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Global profiling of SARS-CoV-2 specific IgG/ IgM responses of convalescents using a
proteome microarray
He-wei Jiang1,#, Yang Li1,#, Hai-nan Zhang1,#, Wei Wang2,#, Dong Men3, Xiao Yang4, Huan Qi1, Jie
Zhou2,*, Sheng-ce Tao1,*
1Shanghai Center for Systems Biomedicine, Key Laboratory of Systems Biomedicine (Ministry
of Education), Shanghai Jiao Tong University, Shanghai 200240, China
2Foshan Fourth People's Hospital, Foshan 528000, China
3State Key Laboratory of Virology, Wuhan Institute of Virology, Chinese Academy of Sciences,
Wuhan 430071, China
4Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences,
Beijing, 100101, China
# These authors contributed equally to this work.
* corresponding should address to: Shanghai Center for Systems Biomedicine, Key Laboratory
of Systems Biomedicine (Ministry of Education), Shanghai Jiao Tong University, Shanghai
200240, China. E-mail address: [email protected] (S.-C. Tao) or Foshan Fourth People’s
Hospital, Foshan 528000, China. [email protected] (J. Zhou)
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Abstract
COVID-19 is caused by SARS-CoV-2, and has become a global pandemic. There is no highly
effective medicine or vaccine, most of the patients were recovered by their own immune
response, especially the virus specific IgG and IgM responses. However, the IgG/ IgM responses
is barely known. To enable the global understanding of SARS-CoV-2 specific IgG/ IgM
responses, a SARS-CoV-2 proteome microarray with 18 out of the 28 predicted proteins was
constructed. The microarray was applied to profile the IgG/ IgM responses with 29 convalescent
sera. The results suggest that at the convalescent phase 100% of patients had IgG/ IgM responses
to SARS-CoV-2, especially to protein N, S1 but not S2. S1 purified from mammalian cell
demonstrated the highest performance to differentiate COVID-19 patients from controls. Besides
protein N and S1, significant antibody responses to ORF9b and NSP5 were also identified. In-
depth analysis showed that the level of S1 IgG positively correlate to age and the level of LDH
(lactate dehydrogenase), especially for women, while the level of S1 IgG negatively correlate to
Ly% (Lymphocyte percentage). This study presents the first whole picture of the SARS-CoV-2
specific IgG/ IgM responses, and provides insights to develop precise immuno-diagnostics,
effective treatment and vaccine.
Keywords: SARS-CoV-2; Protein microarray; Serum profiling; IgG; IgM
Highlights
⚫ A SARS-CoV-2 proteome microarray contains 18 of the 28 predicted proteins
⚫ The 1st global picture of the SARS-CoV-2 specific IgG/ IgM response reveals that at the
convalescent phase, 100% of patients have IgG/ IgM responses to protein N and S1
⚫ Significant antibody responses against ORF9b and NSP5 were identified
⚫ Protein S1 specific IgG positively correlates to age and LDH, while negatively to Ly%
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Introduction
COVID-19 is caused by coronavirus SARS-CoV-21,2. In China alone, by Mar. 18, 2020, there are
81,163 diagnosed cases with SARS-CoV-2 infection, and 3,242 death according to Chinese CDC
(http://2019ncov.chinacdc.cn/2019-nCoV/). Globally, 184,976 diagnosed cases were reported in
159 countries, areas or territories. And on Mar. 11, WHO announced COVID-19 as a global
pandemic. Aided by the high-throughput power of Next Generation Sequencing (NGS), the
causative agent of COVID, i.e., SARS-CoV-2, was successfully identified and genome
sequenced. Sequence analysis suggests that SARS-CoV-2 is most closely related to BatCoV
RaTG13 and belongs to subgenus Sarbecovirus of Betacoronavirus, together with Bat-SARS-like
coronavirus and SARS coronavirus1,2. Through the comparison with SARS-CoV and other related
coronaviruses, it is predicted that there are 28 proteins may encoded by the genome of SARS-
CoV-2, including 5 structure proteins (we split S protein to S1 and S2, and thus count as 2
proteins), 8 accessory proteins and 15 non-structural proteins3. SARS-CoV-2 may utilize the
same mechanism to enter the host cells, i.e., the high affinity binding between the receptor
binding domain (RBD) of the spike protein and angiotensin converting enzyme 2 (ACE2)4-9.
Though tremendous efforts are being poured for hunting effective therapeutic agents, i.e., small
molecules/ neutralization antibodies, and protective vaccines, unfortunately, none of them are
available at this moment, even in the near future10. By Mar. 18, 2020, 69,740 patients have been
cured in China (http://2019ncov.chinacdc.cn/2019-nCoV/). Since there is no effective anti-SARS-
CoV-2 drug and therapeutic antibody, theoretically, most of these patients are cured by
themselves, i.e., by their own immune system. It is known that for combating virus infections,
usually antibodies (IgG and IgM) play critical roles, for example, SARS-CoV11,12 and MERS-
CoV13,14. Thus, it is reasonable to argue that virus specific IgG and IgM may also significantly
contribute to the battle against SARS-CoV-2 infection. Indeed, high levels of SARS-CoV-2
specific IgG and IgM could be monitored for many of the patients15. In addition, positive results
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were observed by treating the patients with convalescent plasma collected from COVID-19
patients16,17.
However, because SARS-CoV-2 is a newly occurred pathogen, the IgG/IgM response is barely
known. There are many important questions need to be experimentally addressed: 1. What’s the
variation among different patients, especially for antibodies against nucleocapsid protein (protein
N) and spike protein (protein S)? 2. Besides nucleocapsid protein and spike protein, is there any
other viral protein that could trigger significant antibody response for at least some of the
patients? 3. Is it possible to link the intensity of the overall IgG/IgM response to the severity of
patients? and etc. It is urgently needed to answer these questions, especially in a systematic
manner. Once these questions are answered or at least touched, we may can understand the
IgG/IgM response in detail, and in turn facilitate us to develop more effective treatment,
therapeutic antibody and protective vaccine.
Traditional techniques for studying IgG/ IgM responses including ELISA18-20, and immune-
colloidal gold strip assay19,21,22. However, these techniques usually can only test one target protein
or one antibody in one reaction. Thus a powerful tool is needed that enables the studying of the
IgG/ IgM responses on a systems level. Featured by the capability of high-throughput and parallel
analysis, and miniaturized size, protein microarray may be the choice for systematic study of the
SARS-CoV-2 stimulated IgG/IgM responses. A variety of protein microarrays have already been
constructed and successfully applied for serum antibody profiling, such as the Mtb proteome
microarray23, the SARS-CoV protein microarray11, and the Dengue virus protein microarray24.
Here, we present a SARS-CoV-2 proteome microarray developed using an E.coli expression
system and SARS-CoV-2 proteins collected from several commercial sources. COVID-19
Convalescent sera were analyzed on the microarray, the first overall picture of SARS-CoV-2
specific IgG/ IgM responses was revealed.
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Results
Schematic diagram and workflow
The genome of SARS-CoV-2 is ~29.8 kb, which is predicted to encode 28 proteins3, i.e., 4
structural proteins (split S protein as S1 and S2), 8 accessory proteins and 15 non-structural
proteins (nsp) (Fig. 1A). The sequences of all these proteins and the receptor binding domain
(RBD) on S1 were codon optimized, gene synthesized and cloned to appropriate vector for
expression in E. coli. We affinity purified these proteins, meanwhile, we collected recombinant
proteins expressed from both prokaryotic and eukaryotic systems from other sources. After
quality control, these proteins were then printed on appropriate substrate slides. Convalescent sera
were collected and analyzed on the proteome microarray. The global SARS-CoV-2 specific IgG
and IgM responses were revealed.
