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. 2024 Feb 24;27(3):109330.
doi: 10.1016/j.isci.2024.109330. eCollection 2024 Mar 15.

Soluble ACE2 correlates with severe COVID-19 and can impair antibody responses

Mikhail Lebedin  1   2 , Christoph Ratswohl  1   3 , Amar Garg  4 , Marta Schips  4 , Clara Vázquez García  1   2 , Lisa Spatt  1 , Charlotte Thibeault  5 , Benedikt Obermayer  6 , January Weiner  6 , Ilais Moreno Velásquez  7 , Cathrin Gerhard  1 , Paula Stubbemann  5 , Leif-Gunnar Hanitsch  5 , Tobias Pischon  2   7   8 , Martin Witzenrath  5   9   10 , Leif Erik Sander  5   9   11 , Florian Kurth  5   9 , Michael Meyer-Hermann  4   12 , Kathrin de la Rosa  1   2   11
Affiliations

Soluble ACE2 correlates with severe COVID-19 and can impair antibody responses

Mikhail Lebedin et al. iScience. .

Abstract

Identifying immune modulators that impact neutralizing antibody responses against severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) is of great relevance. We postulated that high serum concentrations of soluble angiotensin-converting enzyme 2 (sACE2) might mask the spike and interfere with antibody maturation toward the SARS-CoV-2-receptor-binding motif (RBM). We tested 717 longitudinal samples from 295 COVID-19 patients and showed a 2- to 10-fold increase of enzymatically active sACE2 (a-sACE2), with up to 1 μg/mL total sACE2 in moderate and severe patients. Fifty percent of COVID-19 sera inhibited ACE2 activity, in contrast to 1.3% of healthy donors and 4% of non-COVID-19 pneumonia patients. A mild inverse correlation of a-sACE2 with RBM-directed serum antibodies was observed. In silico, we show that sACE2 concentrations measured in COVID-19 sera can disrupt germinal center formation and inhibit timely production of high-affinity antibodies. We suggest that sACE2 is a biomarker for COVID-19 and that soluble receptors may contribute to immune suppression informing vaccine design.

Keywords: Human specimen; Immune response; In silico biology; Properties of biomolecules.

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Conflict of interest statement

The Max Delbrück Center for Molecular Medicine in the Helmholtz Association and the Charité - Universitätsmedizin Berlin have filed three patent applications in connection with this work on which M.L., F.K., L.E.S., and K.D.L.R. (EP21155584.2); C.V.G. and K.D.L.R. (EP22164013.9); and M.L., C.R., C.V.G., and K.D.L.R. (EP21194414.5) are inventors.

