key: cord-0960299-kyzku88p
authors: Holenya, P.; Lange, P. J.; Reimer, U.; Woltersdorf, W.; Panterodt, T.; Glas, M.; Wasner, M.; Eckey, M.; Drosch, M.; Hollidt, J.-M.; Naumann, M.; Kern, F.; Wenschuh, H.; Lange, R.; Schnatbaum, K.; Bier, F. F.
title: Peptide microarray based detection of antibody responses against SARS-CoV-2 species-specific epitopes in spike and nucleocapsid proteins with potential for diagnostic test development
date: 2020-11-27
journal: nan
DOI: 10.1101/2020.11.24.20216663
sha: 3bc57f6398788d3a21ad2ba051e4caee28b1cad8
doc_id: 960299
cord_uid: kyzku88p

Humoral immunity to the Severe Adult Respiratory Syndrome (SARS) Coronavirus (CoV)-2 is not well understood but may be a crucial factor of immune protection. The possibility of antibody cross-reactivity between SARS-CoV-2 and other human coronaviruses (HCoVs) would have important implications for immune protection but also for the development of specific diagnostic ELISA tests. Using peptide microarrays, n=24 patient samples and n=12 control samples were screened for antibodies against the entire SARS-CoV-2 proteome as well as the Spike (S), Nucleocapsid (N), VME1 (V), R1ab, and Protein 3a (AP3A) of the HCoV strains SARS, MERS, UC43 and 229E. While widespread cross-reactivity was revealed across several immune dominant regions of S and N, IgG binding to several SARS-CoV-2-derived peptides provided statistically significant discrimination between COVID-19 patients and controls. Selected target peptides may serve as capture antigens for future, highly COVID-19-specific diagnostic antibody tests.

The COVID-19 pandemic hit the world unprepared. At the time of writing global cases were in the millions and still rising [WHO, 2020] . Apart from social distancing, rapid testing of suspected cases and contacts is the key measure helping to contain the infection. The experience from many countries has shown that imprecise or incomplete knowledge of infection prevalence and viral transmission rates may result in a wrong assessment of the epidemiological situation, wrong predictions, and inadequate guidance and action with potentially grave consequences for societies and national economies. These observations underline the global need for reliable and highly specific tests.

SARS-CoV-2 infection is generally confirmed by RT-PCR on swabs taken from the nose and throat, however, virus load quickly becomes unmeasurable following the acute infection. Reliable serological monitoring will therefore be instrumental in determining the SARS-CoV-2 infection status of larger populations, determine existing immunity, and track local outbreaks [Pollán et al., 2020] . In the near future it may also greatly contribute to assessing vaccine-induced immunity at the population level. However, SARS-CoV-2 antibody testing is much more complex than appears at first glance. Currently available antibody tests are not very specific and may produce conflicting results [Krammer and Simon, 2020; Lisboa Bastos et al., 2020; Bond et al., 2020] . A plausible reason for that might be that antibody tests have generally been validated by samples from clinically symptomatic rather than mild or even asymptomatic cases. However, the reason for the overall unreliable performance of antibody tests is not fully understood. Interestingly, it has been suggested that, particularly in mild and asymptomatic cases, IgG responses may not be detectable until 3-4 weeks after infection [Fafi-Kremer et al., 2020] which may compound the issue.

A large number of studies have examined antibody responses to SARS-CoV-2 [Siracusano et al., 2020] . Most of these used full-length proteins as capture antigens and thus have not identified reactivity at the single epitope level. Only few references Farrera-Soler et al. 2020; Poh et al., 2020; Wang et al., 2020; Zhang1 et al., 2020] including preprints [Zhang2 et al., 2020; Smith et al., 2020; Dahlke et al., 2020; Ladner et al., 2020] provide more detailed information on antibody specificity. Most of these studies comprise small sample numbers (n ≤ 10) and only one of them [Ladner et al., 2020 ] examined cross-reactivity with other HCoVs despite in silico analysis indicating that significant cross-reactivity is to be expected [Ahmed et al., 2020; Grifoni et al., 2020] . 3