Eighteen of the 28 predicted SARS-CoV-2 were prepared
To prepare recombinant proteins of SARS-CoV-2 for microarray fabrication, we first determined
the amino acid sequences of predicted proteins3 follow a reference genome (Genbank accession
No. MN908947.3). To obtain more precise analysis, we split protein S as S1 and S23, and also
included RBD because of its critical role during the entry of SARS-CoV-2 into the cells. The
protein sequences were subjected for codon optimization and then cloned into E.coli expression
vector (pET32a or pGEX-4T-1). The final expression library includes 31 clones (Table S1). After
several rounds of optimizations, so far, we managed to purify 17 of these proteins (Fig. S1). As
demonstrated by Western blotting with an anti-6xHis antibody and Coomassie staining, Most of
the SARS-CoV-2 proteins showed clear bands of the expected size (±10 kDa) and good purity.
Meanwhile, in order to cover the proteome of SARS-CoV-2 as completed as possible, and to take
post-translational modification (PTM), especially glycosylation into account, we also collected
recombinant SARS-CoV-2 proteins prepared using yeast cell-free system and mammalian cell
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expression system, from a variety of sources (Figure S1). Among the collected proteins, there are
several different versions of S1 and N protein (Table S1). Finally, we obtained 37 proteins of
different versions from different sources, which covers 18 out of the 28 predicted proteins of
SARS-CoV-2. These proteins are suitable for microarray construction in terms of both
concentration and purity.
Protein microarray fabrication
A total of 38 proteins along with positive and negative controls were printed on the microarray
slide (Fig. 2). Since most of the proteins were tagged with 6xHis tag, the overall quality of the
microarray were evaluated by probing with an anti-6xHis antibody. The anti-6xHis antibody results
showed that most of the proteins were nicely immobilized, and the microarray quality if fairly good.
The detailed layout of the SARS-CoV-2 proteome microarray was indicated as well (Fig. 2A). High
antibody responses were usually observed for COVID-19 patients while not in control sera (Fig.
2B). Since the Fc tag could be recognized by fluorescence labeled anti-human IgG antibody, the
ACE2-Fc generated high signals for all the tests, which could serve as control for the anti-human
IgG antibody, though the initial reason to include ACE2 on the microarray is for applications other
than serum profiling. To test the experimental reproducibility of the serum profiling using the
microarray, two COVID-19 convalescent sera were random selected. Three independent analysis
for each of these two sera were repeated on the microarray. Pearson correlation coefficients
between two repeats were 0.988 and 0.981 for IgG and IgM, respectively, and the overall
fluorescence intensity ranges of the repeated experiments were fairly close, demonstrating high
reproducibility of the microarray based serum profiling both for IgG and IgM (Fig. 2 C-E).
SARS-CoV-2 specific Serum profiles depicted by proteome microarray
To globally profile the antibody response against the SARS-CoV-2 proteins in the serum of
COVID-19 patients, we screened sera from 29 convalescent patients, along with 21 controls by the
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proteome microarray. The patients were hospitalized in Foshan Fourth hospital, China during 2020-
1-25 to 2020-2-27 with variable stay time. The information of the patients was summarized in
Table 1. Serum from each patient was collected on the day of hospital discharge when the standard
criteria were met. And there is no recurrence reported for these patients. All the samples and the
controls were probed on the proteome microarray, after data filtering and normalization, we built
the IgG and IgM profile for each serum and performed clustering analysis to generate heatmaps for
overall visualization (Fig. 3 and Fig. 4). The patients and controls are perfectly clustered for both
IgG and IgM, justifying the utility of the SARS-CoV-2 proteome microarray for the virus specific
antibody analysis of COVID-19. As expected, the N protein and S1 protein elicited high antibody
responses in almost all patients but barely in control groups, confirming the efficacy of these two
proteins for diagnosis. Interestingly, we also found that in some cases, some proteins other than N
or S1 can generate significantly higher signals compared with that of the control groups.
Strong response against S and N proteins
Since S and N protein are widely used as antigens for diagnosis of COVID-19, we next
characterized the serum antibody responses against these two proteins. For the present cohort, both
S and N proteins, except for S1-4, were proved to have excellent discrimination ability between
COVID-19 patients and controls both for IgG and IgM (Fig. 5A, B, Fig. S3A, B). The overall IgG
signal intensities were much higher than that of IgM, mainly because the sera were collected at the
convalescent stage when IgG are supposed to be dominant. It is notable that two sera from control
group have significantly higher IgG antibody response to N proteins than other controls, with one
to N-Nter and another to C-Nter (Fig. 5G), suggesting the N protein may generate a higher false
positive rate than S protein, especially S1. To investigate the consistence of signal intensities among
different sources or versions (C-term, N-term, domain or fragment) of proteins, we calculated the
Pearson correlation coefficients among S proteins (Fig. 5C) and N proteins (Fig. S2F) using data
of the convalescent sera. High correlations are observed among different concentrations of the same
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proteins, and the same protein from different sources is also of high correlation (Fig. 5D, H, Fig.
2SA, B, C, G), although N protein with high concentrations generate almost saturated signals (Fig.
S2G). Specially, for full length S1 proteins from different sources, either obtained from E.coli
(S1_T) or 293T (S1_B and S1_S) expression system, high correlations with each other were
observed (Fig. 5C, D), indicating the proteins from different sources are all have good performance
for detection. However, the background signals in control group are much lower for proteins
purified from mammalian cells, i.e., 293T (Fig. 5A), suggesting they may possess a higher septicity
and could serve as a better reagent for developing immune-diagnostics. The signals of the full
length S1 protein are highly correlated with that of S-RBD (Fig. 5E) but with much stronger signals.
In contrast, the correlation levels of S1-4 region with full-length S1 or RBD are lower (Fig. 5C,
Fig. S2D). In addition, The S1 signals are poorly correlated with S2 proteins (Fig. 5F). These data
may reflect the difference in immunogenicity for different regions of S protein, further epitope
mapping could answer this question. Similar situations are also observed for N proteins (Fig. 5H,
Fig. S2H, I). Interestingly, moderate but significant liner correlations were observed between IgG
responses of N and S1 (Fig. 5I). In addition, the correlations of IgG and IgM signals for the same
protein were low (Fig. 5J, K), this may in-part because the overall IgM signals were much lower
than that of IgG (Fig. S3) at the convalescent stage.
Antibody responses against other proteins
The proteome microarray enables us to investigate antibody responses to 18 of the 28 predicted
proteins of SARS-CoV-2, including S and N proteins. As mentioned above, some proteins other
than N and S proteins also generated high IgG signals (Fig. 3). After global analysis, we identified
6 proteins, against which high IgG responses were detected in at least one convalescent sera (Fig.
6A). Importantly, 44.8% (13/29) patients presented positive IgG antibody to ORF9b under the
threshold set based on the signals of control sera (Fig. 6B). IgG antibodies to NSP5 were positive
in 3/19 patients and positive in 1/21 control sera (Fig. 6C). To investigate if the IgG responses
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against ORF9b or NSP5 depends on the IgG responses against N or S protein, we calculated the
correlations among them. It turned out that there are no obvious correlations between the IgG to
ORF9b or NSP5 with IgG to N or S (Fig. 6D, E), suggesting these two proteins may provide
complementary information either for diagnosis or worth further study to explore the SARS-CoV-
2 specific immune response.