Figures

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Graphical abstract
Figure 1
Figure 1
Circulating a-sACE2 correlates with mortality in COVID-19 (A and B) Concentration of a-sACE2 in (A) 82 severe and (B) 45 moderate patients up to 50 days post-symptom onset (PSO). Patients who donated blood more than once are selected here, WHO severity class corresponds to the highest severity level that a patient reached over the whole disease course. Black lines represent median values for the selected patients calculated as a trend over the whole course with five days binning; healthy donors (HD) median sACE2 level over the whole recruitment period is depicted in blue. (C) Levels of a-sACE2 in healthy donors (151 samples, one sample per donor, no time-based selection performed), COVID-19 patients (717 samples from 295 patients), patients with commonly acquired pneumonia (CAP, 69 samples from 25 patients), and persons with self-reported history of diabetes mellitus and/or myocardial infarction (DM/MI, 91 samples, one sample per donor, NAKO study). WHO severity class is assessed at the time of sampling. Rec denotes recovered COVID-19 patients. For COVID-19 and CAP patients, a mean level within a time frame 10–20 days PSO is depicted. Time frame selection is based on the a-sACE2 level change over the disease course and samples availability, depicted in (A) and (B). Minimum (Min), maximum (Max), and median values by patient/donor group are shown. (D) Receiver operating characteristic (ROC) curves for predicting death in all patients. Area under curve (AUC) calculated for maximum a-sACE2-concentrations within indicated days post-hospital admission. (E and F) Cox regression models with two-sided 95% confidence interval for a-sACE2 and IL-6 showing overall survival probability based on maximum marker concentrations within 15 days post-admission (gray area). N = 12 patients in (E) and (F) are chosen independently based on a-sACE2 and IL-6 level and do not represent the same group. Statistical analyses by two-sided Mann–Whitney test (∗∗∗∗p < 0.0001, ∗∗∗p < 0.001, ∗∗p < 0.01, and ∗p < 0.05; ns, not significant). In (A–C), sample mean is calculated from technical duplicates. Boxplots depict median +/− interquartile range. Whisker length is 1.5 interquartile ranges.
Figure 2
Figure 2
A large fraction of serum sACE2 is inactive and binds the SARS-CoV-2 spike (A) ACE2 was pulled down from indicated volumes of serum from patients and HDs using RBD-coupled beads. Recombinant ACE2 (rACE2) was loaded as control. Markers (M) in kDa. ∗Unspecific bands. (B) Specificity confirmed by a mutant RBD (A475R/G496R) lacking ACE2 binding and by an HIV gp140 control. (C) Percent inhibition (%) of 50 ng/mL rACE2 after addition to serum plotted against a-sACE2. Significance indicates a likelihood ratio test to compare a two-degree polynomial model with linear regression for moderate or severe COVID-19. (D) Activity of rACE2 spike-in (%) determined in serum fractions collected after ultrafiltration with indicated cut-off values. (E) Determination of a-sACE2 in indicated reciprocal serum dilutions. (F) Pearson correlation with a 95% confidence interval of a-sACE2 in undiluted samples and maximum a-sACE2 detected after serum dilution. (C), (E), and (F) include measurements of repeated longitudinal blood drawings. For gel source data, see Figure S11.
Figure 3
Figure 3
Serology suggests that sACE2 impairs generation of antibodies preventing RBD-ACE2 interaction (A) Scheme depicting SARS-CoV-2 spike epitope masking by sACE2. (B) Serum IgG binding to RBD (ED50, serum dilution corresponding to 50% of maximum binding activity) of HDs (blue, N = 151), moderate (yellow, N = 120) and severe (red, N = 198) patients collected >20 PSO. Patients are stratified into the respective WHO class at the time of sampling. Statistical analyses by two-sided Mann–Whitney test (∗∗∗∗p < 0.0001, ∗∗∗p < 0.001, ∗∗p < 0.01, and ∗p < 0.05; ns, not significant). Boxplots depict median +/− interquartile range. Whisker length is 1.5 interquartile ranges. (C) Blocking of ACE2-RBD interaction (BD50, serum dilution corresponding to 50% of maximum blocking activity) plotted against a-sACE2 for moderate and (D) severe COVID-19. Data for individual samples are shown; samples drawn after 15 days PSO are selected. Spearman correlation performed with a 95% confidence interval. (E) ED50 of anti-RBD IgG plotted against blocking of ACE2-binding (BD50). (F) ACE2 competition, (G) anti-RBD IgG, and (H) anti-nucleocapsid IgG confirmed for two sample groups with high (gray, N = 12) versus low (black line, N = 5) RBD titers. Positive and negative controls in blue and red, respectively. (I) Structural depiction of the RBD bound to CC12.1 and P2B-2F6. PDB: 6XDG, 7BWJ, and 6XC2. (J) RBD binding of recombinant antibodies in competition with class 1 nAb CC12.1 and class 2 nAb P2B-2F6. (K) Localization of class 1 and 2 abrogating RBD mutations F456A and E484K. ACE2 footprint shown in blue. (L) RBD high versus low binding sera cleared by a pull-down with RBD WT, RBD-F456A, RBD-E484K, and MERS-CoV RBD as control. Remaining IgG binding to RBD and ACE2 competition is depicted in %.
Figure 4
Figure 4
Soluble ACE2 interferes with the germinal center reaction in silico (A) Scheme depicting the germinal center (GC) response: GC reaction starts with an influx of low-affinity seeder cells. These cells are in a pro-apoptotic state and must acquire survival signals by antigen acquisition and T follicular helper cell (Tfh cell) signaling. Ag binding probability (PAg) is proportional to the mutational distance of the BCR to the Ag (MutDist, see STAR Methods and supplemental information). The Tfh cells preferentially provide signal to B cells that acquire larger amounts of antigen. Some of the selected B cells exit the GC and become output cells, whereas the rest recycle back and proliferate with mutations, resulting in daughter cells of higher affinity. These new cells again compete for survival signals, resulting in progressive B cell affinity increase. Serum sACE2 and the antibodies produced by output B cells can feedback to the GC by making antigen acquisition harder (mechanisms in B and C). (B) Epitope masking mechanism (scenario A): sACE2 and antibodies can bind to the antigen and mask it, thus lowering the amount of antigen available for B cells. (C) Epitope masking and mean field Ab-B cell competition mechanism (scenario B): BCR probability of binding the unmasked Ag is reduced depending on the average affinity of sACE2 and endogenous Abs (ACE2/Ab affinity). (D–K) Simulation results for different sACE2 concentrations (0, 1, 10, and 50 nM constant concentration in D–G; 0, 0.05, 0.23 nM constant concentration and 0.05–0.23 nM linear increase in H–K): IP (D, H) and GC size (E, I) with scenario A; IP (F, J) and GC size (G, K) with mean field Ab-B cell competition. (L–S) Simulation results for different sACE2 affinities (0.475, 1.9, and 3.8 nM in L–O; 5, 25, 125 nM in P–S): IP (L, P) and GC size (M, Q) with epitope masking only; IP (N, R) and GC size (O, S) with scenario B. Mean (continuous lines) and standard deviation (shaded area) of simulations for a total of 20 simulated GCs are shown. Schemes (A–C) are created with BioRender.
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