Serum samples from COVID-19 patients analyzed by peptide microarray technology exhibited high levels of antibody reactivity, whereas almost no reactivity was detected with control samples. Several immunodominant regions were identified. These were located primarily on the SARS2 S and N proteins (Figure 1, Supplementary Figures 1-6) . There was also strong IgG reactivity to several peptides derived from the SARS2 M, AP3A and R1AB proteins, however, immune dominance was less pronounced in these proteins (Supplementary Figures 3, 5 and 6) . All remaining SARS2 antigens demonstrated no or only minor immunogenicity in terms of IgG response.

For quantitative characterization of the identified immunodominant regions, responses in SARS-CoV-2 infected individuals were compared to those in the control group for each single peptide and for combinations of three, four and five peptides by the non-parametric

Wilcoxon Rank Sum test. Results for selected peptide hits and the best combinations thereof are presented in Figure 2 . Table 1 shows a selection of identified epitopes represented either by single peptides or by common core sequences derived from two or more overlapping peptides. To narrow down the identified immunodominant peptides to the potential immunodominant epitope sequences, the peptides with the highest accuracy scores (calculated as the sum of true positive and true negative divided by the total number of observations) were listed in the order of appearance in the corresponding antigens. If applicable, sequence overlaps were marked in red. A complete list of peptides showing the most significant discrimination between SARS-CoV-2 infected and the control group can be found in Supplementary Table 2 .

To evaluate the diagnostic potential of a peptide-based assay, a comparison with a commonly used diagnostic test utilizing a full-length viral antigen was performed. Figure 3 shows the quantitative results obtained for the SARS-CoV-2 infected group with a) the commercial ELISA test (EUROIMMUN) and b) the peptide microarray. Both assays demonstrated a strong correlation with a Spearman's rank correlation coefficient of 0.88.

Cross-reactivity with seasonal common cold coronaviruses experimentally confirmed 5 89

Antibody cross-reactivity between SARS-CoV-2 and seasonal common cold coronaviruses may be highly important with respect to the clinical course of COVID-19 and at the same time represents a challenge for the development of a specific test. We, therefore, analyzed Figures 1 and 3) . Most notably, peptides derived from the S protein of all viral strains showed clear signal patterns among SARS-CoV-2 positive patient samples, suggesting that this antigen contained cross-reactive determinants (Supplementary Figure 2) . IgG reactivity was directed at two sequence regions in particular (Figure 4) . Each of these regions was represented by overlapping peptides containing highly conserved cross-reactive epitopes (Figure 4 , right panel). One consensus motif, R0815S-IED-LF0823 (numbers referring to the location of the epitope in the SARS2 antigen) was present in all five viruses, and a second consensus motif, F1148--ELD--FKN1158 was found in the viruses SARS-CoV-2, SARS-CoV, MERS-Cov and HCoV-OC43, but not HCoV-229E.

To further analyze serologic cross-reactivity of SARS-CoV-2 positive patients against common cold coronaviruses we performed a gapless alignment of the N and S sequences from all coronaviruses ( Figure 5) . For each strain, this yielded the sequence regions that were complementary to the previously identified SARS-CoV-2 epitopes. Finally, for each of the amino acid residues of the thus identified sequences, the median signal of all overlapping peptides containing that residue was calculated and visualized by the same colour coding as in the previously presented heatmaps ( Figure 5 ). The immunodominant epitopes #6 and #7 of the N protein and #1 and #3 of the S protein of SARS-CoV-2 (see Ta Applying stringent bioinformatic criteria we identified several linear epitopes that were immunodominant among SARS-CoV-2 positive patients ( Table 1) . Previous analyses of antibody epitopes in SARS-CoV revealed that many such epitopes were indeed linear [Guo et al., 2004; Shichijo et al., 2004] . Interestingly, some immunodominant regions in the N and S proteins that have been previously reported in the literature correspond closely to epitopes #3 from N and #1 and #4 from S (Table 1) , respectively. This confirms our findings and illustrates the potential of the peptide microarrays for epitope identification. Longer sequence stretches encompassing some of the epitopes identified here have also been described in published manuscripts Farrera-Soler et al., 2020; Zhang1 et al., 2020] or in non-peer-reviewed preprint publications, however, without narrowing them down [Zhang2 et al., 2020; Smith et al., 2020; Dahlke et al., 2020] . With our comprehensive peptide library we further completed the list of the previously described immunodominant SARS-CoV-2 regions and specified the corresponding epitopes.