IgG responses are highly correlated with age, LDH and Ly%
It is known that immune response is closely related to the disease courses. To evaluate how the
relationship between the antibody response and the situation of the disease, we investigated the
correlations between IgG or IgM responses to proteins of virus with clinical characteristics. Not
surprisingly, the days after onset were correlated with the IgG response against S1 (Fig. 7A) or N
protein (date not shown), as the IgG response usually increases over time and reach the peak several
weeks after onset, which is observed by other studies25 and SARS patients26. In contrast, there is
no correlations between IgM response with days after onset. It was observed that age is also
correlated with IgG response to S1 or N proteins (Fig. 7B, Fig. S4A). Three patients with mild
symptoms indicated by red arrows show low IgG response and it is highly variable in the patients
with common symptoms. It is notable that in male patients, especially older than 40, the correlation
between age and S1 IgG is very poor (Fig. 7B). To further investigate this phenomenon, we
separately analyzed the male and female patients in different age groups. For female older than 40
years old, the S1 IgG response is significantly stronger than that either in the group of female
younger than 40 or the male counterpart (Fig. 7C). For male younger than 40, high correlation
between age and S1 or N IgG response was observed (Fig. 7D, Fig. S4C), and this correlation is
not caused by days after onset (Fig. S4B). For female, whatever the age range is, IgG response to
S1 or N is highly correlated with age (Fig. S4D, E). To exclude the influence of days after onset,
the patients with similar (18-24) days after onset were selected (Fig. S4F). High correlation is still
observed (Fig. 7E, Fig. S4G), demonstrating the correlation is independent of days after onset.
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It was found that the IgG response against S1 or N protein is highly correlated with peak lactate
dehydrogenase (LDH) level in female (Fig. S4H) but not significantly in male patients (Fig. 7E).
To evaluate whether there is correlation between peak LDH and virus specific IgG response in
female patients, the patients with 18-24 days after onset and ranging in age from 20 to 60 were
selected to form a smaller cohort, in which, no statistical correlation between age or days after onset
with peak LDH were observed (Fig. S4I). It turns out the correlation was still high and significant
both for S1 and N protein specific IgG response (Fig. 7G, Fig. S4J), demonstrating the correlation
is independent of age and days after onset. Since the LDH level could serve as an indicator of
disease severity26,27, it seems that immune system of women may be more sensitive to the virus. It
was also found that the IgG response to S1 or N protein negatively correlates with the percentage
of lymphocyte (Ly%) (Fig. 7H, I, Fig. S4K), however, this correlation might be dependent of age
(Fig. S4L).
Materials and methods
Construction of expression vectors
The protein sequences of SARS-CoV-2 were downloaded from GenBank (Accession number:
MN908947.3). According to the optimized genetic algorithm28, the amino acid sequences was
converted into E.coli codon-optimized gene sequences. Subsequently, the sequence optimized
genes were synthesized by Sangon Biotech. (Shanghai, China). The synthesized genes were
cloned into pET32a or pGEX-4T-1 and transformed into E. coli BL21 strain to construct the
transformants. Detailed information of the clones constructed in this study is given in Table S1.
Protein preparation
The recombinant proteins were expressed in E. coli BL21 by growing cells in 200 mL LB
medium to an A600 of 0.6 at 37 °C. Protein expression was induced by the addition of 0.2 mM
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isopropyl-β-d-thiogalactoside (IPTG) before incubating cells overnight at 16 °C. For the
purification of 6xHis-tagged proteins, cell pellets were re-suspended in lysis buffer containing 50
mM Tris-HCl pH 8.0, 500 mM NaCl, 20 mM imidazole (pH 8.0), then lysed by a high-pressure
cell cracker (Union-biotech, Shanghai, CHN). Cell lysates were centrifuged at 12,000 rpm for 20
mins at 4℃. Supernatants were purified with Ni2+ Sepharose beads (Senhui Microsphere
Technology, Suzhou, CHN), then washed with lysis buffer and eluted with buffer containing 50
mM Tris-HCl pH 8.0, 500 mM NaCl and 300 mM imidazole pH 8.0. For the purification of GST-
tagged proteins, cells were harvested and lysed by a high-pressure cell cracker in lysis buffer
containing 50 mM Tris-HCl, pH 8.0, 500 mM NaCl, 1 mM DTT. After centrifugation, the
supernatant was incubated with GST-Sepharose beads (Senhui Microsphere Technology, Suzhou,
CHN). The target proteins were washed with lysis buffer and eluted with 50 mM Tris-HCl, pH
8.0, 500 mM NaCl, 1 mM DTT, 40 mM glutathione. The purified proteins were analyzed by
SDS-PAGE followed by Western blotting using an anti-His antibody (Merck millipore, USA) and
Coomassie brilliant blue staining. Recombinant SARS-CoV-2 proteins were also collected from
commercial sources. Detailed information of the recombinant proteins prepared in this study is
given in Table S1.
Protein microarray fabrication
The proteins, along with negative (BSA) and positive controls (anti-Human IgG and IgM antibody),
were printed in quadruplicate on PATH substrate slide (Grace Bio-Labs, Oregon, USA) to generate
identical arrays in a 2 x 7 subarray format using Super Marathon printer (Arrayjet, UK). Protein
arrays were stored at -80°C until use.
Patients and samples
The Institutional Ethics Review Committee of Foshan Fourth Hospital, Foshan, China approved
this study, and written informed consent was obtained from each patient. COVID-19 patients were
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hospitalized and received treatment in Foshan Forth hospital during 2020-1-25 to 2020-2-27 with
variable stay time (Table 1). Serum samples were collected when the patients were discharged
from the hospital. Sera of control group from Lung cancer patients and healthy controls were
collected from Ruijin Hospital, Shanghai, China. All sera were stored at -80 ℃ until use.
Microarray based serum analysis
A 14-chamber rubber gasket was mounted onto each slide to create individual chambers for the 14
identical subarrays. The microarray was used for serum profiling as described previously29 with
minor modifications. Briefly, the arrays stored at -80°C were warmed to room temperature and then
incubated in blocking buffer (3% BSA in PBS buffer with 0.1% Tween 20) for 3 h. Serum samples
were diluted 1:200 in PBS containing 0.1% Tween 20. A total of 200 μL of diluted serum or buffer
only was incubated with each subarray overnight at 4°C. The arrays were washed with PBST and
bound autoantibodies were detected by incubating with Cy3-conjugated goat anti-human IgG and
Alexa Fluor 647-conjugated donkey anti-human IgM (Jackson ImmunoResearch, PA, USA), the
antibodies were diluted 1: 1,000 in PBST, and incubated at room temperature for 1 h. The
microarrays were then washed with PBST and dried by centrifugation at room temperature and
scanned by LuxScan 10K-A (CapitalBio Corporation, Beijing, China) with the parameters set as
95% laser power/ PMT 550 and 95% laser power/ PMT 480 for IgM and IgG, respectively. The
fluorescent intensity data was extracted by GenePix Pro 6.0 software (Molecular Devices, CA,
USA).
Statistics
Signal Intensity was defined as median of foreground subtracted by median of background for each
spot and then averaged of the quadruplicate spots for each protein. IgG and IgM data were analyzed
separately. Before processing, data from some spots, such as NSP7_0.1_T, NSP9P_K, are excluded
for probably printing contamination. Pearson correlation coefficient between two proteins or
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indicators and the corresponding p value was calculated by SPSS software under the default
parameters. Cluster analysis was performed by pheatmap package in R30.
Discussion
In order to profile the SARS-CoV-2 specific IgG/IgM responses, we have constructed a SARS-
CoV-2 proteome microarray with 18 of the 28 predicted proteins. To our knowledge, this is the
first of such. A set of 29 recovered sera were analyzed on the microarray, global IgG and IgM
profile were obtained simultaneously through a dual color strategy. Our data clearly showed that
both protein N and S1 are suitable for diagnostics, while S1 purified from mammalian cell may
possess better specificity. Significant antibody responses were identified for ORF9b and NSP5.
We showed that the level of S1 IgG positively correlate to age and the level of LDH while
negatively correlate to Ly%.
