patient received hydrocortisone from days 7–16
Fig. 5Serum concentrations of terminal pathway analytes C5a and sC5b-9 throughout ICU stay for patients in which at least four time-points were available for testing. Patients who received the IL-6 receptor antagonist tocilizumab (B, C, E, F) were given a single dose of 400 mg on the day indicated. Shaded areas indicate the duration of daily corticosteroid administration for each patient where applicable. A Patient was treated with hydrocortisone on days 8–11, received 50 mg on day 13, and 25 mg on days 14–17. B patient received 40 mg methylprednisolone daily from days 12–17. D Patient received 100 mg hydrocortisone from days 12–16, 50 mg on day 17, and 100 mg from days 18–23. E Patient received hydrocortisone from days 2–6, day 8, and days 19–20. F Patient received hydrocortisone from days 7–16
Associations between PaO2/FIO2 and complement analytes
| PaO2/FIO2 vs | Spearman rho | 95% CI | |
|---|---|---|---|
| Bb | − 0.61 | − 0.82 to − 0.27 | 0.008 |
| C5a | − 0.53 | − 0.77 to − 0.16 | 0.028 |
| IL-6 | − 0.48 | − 0.74 to − 0.10 | 0.032 |
| Ba | − 0.48 | − 0.74 to − 0.09 | 0.032 |
| FD | − 0.41 | − 0.70 to − 0.01 | 0.064 |
| FH | − 0.17 | − 0.54 to 0.25 | 0.466 |
| sC5b-9 | 0.01 | − 0.40 to 0.41 | 0.971 |
| C4d | 0.18 | − 0.24 to 0.55 | 0.466 |
Analytes are ranked from top to bottom based on the strength of the association. Spearman’s correlation coefficient, 95% CI, and p values were calculated automatically by Prism 8. The Benjamini–Hochberg method was used to correct for multiple comparisons with the false discovery rate set to 5%. q values obtained from correcting for multiple comparisons are reported