The most important question with respect to developing a reliable diagnostic test was, however, whether the IgG response to the identified SARS2-specific peptides can effectively distinguish COVID-19 patients and controls. This was indeed the case, as a comparison of serum reactivity in COVID-19 patients versus the control group, peptide by peptide, identified many peptides to which responses were significantly different between the groups (Table 1, Figure 2A ). The combination of selected peptides derived from different SARS-CoV-2 antigens further improved the discrimination between the sample groups as shown in Figure 2B , and also might increase sensitivity when applied in a diagnostic test.

As a further step to assess the diagnostic potential of the obtained data, the quantitative results obtained with the peptide microarrays were compared with those generated by a commercial ELISA test (Figure 3 ). We observed a very strong correlation what suggests that a combination of short linear synthetic peptides may be used for the development of a diagnostic test instead of full-length antigens. Moreover, as it was found that only a small proportion of all peptides forms B cell epitopes, the selection of peptides must not necessarily span the whole antigen, but might be limited to immunodominant regions identified by a peptide microarray screening.

Multiple studies utilizing in silico prediction of SARS-CoV-2 immunogenicity Yuan et al., 2020; Palm et al., 2020; Smith et al., 2020] postulated the existence of cross-reactive immunity between different coronaviruses, in particular between SARS-

CoV exist almost identically in SARS-CoV-2. Cross-reactive T cell epitopes were experimentally confirmed by several authors [Braun et al., 2020; Mateus et al., 2020] and some degree of B cell cross reactivity to HCoVs has been identified by western blot analysis of full length antigens [Bonifacius et al., 2020] . However, there is still a lack of experimental data identifying cross-reactive B cell epitopes. We found two immunodominant regions in the SARS-CoV-2 S protein (1148-FKEELDKYFKN-1158 and 0815-RSFIEDLLF-0823) which were highly cross-reactive with the prevalent seasonal "common cold" coron- perpetuity.

preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in

The copyright holder for this this version posted November 27, 2020. ; the common cold coronaviruses generate only a weak and transient humoral immune response and that reconvalescents from SARS1 -a virus that is structurally more closely related to SARS2 than the common cold coronaviruses -did not have SARS2 neutralization titers in plasma [Lv et al., 2020] . Of note, preexisting immunity could also be detrimental, as it may inhibit production of antibodies with certain specificity, i.e. a phenomenon called original antigenic sin. This phenomenon may be responsible for ineffective vaccine response to a recently developed Cytomegalovirus vaccine [Baraniak et al., 2019] . Even worse, existing antibodies could promote infection of target cells by facilitating viral uptake, a phenomenon called antibody-dependent enhancement [Fierz et al., 2020; Arvin et al., 2020] . Our results demonstrate that some of the patient sera, e.g. numbers SARS_20 to SARS_23, show stronger reactivity towards the HCoV-OC43 variant of the cross-reactive epitope II than to the respective SARS-CoV-2 epitope (Figure 4) . This is an interesting observation as there is no other logical explanation for this than that the antibody was made in response to the HCoV-OC43 virus in a previous HCoV-OC43 infection.

A number of additional peptides that led to strong IgG responses (Figure 4 ) support the notion of preexisting B cell immunity to SARS-CoV-2 in those exposed to other coronaviruses. It will be up to future studies with larger cohorts to determine whether preexisting immunity has an effect on disease severity.