The SARS-CoV-2 proteome microarray enables not only the global profiling of virus specific
antibody responses but also providing semi-quantitative information. By adopting the dual color
strategy of microarray, we can measure IgG and IgM simultaneously. For the convalescent
COVID-19 patients tested in this study that with a median of 22 days after onset, we found that
the overall IgG response is significantly higher than that of IgM, indicating for these patients the
SARS-CoV-2 specific IgG responses are dominant at the convalescent phase, although IgM level
might reach the peak at a similar time point with that of IgG, according to some studies of SARS-
CoV31,32.
It is well known that S1 and N proteins are the dominant antigens of SARS-CoV and SARS-
CoV-2 that elicit both IgG and IgM antibodies, and antibody response against N protein is usually
stronger. However, we found for two of the control sera, strong IgG bindings were observed for
N protein, and specifically one control recognizing N protein at the N terminal while another at
the C terminal. This maybe due to the high conservation of N protein sequences across the
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coronavirus species, this indicating we should be aware of the false positive when applying N
protein for diagnosis. In contrast, S1 protein demonstrating a higher specificity. Thus an ideal
choice of developing immune-diagnostics maybe the combining of both N protein and S1 protein.
We also compared the antibody responses against a variety versions of S1, including the full
length, the RBD domain, the N terminal and the C terminal. The antibody response to RBD
region is highly correlated with that to full length protein but with weaker signals, however, the
correlations among other S1 versions are not significant, suggesting dominant epitopes that elicit
antibodies might differ among individuals. Further study of detailed epitope mapping might give
us a clear answer.
In this study, we also found the significant presence of IgG and IgM against ORF9b (13 out of
29 cases) and NSP5 (3 out of 29 cases). ORF9b is predicted as an accessory protein, exhibiting
high overall sequence similarity to SARS and SARS-like COVs ORF9b (V23I)3, and is likely to
be a lipid binding protein33. Previous studies showed that SARS ORF9b suppresses innate
immunity by targeting mitochondria34. Two previous studies have found antibody against SARS
ORF9b presented in the sera of patients recovering from SARS35,36. Our study also demonstrates
the potential of antibody against ORF9b for detection of convalescent COVID-19 patients.
COVID-19 NSP5 is also highly homologous to SARS NSP5 (96% identity, 98% similarity). Its
homologous proteins in a variety of coronaviruses have been proven to impair IFN response37-39.
Our study is the first report to provide experimental evidence to show the existence of NSP5
specific antibody in convalescents. Since NSP5 is a non-structural protein, theoretically, it should
present only in the infected cells but not in virions. So antibody against NSP5 has the potential to
be applied to distinguish between COVID-19 patients and healthy people immunized with
inactivated virus.
We have analyzed the correlations between the COVID-19 specific IgG responses with clinical
characteristics as well. It is expected that IgG responses improve over time within one or two
months after onset25,31,32 and we did observe a significant correlation between IgG signals with
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15
days after onset. We also found peak LDH was highly correlated with IgG response, especially
for female patients. As many studies reported, LDH tends to have a higher level in severe
COVID-19 patients and could be an indicator of severity26,27. In fact, it has been observed in
SARS patients that more severe SARS is associated with more robust serological response26,40,
similar association was confirmed in COVID-19 patients, especially for females. Interestingly, we
observed high correlation between age with IgG response in female patients and in male patients
with age less than 40 but not in older male patients, implying the humoral response against
SARS-CoV-2 may differ in gender. It is reported that severe cases are significantly more frequent
in aged patients and the mortality of male patients is higher than that of female, but the reason is
not clear. Based on our observations, we assumed that the situation might be associated with the
immune response. However, female patients, compared with male patients, may generate humoral
response more efficiently. This difference should be considered during treatment.
There are some limitations of the current SARS-CoV-2 proteome microarray. Firstly, due to
the difficulty of protein expression and purification, there are still 10 proteins missing3. We will
try to obtain these proteins through vigorous optimization or other sources, interesting finding is
anticipated in the near future for these missing proteins. Secondly, most of the proteins on the
microarray are not expressed in mammalian cells, critical post-translational modifications, such
as glycosylation is absent. It is known that there are 23 N-glycosylation sites on S protein, which
is heavily glycosylated, and the glycosylation may play critical roles in antibody- antigen
recognition5,41. We are preparing these proteins using mammalian cell systems. Once the
microarray is upgraded with proteins purified from mammalian cells, PTM specific IgG/IgM
response may could be elicited. Thirdly, only 29 samples at collected at a single time point were
analyzed. Though there are some interesting findings, we believe some of the current conclusions
could be strengthened by including more samples. Furthermore, longitudinal samples29,42
collected at different time points from the same individual after diagnosis or even after cured may
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16
enable us to reveal the dynamics of the SARS-CoV-2 specific IgG/IgM responses. The data may
could be further linked to the severity of COVID-19 among different patients.
The application of the SARS-CoV-2 proteome microarray is not limited to serum profiling. It
could also be explored for host-pathogen interaction43, drug/ small molecule target
identification44,45,and antibody specificity assessment46.
Through the same construction procedure, we could easily expand the microarray to a pan
human coronavirus proteome microarray by including the other two severe coronaviruses, i.e.,
SARS-CoV11,47,48 and MERS-CoV47, as well as the four known mild human coronaviruses49,50,
i.e., CoV 229E, CoV OC43, CoV HKU-1 and CoV NL63. By applying this microarray, we can
assess the immune response to coronavirus on a systems level, and the possible cross-reactivity
could be easily judged.
Taken together, we have constructed the first SARS-CoV-2 proteome microarray, this
microarray could be applied for a variety of applications, including but not limited to in-depth
IgG/ IgM response profiling. Through the analysis of convalescent sera on the microarray, we
obtained the first overall picture of SARS-CoV-2 specific IgG/ IgM profile. We believe that the
findings in this study will shed light in the development of more precise diagnostic kit, more
appropriate treatment and effective vaccine for combating the global crisis that we are facing
now.
Funding sources
This work was partially supported by National Key Research and Development Program of China
Grant (No. 2016YFA0500600), National Natural Science Foundation of China (No. 31970130,
31600672, 31670831, and 31370813).
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17
Acknowledgements
We thank Dr. Min Guo of Healthcode Co., Ltd. for providing affinity purified proteins. We thank
Dr. Guo-Jun Lang of Sanyou Biopharmaceuticals Co., Ltd. for provide proteins and antibodies.
We also thank Dr. Jie Wang of VACURE l Biotechnology Co.,Ltd. , Dr. Yin-Lai Li of Hangzhou
Bioeast biotech. Co.,Ltd., and Sino biological Co.,Ltd. for providing the proteins.
Author contributions
S-C. T. developed the conceptual ideas and designed the study. J. Z., W. W., D. M. and X. Y.
collected the sera samples and provided key reagents. H-W. J., Y. L, H-N. Z., H. Q. performed
the experiments, S-C.T., Y. L., and H-W. J. wrote the manuscript with suggestions from other
authors.
Declaration of conflict of interest
The authors declare no conflicts of interest.
Data availability
The SARS-CoV-2 proteome microarray data are deposited on Protein Microarray Database
(http://www.proteinmicroarray.cn) under the accession number PMDE241. Additional data
related to this paper may be requested from the authors.
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Figure Legends
Figure 1. The workflow of SARS-CoV-2 proteome microarray fabrication and serum
profiling. A) The genome of SARS-CoV-2 and the 28 predicted proteins. B) The workflow of
proteome microarray fabrication and serum profiling on the microarray.
Figure S1. The SARS-CoV-2 proteins included in this proteome microarray. The Up panel is
western blotting with an anti-6xHis antibody. The low panel is Coomassie staining. These proteins
were prepared and collected from different sources. T: Tao Lab (our laboratory); B: Hangzhou
Bioeast biotech. Co., Ltd.; K: Healthcode Co., Ltd.; S: Sanyou biopharmaceuticals Co., Ltd.; W:
VACURE l Biotechnology Co., Ltd. Y: Sino biological Co., Ltd.
