Plasma Lectin Pathway Complement Proteins in Patients With COVID-19 and Renal Disease

Abstract

We do not understand why non-white ethnicity and chronic kidney disease increase susceptibility to COVID-19. The lectin pathway of complement activation is a key contributor to innate immunity and inflammation. Concentrations of plasma lectin pathway proteins influence pathway activity and vary with ethnicity. We measured circulating lectin proteins in a multi-ethnic cohort of chronic kidney disease patients with and without COVID19 infection to determine if lectin pathway activation was contributing to COVID19 severity. We measured 11 lectin proteins in serial samples from a cohort of 33 patients with chronic kidney impairment and COVID19. Controls were single plasma samples from 32 patients on dialysis and 32 healthy individuals. We demonstrated multiple associations between recognition molecules and associated proteases of the lectin pathway and COVID-19, including COVID-19 severity. Some of these associations were unique to patients of Asian and White ethnicity. Our novel findings demonstrate that COVID19 infection alters the concentration of plasma lectin proteins and some of these changes were linked to ethnicity. This suggests a role for the lectin pathway in the host response to COVID-19 and suggest that variability within this pathway may contribute to ethnicity-associated differences in susceptibility to severe COVID-19.

Keywords: COVID-19; chronic kidney disease; complement; coronavirus; lectin.

Figure 1

Lectin pathway protein levels associate with COVID-19 severity in patients with chronic kidney impairment. (A) Schematic depicting blood sample collection in relation to symptom onset and disease severity. Each row represents one patient. Patients with severe COVID-19 infection are in the left panel, and those with non-severe COVID-19 infection in the right panel. The Y-axis also shows patient ethnicity. Boxed diagonal black lines represent hospital admissions. Two patients (C37 and C16) were hospitalised before developing COVID-19 symptoms. Sampling times are shown with black circles. Black crosses mark patient deaths. Coloured bars show when patients met criteria for severe (red) and non-severe (blue) COVID-19. (B) Lectin protein levels in 118 samples from 33 patients with COVID-19. 39 samples were from patients with severe (red triangles) and 79 samples were from patients with non-severe (blue triangles) COVID-19 at sampling. Controls are 32 dialysis patients without COVID-19 (dialysis control cohort, grey squares) and 32 healthy individuals (healthy control cohort, grey circles). (C) CRP and d-dimer levels in patients with kidney disease and COVID-19. Lines and whiskers show the median and interquartile values. Differences between cohorts were calculated with a mixed model for repeated measures and adjusted for multiple comparisons as described in the methods. (D) Summary of significant associations identified.

Figure 2

Lectin pathway protein concentrations associate with COVID-19 from the first samples after diagnosis. (A) Lectin protein levels from first sample collected after COVID-19 diagnosis in 33 kidney disease patients, of whom 16 developed severe COVID-19 (red triangles) and 17 had non-severe disease (blue triangles). Controls are 32 haemodialysis patients without COVID-19 (grey squares). (B) CRP and d-dimer levels from first sample collected after COVID-19 diagnosis in patients with kidney disease and COVID-19. Lines and whiskers show the median and interquartile values. Differences between cohorts were calculated with a Kruskall-Wallis test and follow-up comparison of the mean rank of every column. P values were adjusted for multiple comparisons as described in the methods.

Figure 3

Changes in lectin pathway protein levels during COVID-19 are influenced by ethnicity. (A) Samples are grouped by patient self-reported White (circles), Black (squares) or Asian (triangles) ethnicity. 57 samples were collected from 19 White patients, 23 samples were collected from 10 Black patients, and 61 samples were collected from 26 Asian patients. COVID-19 status at sampling was negative (‘Neg’, grey), non-severe (‘Non-S’, blue) or severe (‘S’, red) at sampling. We did not detect differences for CL-L1, CL-K1 and MAp44 (data not shown). Lines and whiskers show the cohort median and interquartile values. Differences between cohorts were calculated with a mixed model for repeated measures and P values adjusted for multiple comparisons as described in the methods. (B) Prevalence of MBL deficiency, defined as plasma MBL level less than 100ng/ml, in each ethnicity and severity groups.

Figure 4

Associations between lectin protein levels and biomarkers of COVID-19 severity. (A) Heat map of correlations between lectin proteins and clinical biomarkers in 33 patients with COVID-19. Correlations calculated from 80 samples pairs for CRP, 79 for white cell count (WCC), 75 for neutrophil count and lymphocyte count, 33 for D-dimer, and 40 for troponin and ferritin. (B) Heat map of correlations that reached statistical significance after adjusting p-values for multiple analyses. Of these, six correlations had best fit line gradients that were significantly non-zero (C). Solid and dotted lines show the lines of best fit and 95% confidence intervals (95% CI). (D) Heat map of correlations between lectin protein levels in 32 healthy controls, 32 haemodialysis patients without COVID-19 (dialysis controls) and 118 samples from 33 patients with kidney impairment and COVID-19. Red stars mark correlations that reached statistical significance after adjusting for multiple analyses. Double red stars mark statistically significant correlations detected in more than one cohort. For the healthy controls and dialysis controls cohorts, we calculated Pearson correlations (R) on log-transformed data. For the COVID-19 cohort, we applied linear mixed models and repeated measures correlation technique (rmcorr) of log-transformed data to calculate correlations (R_rm) (23).