While the physiological effects of serologic cross-reactivity require further studies, it is obvious that it makes the development of reliable antibody tests more difficult. Antibody based ELISA tests that use full-length conformational antigens or parts thereof as capture antigens might give false positive results in individuals with previous exposure to common cold coronaviruses. In this regard, the usage of the S protein antigen in a diagnostic test might be particularly problematic due to several known homologous regions between SARS-CoV-2 and other coronaviruses. The detailed analysis of IgG reactivity against SARS-CoV, MERS-CoV, HCoV-OC43 and HCoV-229E S and N proteins indeed revealed widespread cross-reactivity in several immunodominant regions (Figure 4) , which is most probably a consequence of sequence homology. However, the observed cross-reactivity could not be predicted by simple sequence alignment unless 100% homology was present.

With a view to develop a diagnostic test, our finding that epitopes #6 and #7 of N and #1 and #3 of S appear to provide a high specificity for SARS-CoV-2 ( perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted November 27, 2020. ;

In conclusion, our study experimentally confirms antibody cross-reactivity between SARS-CoV-2 and other HCoVs, but at the same time reveals antibody reactivity to several epitopes that are unique to SARS-CoV-2. These epitopes are of particular interest for the development of diagnostic tests. Additional, larger studies are required to confirm these results and accelerate the development of a sensitive and specific test for SARS-CoV-2 infection that we urgently need in order to deal with the current and possible future waves of this pandemic. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted November 27, 2020. ; https://doi.org/10.1101/2020.11.24.20216663 doi: medRxiv preprint The Spearman's correlation coefficient between the ELISA signal and the summed microarray signals for SARS-CoV-2 S and N peptides was calculated for sera from SARS-CoV-2 infected patients. In the ELISA assay, which used a recombinant S glycoprotein as capture antigen, the quotient of the extinction of the patient sample in comparison to the calibrator was used as net assay signal. The total microarray signals for each sample were calculated as a sum of all corresponding signal intensities above the upper 10 -16 % quantile of the noise distribution. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted November 27, 2020. ;

Selected peptides providing good separation between the groups are listed. Immunodominant epitopes (highlighted red) are derived from the sequences of (overlapping) peptides. 

Blood sampling. Following written informed consent peripheral blood samples were obtained by vene puncture / from periperal venous catheters. Multiple samples were collected into a Vacutainer Tube (Serum-separating tube) with a volume of 8 ml. Serum was collected after centrifugation, samples were allowed to clot for 1 hour, and the clot was removed by centrifugation. The samples were divided and stored in aliquots at −80°C until use.

from the upper respiratory tract using a nasopharyngeal (NP) with plastic by trained medical staff. In the case of the NP the swab was inserted deeply into the nostril until resistance was encountered and the depth was equal to the distance from the nose to the ears of the patients. The swab was gently rubbed against the nasopharynx and left there in order to absorb enough secret. Swabs were then immediately placed into a sterile transport tube. Samples were cooled and tested no more than 48 hours after collection. All proteins covered by the peptide library are listed in Supplementary Table 1 .

Peptide synthesis and microarray production. Peptides were synthesized and immobilized on peptide microarray slides as described previously [Stephenson et al., 2015] . In brief, the peptides were synthesized using SPOT synthesis [Wenschuh et al., 2000] , cleaved from the solid support and chemoselectively immobilized on functionalized glass slides. Each peptide was deposited on the microarray in triplicates.

The peptide microarrays were incubated with sera (applied dilution 1:200) in an HS 4800 microarray processing station (Tecan) for two hours at 30°C, followed by incubation with 0.1 μg/mL fl g/mL fluorescently labelled anti human IgG detection antibody (Jackson Immunoresearch, 109-605-098). Washing steps were performed prior to every incubation step with 0.1% Tween-20 in 1x TBS. After the final incubation step the microarrays were washed (0.05% Tween-20 in 0.1x SSC) and dried in a stream of nitrogen. Each microarray was scanned using a GenePix Autoloader 4300 SL50 (Molecular Devices, Pixel size: 10 μg/mL fl m). Signal intensities were evaluated using GenePix Pro 7.0 analysis software (Molecular Devices). For each peptide, the MMC2 value of the three triplicates was calculated. The MMC2 value was equal to the mean value of all three instances on the microarray except when the coefficient of variation (CV) -standard-deviation divided by the mean value -was larger than 0.5. In this case the mean of the two values closest to each other (MC2) was assigned to MMC2. Further data analysis and generation of the heatmapts was performed using the statistical computing and graphics software R (Version 4.0.2, www.r-project.org).