Figure 2. SARS-CoV-2 proteome microarray layout and quality control. A) There are 14
identical sub-arrays on a single microarray. A microarray was incubated with an anti-6xHis
antibody to demonstrate the overall microarray quality (green). One sub-array is shown. To
facilitate labeling, this sub-array is split into 3 parts. The proteins were printed in quadruplicate.
The triangles indicate dilution titers of the same proteins. B) Representative sub-arrays probed with
sera of a COVID-19 convalescent and a healthy control. The IgG and IgM responses are shown in
green and red, respectively. C) and D) The correlations of the overall IgG and IgM signal intensities
between two repeats probed with the same serum. E) Statistics of the Pearson correlation confidents
among repeats probed with the same serum.
Figure 3. The overall SARS-CoV-2 specific IgG profiles of the 29 convalescent sera against
the proteins. Each square indicates the IgG antibody response against the protein (row) in the
serum (column). Proteins are shown with names along with concentrations (μg/mL) and sources.
Sera are shown with group information and serum number. NCP: Novel Coronavirus Patients or
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23
COVID-19 patients; LC:Lung Cancer; NC:Normal Control. Blank means no serum. Three repeats
were performed for serum NCP534 and NCP535. FI:Fluorescence Intensity.
Figure 4. The overall SARS-CoV-2 specific IgM profiles of the 29 convalescent sera against
the proteins. Each square indicates the IgM antibody response against the protein (row) in the
serum (column). The rest are the same as that of Figure 3.
Figure 5. IgG response to S and N proteins. A) Box plots of IgG response for S1 and S2 proteins.
Each dot indicates one serum sample either from convalescent group (green) or control group
(brown). Mean and standard deviation value for each group are indicated. The proteins labeled with
red are over expression in mammalian cell lines. P values were calculated by t.test. **, p <0.01; *,
p <0.05; n.s., not significant. B) Box plots of IgG response for N proteins. C) Pearson correlation
coefficient matrix of IgG response among different S1 and S2 proteins. D-F) Correlations of overall
IgG responses among different S1 proteins (D), S1 vs. RBD (E) and S1 vs. S2 (F). Each spot
represents one sample. G) One part of a sub-microarray showed the IgG responses of two controls,
i.e., LC169 and NC96 against N proteins, N-Cter and N-Nter indicated the C-terminal and N-
terminal of N protein, respectively. H-I) Correlations of the overall IgG responses among different
N proteins (H) and N protein vs. S protein (I). J) Statistics of the Pearson correlation coefficients
between IgG and IgM profile against a set of proteins. K) Correlations between IgG and IgM profile
against S1_0.1_W.
Figure S2. IgG response to S and N proteins. A-E)Correlations of the overall IgG responses
among different dilutions of protein S1_S (A), two RDB proteins from different sources (B), S2-1
vs. S2-2 (C), S1 vs. S1-4 (D) and S1 vs. S2 (E). F) Pearson correlation coefficient matrix of IgG
responses among N proteins. Each spot represents one sample. G-I) Correlations of the overall IgG
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24
responses among different dilutions of protein N_W (A), N-Nter/ N-Cter vs. N protein (H) and N-
Cter vs. N-Nter (I).
Figure S3. IgM Antibody response to S and N proteins. A) Box plots of IgM responses to S1
and S2 proteins. Each dot indicates one serum sample either from patient group (green) or control
group (brown). Mean and standard deviation for each group are indicated. The proteins labeled
with red are over-expressed in 293T. P values were calculated by t test. **, p <0.01; *, p <0.05;
n.s., not significant. B) Box plots of IgM responses to N proteins. C) Pearson correlation coefficient
matrix of IgM responses among S1 and S2 proteins of different versions from different sources.
(D-H) Correlations of the overall IgM responses among S1 proteins (D), S1 vs. RBD (E), S1 vs.
S2 proteins (F), different N proteins (G) and N vs. S proteins (H). Each spot represents one sample.
Figure 6. IgG response to other SARS-CoV-2 proteins. A) Other SARS-CoV-2 proteins that
were recognized by IgG from the convalescent sera, in comparison to that of the controls. B-C)
Anti-ORF9b IgG (B) or anti-NSP5 IgG (C) in patient and control group. Each dot represents one
serum sample. Mean and standard deviation value for each group are indicated. The dashed line
indicates cutoff value calculated as mean plus 3x standard deviation of the control group. P values
were calculated by t test. D-E) Correlations of the overall IgG responses for N or S1 protein vs.
ORF9b (D) or NSP5 (E).
Figure 7. Correlation with clinical characteristics. A) Correlations of S1 IgG responses with
Days after COVID-19 onset. Each spot indicates one COVID-19 patient. B)Correlations of S1
IgG responses with age either for female (blue) or male (pink). The red arrows indicate patients
with mild symptoms while others with common symptoms. C) S1 IgG responses in groups of
different age and gender. P values were calculated by t test. **, p <0.01; *, p <0.05; n.s., not
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25
significant. D-I) Correlations of S1 IgG responses with Age for male (age <40) (D), Age for female
(E), LDH for male (F), LDH for female (G), Ly% for male (H) and Ly% for female (I). Each spot
indicates one patient from the corresponding group. For E), only the patients with 18-24 days after
onset were selected. For G), only the female patients with 18-24 days after onset and ranging in
age from 20 to 60.
Figure S4. Correlation with clinical characteristics. A) Correlations of N protein specific IgG
responses with days after onset. The red arrows indicate patients with mild symptoms while others
with regular symptoms. B-L) Correlations of S1 specific IgG or N specific IgG with Days after
onset, Age, LDH or Ly% in different groups, specifically, Days after onset vs. Age for male (age
<40) (B), N protein specific IgG vs. Age for male (age <40)(C), S1 protein specific IgG vs. Age
for female (D), N protein specific IgG vs. Age for female (E), Days after onset vs. Age for female
patients with18-24 days after onset (F), N protein specific IgG vs. Age for female patients with18-
24 days after onset (G), S1 protein specific IgG vs. LDH for female; (H), Age or Days after onset
vs. LDH for female patients with 18-24 days after onset and ranging in age from 20 to 60. (I), N
protein specific IgG vs. LDH for female patients with 18-24 days after onset and ranging in age
from 20 to 60. (J), N protein specific IgG vs. Ly% for female (K) and Age vs. Ly% for female (L).
For F) and G), only the patients with 18-24 days after onset were selected.
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nsp1
nsp2
nsp3
nsp4
nsp5
5’ UTR 3’ UTRN
nsp12
nsp13
nsp14
nsp15
nsp16
pp1ab
pp1a
S 3a
3b
M
p6
7a
7b
8b
9borf14
E
nsp6
nsp7
nsp8
nsp9
nsp10
Predicted ORFs in SARS-CoV-2 Genome (~29.8 kb)
4 structure proteins
8 accessory proteins
15 nonstructural proteins (nsp)
S1+S2
26 proteins + S1 + S2 + RBD
Codon optimization
Gene synthesis and cloned to pET32a/pGEX-4T-1
Protein expression and purification
Collect recombinant proteins
from other sources
Protein microarray printing
Protein microarray quality control
A.
B.
Figure 1. The workflow of SARS-CoV-2 proteome microarray fabrication and serum profiling.
Profiling of SARS-CoV-2 specific serum IgG & IgM
on the microarray
Serum collection from
recovered person
27 proteins
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IB: His
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170-130-
100-
Figure S1. The SARS-CoV-2 proteins included in this proteomemicroarray.
The Up panel is western blotting with an anti-6xHis antibody. The low panel isCoomassie staining. These proteins were prepared and collected fromdifferent sources. T: Tao Lab; B: Hangzhou Bioeast biotech. Co.,Ltd.; K:
Healthcode Co., Ltd.; S: Sanyou biopharmaceuticals Co.,Ltd.; W: VACURE l Biotechnology Co.,Ltd. Y: Sino biological Co.,Ltd.
72-55-
40-
35-
72-
55-
40-
35-
kDa
. CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprint this version posted March 27, 2020. .https://doi.org/10.1101/2020.03.20.20039495doi: medRxiv preprint
Figure 2. SARS-COV-2 proteome microarray Layout and quality control.
0
20,000
40,000
60,000
0 30,000 60,000
Re
pe
at 2
Repeat 1
0
5,000
10,000
15,000
20,000
0 10,000 20,000
Re
pe
at 2
Repeat 1
R e p ro d u c ib ility
Lo
g 2
(Sig
na
l In
ten
sit
y)
IgG
IgM
0 .9 0
0 .9 5
1 .0 0
1 .0 51.05
1.00
0.95
0.90Corr
ela
tion c
oeff
icie
nt IgG IgM
r=0.998 r=0.987
A. B.
C.
COVID-19 Patient
Normal Control
D. E.
. CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprint this version posted March 27, 2020. .https://doi.org/10.1101/2020.03.20.20039495doi: medRxiv preprint
Figure 3. The overall SARS-CoV-2 specific IgG profiles of the
29 sera against the proteins.
Log2(FI)
NC
P4
36
NC
P5
22
NC
P5
32
NC
P5
31
NC
P5
02
NC
P5
30
NC
P5
08
NC
P4
09
NC
P4
10
NC
P6
10
NC
P5
26
NC
P4
29
NC
P7
44
NC
P5
35
.2N
CP
53
5.1
NC
P5
35
.3N
CP
52
0N
CP
40
1N
CP
40
7N
CP
52
9N
CP
40
6N
CP
40
5N
CP
51
0N
CP
52
3N
CP
50
9N
CP
60
7N
CP
52
7N
CP
41
4N
CP
53
3N
CP
60
5N
CP
53
4.2
NC
P5
34
.1N
CP
53
4.3
Bla
nk.1
Bla
nk.2
LC
16
9N
C6
4N
C9
7L
C1
82
NC
96
LC
17
1N
C1
00
LC
18
4L
C1
75
LC
18
0N
C9
5N
C6
7L
C1
68
LC
17
4N
C6
6N
C9
8L
C1
77
LC
18
1N
C9
9N
C6
3N
C6
5
S-RBD_0.25_SEGFP_0.25_KEGFP_0.1_KCy5NSP4_0.1_TNSP16_0.1_KACE2-Fc_0.25_SACE2-Fc_0.5_SN-protein_0.1_SS1_0.1_SS-RBD_0.5_SS1_0.25_BS1_0.5_BN-protein_0.1_TN-protein_0.1_WN-protein_0.25_WN-Cter_0.5_KN-Cter_0.1_KN-Cter_0.25_KN-protein_0.05_KN-protein_0.1_KN-protein_0.2_KN-protein_0.25_SN-protein_0.5_WN-protein_0.5_SN-Nter_0.1_KN-Nter_0.25_KN-Nter_0.5_KS1_0.25_SS1_0.5_SS1_0.1_BeGFP_0.5_KNSP10_0.25_KNSP15_0.1_KNSP2_0.05_KE-protein_0.25_KE-protein_0.25_TORF7b_0.1_TORF6_0.1_TE-protein_0.1_KGST_0.1_H-IgGH-IgMS1_0.1_TS-RBD_0.5_YNSP5_0.1_TNSP1_0.1_TNSP16_0.1_TORF9b_0.1_TNSP5_0.25_TNSP5_0.5_TNSP8_0.25_TNSP9_0.25_TUBE2D3-HisAnti-H-IgG_0.1Anti-H-IgM_0.1S1-4_0.2_KS2-1_0.05_TS2-2_0.1_TNSP10_0.5_KNSP8_0.1_KNSP8_0.25_KNSP8_0.5_KNSP14_0.05_KACE2_0.5_SNSP14_0.1_KNSP14_0.25_KE-protein_0.5_KNSP15_0.1_TAnti-H-IgM_0.025Cy3NSP15_0.25_KNSP15_0.5_KNSP1_0.1_KNSP1_0.25_KNSP1_0.5_KNSP2_0.1_KNSP2_0.25_KNSP16_0.25_KNSP16_0.5_KAGR2-biotinAnti-H-IgG_0.025
0
5
10
15
. CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprint this version posted March 27, 2020. .https://doi.org/10.1101/2020.03.20.20039495doi: medRxiv preprint
Figure 4. The overall SARS-CoV-2 specific IgM profiles of the 29sera against the proteins.
Log2(FI)
NC
P5
23
NC
P6
10
NC
P5
35
.1N
CP
53
5.2
NC
P5
35
.3N
CP
74
4N
CP
42
9N
CP
50
2N
CP
41
0N
CP
52
6N
CP
43
6N
CP
52
2N
CP
40
9N
CP
53
3N
CP
52
9N
CP
60
5N
CP
40
1N
CP
53
0N
CP
50
8N
CP
52
7N
CP
60
7N
CP
53
1N
CP
40
7N
CP
53
2N
CP
40
6N
CP
51
0N
CP
40
5N
CP
52
0N
CP
53
4.2
NC
P5
34
.1N
CP
53
4.3
NC
P4
14
NC
P5
09
Bla
nk.1
Bla
nk.2
NC
67
LC
18
1N
C6
4N
C6
6L
C1
71
LC
16
8L
C1
74
LC
16
9N
C9
5L
C1
80
NC
10
0L
C1
82
NC
65
LC
17
5L
C1
84
LC
17
7N
C9
8N
C6
3N
C9
6N
C9
7N
C9
9
NSP8_0.5_KNSP10_0.25_KNSP15_0.5_KNSP8_0.1_KNSP8_0.25_KNSP1_0.1_KNSP15_0.25_KNSP14_0.05_KNSP2_0.1_KNSP1_0.25_KNSP1_0.5_KAnti-H-IgG_0.025H-IgGAnti-H-IgG_0.1EGFP_0.1_KEGFP_0.25_KGST_0.1_eGFP_0.5_KE-protein_0.5_KNSP2_0.05_KNSP15_0.1_KCy3E-protein_0.1_KE-protein_0.25_KS-RBD_0.25_SNSP4_0.1_TNSP16_0.1_KH-IgMACE2_0.5_SACE2-Fc_0.25_SACE2-Fc_0.5_SNSP9_0.25_TUBE2D3-HisAnti-H-IgM_0.1NSP5_0.1_TNSP5_0.25_TNSP5_0.5_TE-protein_0.25_TORF7b_0.1_TORF6_0.1_TS2-1_0.05_TS2-2_0.1_TNSP8_0.25_TAnti-H-IgM_0.025NSP16_0.1_TORF9b_0.1_TNSP1_0.1_TNSP16_0.25_KNSP16_0.5_KNSP10_0.5_KNSP15_0.1_TNSP14_0.1_KNSP14_0.25_KS1_0.1_BS-RBD_0.5_SS1_0.1_SS1_0.25_BS1_0.5_BN-protein_0.1_TN-protein_0.1_SS1_0.1_TS-RBD_0.5_YS1_0.25_SS1_0.5_SN-protein_0.05_KN-protein_0.1_KN-protein_0.2_KN-protein_0.1_WN-protein_0.25_WN-protein_0.5_WN-protein_0.25_SN-protein_0.5_SN-Cter_0.1_KN-Cter_0.25_KN-Cter_0.5_KN-Nter_0.1_KN-Nter_0.25_KN-Nter_0.5_KCy5NSP2_0.25_KS1-4_0.2_KAGR2-biotin
0
5
10
15
. CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprint this version posted March 27, 2020. .https://doi.org/10.1101/2020.03.20.20039495doi: medRxiv preprint
S1_0.1
_T
S1_0.1
_S
S1_0.2
5_S
S1_0.5
_S
S1_0.1
_B
S1_0.2
5_B
S1_0.5
_B
S.R
BD
_0.5
_Y
S.R
BD
_0.5
_S
S1.4
_0.2
_K
S2.1
_0.2
5_T
S2.2
_0.1
_T
S1_0.1_T
S1_0.1_S
S1_0.25_S
S1_0.5_S
S1_0.1_B
S1_0.25_B
S1_0.5_B
S-RBD_0.5_Y
S-RBD_0.5_S
S1-4_0.2_K
S2-1_0.25_T
S2-2_0.1_T
0.4
0.5
0.6
0.7
0.8
0.9
1
R e p ro d u c ib ility
Lo
g 2
(Sig
na
l In
ten
sit
y)
S1
S-R
BD S
2 N
0 .0
0 .2
0 .4
0 .6
0 .80.8
0.6
0.4
0.2
Corr
ela
tio
n c
oe
ffic
ien
t
0
IgG Vs. IgM
0
35,000
70,000
0 35,000 70,000
N_0.25_S
N_0.1_TN_0.1_WN_0.1_K
Lo
g 2
(Sig
na
l In
ten
sit
y)
S1_0.1
_T
S1_0.1
_S
S1_0.1
_B
S-R
BD
_0.1
_T
S-R
BD
_0.5
_S
S2-1
_0.0
5_T
S2-2
_0.1
_T
S1-4
_0.2
_K
0
4
8
1 2
1 6
**** **
** **
** **
**
Log
2(F
luore
scen
ce In
ten
sity)
** **** ** **
**
Log
2(F
luore
scen
ce In
ten
sity)