We used the protocol described by the centers for disease control and prevention for the 

To detect antibodies, we used the Euroimmun Anti-SARS-CoV-2 ELISA (IgG), which was manufactured by Euroimmun Medizinische Labordiagnostika AG, Lübeck, Germany. The break-off microplate wells were covered with the antigen, a recombinant structural spike 1 (S1) protein of SARS-CoV-2 expressed in HEK293 [FDA, 2020] . First, the diluted patient samples were incubated in the wells which lead to specific IgG antibodies binding to the antigens. In order to detect the bound antibodies, an enzyme-labelled antihuman IgG antibody detected antigen-antibody complexes and catalysed a colour reaction. By calculating the extinction of the sample over the extinction of the calibrator the results could be evaluated semi-quantitatively. A ratio below 0.8 was considered a negative result. A ratio between 0.8 and 1.1 was considered a borderline result. A ratio above 1.1 was considered a positive result [Euroimmun Medizinische Labordiagnostika AG, 2020].

Data analysis and heatmap generation was performed using the statistical computing and graphics software R (Version 4.0.2, www.r-project.org). Table 2 ). Immunodominance was assigned to epitopes based on the highest accuracy scores and the sequences of the corresponding overlapping peptides ( Table 1) .

The Spearman's rank correlation coefficient reflecting the relationship between the commercial ELISA and the peptide microarray (Figure 3 perpetuity.

preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in

The copyright holder for this this version posted November 27, 2020. ; https://doi.org/10.1101 https://doi.org/10. /2020 perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted November 27, 2020. ; https://doi.org/10.1101/2020.11.24.20216663 doi: medRxiv preprint perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted November 27, 2020. ; Figure 6 . Peptide microarray analysis of IgG responses to other non-structural proteins of SARS-CoV-2. Heatmaps represent two groups of serum samples: SARS-CoV-2 infected and control groups. The two left rows of the heatmaps reflect signals obtained with detection antibody only (no serum). Columns represent peptide sequences, rows represent samples. Colours indicate the signal values obtained from triplicate spots ranging from white (0 or low intensity) over yellow (middle intensity) to red (maximal intensity of 65535 light units). The two left rows of the heatmaps reflect signals obtained with detection antibody only (no serum). The upper row in each heatmap reflects the highest possible signal intensity. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted November 27, 2020. ; https://doi.org/10.1101/2020.11.24.20216663 doi: medRxiv preprint perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted November 27, 2020. ; https://doi.org/10.1101/2020.11.24.20216663 doi: medRxiv preprint perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted November 27, 2020. ; https://doi.org/10.1101/2020.11.24.20216663 doi: medRxiv preprint perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted November 27, 2020. ; https://doi.org/10.1101/2020.11.24.20216663 doi: medRxiv preprint perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted November 27, 2020. ; https://doi.org/10.1101/2020.11.24.20216663 doi: medRxiv preprint perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted November 27, 2020. ; https://doi.org/10. 1101 

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The authors are grateful for the assistance of the following local health authorities (Gesundheitsämter) of Landkreis Börde (Frau Dr. Kontzog and Team), Landkreis Salz-

Team) for their support in identifying appropriate blood donors.

*: The mnemonic organism identification code of SARS-CoV-2 has been changed. In the updated version, all UniProtKB entry names of SARS-CoV-2 contain the "_SARS2" species identification code instead of "_WCPV".