16
12
8
4
0
16
12
8
4
0
A. B.
0
1,000
2,000
3,000
0 5,000 10,000
S2
_0
.1_
T
S1_0.1_T
r=0.354
p=0.06
C. D. E.
F.LC169
NC96
N-C N-NG. H.
r=0.890
r=0.943
r=0.948
0
15,000
30,000
45,000
0 8,000 16,000
N_0
.1_W
S1_0.1_B
r=0.65
p<0.01
I. J. K.
0
2,000
4,000
6,000
0 10,000 20,000
IgM
IgG
S1_0.1_W
r=0.444
0
10,000
20,000
30,000
0 10,000 20,000
S1_0.1_B
S1_0.1_S
S1_0.1_T
r=0.984
r=0.928
0
6,000
12,000
0 10,000 20,000 30,000
S-R
BD
_0
.5_
S
S1_0.5_S
r=0.916
S IgG response N-protein IgG response
Figure 5. Antibody response to S and N proteins.
S1_0.1_T
S1_0.1_S
S1_0.25_S
S1_0.5_S
S1_0.1_B
S1_0.25_B
S1_0.5_B
S-RBD_0.1_Y
S-RBD_0.5_S
S1-4_0.2_K
S2-1_0.05_T
S2-2_0.1_T
S1_0.1
_T
S1_0.1
_S
S1_0.2
5_S
S1_0.5
_S
S1_0.1
_B
S1_0.2
5_B
S1_0.5
_B
S-R
BD
_0.1
_Y
S-R
BD
_0.5
_S
S1-4
_0.2
_K
S2-1
_0.0
5_T
S2-2
_0.1
_T
S1_0.1
_T
S1_0.1
_S
S1_0.2
5_S
S1_0.5
_S
S1_0.1
_B
S1_0.2
5_B
S1_0.5
_B
S.R
BD
_0.5
_Y
S.R
BD
_0.5
_S
S1.4
_0.2
_K
S2.1
_0.2
5_T
S2.2
_0.1
_T
S1_0.1_T
S1_0.1_S
S1_0.25_S
S1_0.5_S
S1_0.1_B
S1_0.25_B
S1_0.5_B
S-RBD_0.5_Y
S-RBD_0.5_S
S1-4_0.2_K
S2-1_0.25_T
S2-2_0.1_T
0.4
0.5
0.6
0.7
0.8
0.9
11
0.4
0.6
0.8
Lo
g 2
(Sig
na
l In
ten
sit
y)
N-p
rote
in_0.1
_T
N-p
rote
in_0.1
_K
N-p
rote
in_0.1
_W
N-p
rote
in_0.2
5_S
N-C
ter_
0.1
_K
N-N
ter_
0.1
_K
0
4
8
1 2
1 6
. CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprint this version posted March 27, 2020. .https://doi.org/10.1101/2020.03.20.20039495doi: medRxiv preprint
0
6,000
12,000
0 10,000 20,000 30,000
S1
-4_
0.2
_K
S1_0.5_S
r=0.845
p<0.01
0
10,000
20,000
30,000
0 10,000 20,000 30,000
S1_0.1_S
S1_0.25_S
S1_0.5_S
0
4,000
8,000
12,000
0 4,000 8,000 12,000
S-R
BD
_0
.5_
S
S-RBD_0.5_Y
r=0.966
0
1,000
2,000
3,000
0 1,000 2,000
S2
-2_
0.1
_T
S2-1_0.05_T
r=0.974
0
1,000
2,000
3,000
0 5,000 10,000
S2
-2_
0.1
_T
S1_0.1_T
r=0.354
p=0.06
0
35,000
70,000
0 25,000 50,000
N_0.1_W
N_0.25_W
N_0.5_W
r=0.981
N.p
rote
in_0.1
_T
N.p
rote
in_0.1
_K
N.p
rote
in_0.2
5_K
N.p
rote
in_0.5
_K
N.p
rote
in_0.1
_W
N.p
rote
in_0.2
5_W
N.p
rote
in_0.5
_W
N.p
rote
in_0.2
5_S
N.p
rote
in_0.5
_S
N.C
ter_
0.1
_K
N.C
ter_
0.2
5_K
N.C
ter_
0.5
_K
N.N
ter_
0.1
_K
N.N
ter_
0.2
5_K
N.N
ter_
0.5
_K
N-protein_0.1_T
N-protein_0.1_K
N-protein_0.25_K
N-protein_0.5_K
N-protein_0.1_W
N-protein_0.25_W
N-protein_0.5_W
N-protein_0.25_S
N-protein_0.5_S
N-Cter_0.1_K
N-Cter_0.25_K
N-Cter_0.5_K
N-Nter_0.1_K
N-Nter_0.25_K
N-Nter_0.5_K
0.7
0.75
0.8
0.85
0.9
0.95
1
0
35,000
70,000
0 20,000 40,000 60,000
N_0.1_K
N-Cter_0.1_KN-Nter_0.1_K
r=0.893
r=0.856
0
25,000
50,000
0 40,000 80,000
N-N
ter
N-Cter
r=0.676
p<0.01
r=0.998
r=0.987
A. B. C.
D. E. F.
G. H.
Figure S2. IgG response to S and N proteins.
N_0.1_T
N_0.1_K
N_0.25_K
N_0.5_K
N_0.1_W
N_0.25_W
N_0.5_W
N_0.25_S
N_0.5_S
N-Nter_0.1_K
N-Nter_0.25_K
N-Nter_0.5_K
N-Cter_0.1_K
N-Cter_0.25_K
N-Cter_0.5_K
1
0.7
0.8
0.9
N.p
rote
in_0.1
_T
N.p
rote
in_0.1
_K
N.p
rote
in_0.2
5_K
N.p
rote
in_0.5
_K
N.p
rote
in_0.1
_W
N.p
rote
in_0.2
5_W
N.p
rote
in_0.5
_W
N.p
rote
in_0.2
5_S
N.p
rote
in_0.5
_S
N.C
ter_
0.1
_K
N.C
ter_
0.2
5_K
N.C
ter_
0.5
_K
N.N
ter_
0.1
_K
N.N
ter_
0.2
5_K
N.N
ter_
0.5
_K
N-protein_0.1_T
N-protein_0.1_K
N-protein_0.25_K
N-protein_0.5_K
N-protein_0.1_W
N-protein_0.25_W
N-protein_0.5_W
N-protein_0.25_S
N-protein_0.5_S
N-Cter_0.1_K
N-Cter_0.25_K
N-Cter_0.5_K
N-Nter_0.1_K
N-Nter_0.25_K
N-Nter_0.5_K
0.7
0.75
0.8
0.85
0.9
0.95
1
N_0.1
_T
N_0.1
_K
N_0.2
5_K
N_0.5
_K
N_0.1
_W
N_0.2
5_W
N_0.5
_W
N_0.2
5_S
N_0.5
_S
N-N
ter_
0.1
_K
N-N
ter_
0.2
5_K
N-N
ter_
0.5
_K
N-C
ter_
0.1
_K
N-C
ter_
0.2
5_K
N-C
ter_
0.5
_K
I.
. CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprint this version posted March 27, 2020. .https://doi.org/10.1101/2020.03.20.20039495doi: medRxiv preprint
Lo
g 2
(Sig
na
l In
ten
sit
y)
S1_0.1
_T
S1_0.1
_S
S1_0.1
_B
S-R
BD
_0.1
_T
S-R
BD
_0.5
_S
S2-1
_0.2
5_T
S2-2
_0.1
_T
S1-4
_0.2
_K
0
4
8
1 2
1 6
**** **
** **
n.s. n.s.
*
Log
2(F
luore
scen
ce In
ten
sity)
16
12
8
4
0
A.
Lo
g 2
(Sig
na
l In
ten
sit
y)
N-p
rote
in_0.1
_T
N-p
rote
in_0.1
_K
N-p
rote
in_0.1
_W
N-p
rote
in_0.1
_S
N-C
ter_
0.1
_K
N-N
ter_
0.1
_K
0
4
8
1 2
1 6 **
**
**
**
**
**
Log
2(F
luore
scen
ce In
ten
sity) 16
12
8
4
0
B.S IgM response N-protein IgM response
Figure S3. IgM response to S and N proteins.
0
6,000
12,000
18,000
24,000
30,000
0 20,000 40,000
N_0.1_T
N_0.1_K
N_0.1_W
N_0.1_S
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
0 2,000 4,000 6,000 8,000
S1_0.1_S
S1_0.1_T
S1_0.1_B
r=0.817
r=0.908
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
0 2,000 4,000 6,000 8,000
S-R
BD
_0
.5_
S
S1_0.5_S
r=0.825
F. G. H.
0
200
400
600
800
1,000
1,200
0 1,000 2,000 3,000 4,000
S2
-2_
0.1
_T
S1_0.1_T
S1_0.1
_T
S1_0.1
_S
S1_0.2
5_S
S1_0.5
_S
S1_0.1
_B
S1_0.2
5_B
S1_0.5
_B
S.R
BD
_0.5
_Y
S.R
BD
_0.5
_S
S1.4
_0.2
_K
S2.1
_0.2
5_T
S2.2
_0.1
_T
S1_0.1_T
S1_0.1_S
S1_0.25_S
S1_0.5_S
S1_0.1_B
S1_0.25_B
S1_0.5_B
S-RBD_0.5_Y
S-RBD_0.5_S
S1-4_0.2_K
S2-1_0.25_T
S2-2_0.1_T
0
0.2
0.4
0.6
0.8
1
r=0.327
p=0.084 r=0.998
r=0.615
r=0.988
0
6,000
12,000
18,000
24,000
30,000
0 2,000 4,000 6,000 8,000
N_0
.1_W
S_0.1_B
r=0.225
p=0.241
C. D. E.
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Figure 6. IgG response to other SARS-CoV-2 proteins.
Protein COVID-19 Control
ORF9b 13/29 0/21
NSP5 3/29 1/21
NSP14 1/29 0/21
NSP10 1/29 0/21
NSP8 1/29 0/21
NSP16 1/29 1/21
N S P 5
Lo
g 2
(Sig
na
l In
ten
sit
y)
769
384.2
5
0
1 ,0 0 0
2 ,0 0 0
3 ,0 0 0
4 ,0 0 0
COVID-19 Control
Anti-NSP5 IgG
4000
3000
2000
1000
0
Flu
ore
sce
nce
In
ten
sity
O R F 9 b
Lo
g 2
(Sig
na
l In
ten
sit
y)
OR
F-9
b_0.1
_T
Contr
ol
0
5 0 0
1 ,0 0 0
1 ,5 0 0
3 5 0 0
4 0 0 0
COVID-19 Control
Anti-ORF9b IgG
4000
3500
1500
1000
500
0
Flu
ore
sce
nce
In
ten
sity
A. B.p=0.006 p=0.014
0
6,000
12,000
18,000
0
12,000
24,000
36,000
48,000
0 1,000 2,000 3,000 4,000
S1
N P
rote
in
ORF9b
N-protein_0.1_W
S1_0.1_B
0
6,000
12,000
18,000
0
12,000
24,000
36,000
48,000
0 1,000 2,000 3,000 4,000
S1
N P
rote
in
NSP5
N-protein_0.1_W
S1_0.1_B
D. E.
r=0.152
p=0.432
r=-0.021
p=0.915
r=0.257
p=0.179
r=-0.207
p=0.281
C.
. CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprint this version posted March 27, 2020. .https://doi.org/10.1101/2020.03.20.20039495doi: medRxiv preprint
0
4,000
8,000
12,000
200 400 600 800
S1
Ig
GPeak LDH
0
6,000
12,000
18,000
0 10 20 30 40
S1
Ig
G
Ly%
Figure 7. Correlation with clinical characteristics
0
4,000
8,000
12,000
16,000
0 20 40 60 80
S1
Ig
G
Age
0
6,000
12,000
18,000
0 10 20 30 40
S1
Ig
G
Days after onset
0
2,000
4,000
6,000
20 25 30 35 40
S1
Ig
G
Age
MaleFemaleMale (< 40 )
Male
0
6,000
12,000
18,000
0 25 50
S1
Ig
G
Ly%
Female
r=0.846
p=0.016
r=0.429
p=0.02
0
15,000
30,000
45,000
0 20 40 60 80
M
F
r=0.763
p<0.01
r=0.382
p=0.108
AgeS
1 Ig
G
r=0.86
p<0.01 r=0.227
p=0.455
r=-0.683
p=0.01r=-0.508
p=0.045
M a le a n d F a m a le
<40
≥40
<40
≥40
0
5 ,0 0 0
1 0 ,0 0 0
1 5 ,0 0 0
2 0 ,0 0 0
n.s.
*n.s. *
Male Female
S1
Ig
G
A. B. C.
D. E. F.
G. H. I.
0
6,000
12,000
200 400 600 800
S1
Ig
G
Peak LDH
r=0.79
p<0.01
Female (partial)
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0
10
20
30
40
50
60
0
10
20
30
40
50
60
70
200 400 600 800
Days
aft
er
onset
Age
Peak LDH
AgeDays after onset
Figure S4. Correlation with clinical information
0
10,000
20,000
30,000
40,000
50,000
60,000
0 40 80
N I
gG
Age
Mr=0.627
p<0.01
r=0.588
p=0.035
0
5
10
15
20
25
30
35
20 30 40
Da
ys a
fte
r o
nse
t
Age
0
6,000
12,000
18,000
24,000
30,000
20 30 40
N I
gG
Age
r=0.828
p=0.021
r=0.372
p=0.412
Male <40 Male <40
0
6,000
12,000
18,000
0 40 80
S1
Ig
G
Age
0
15,000
30,000
45,000
0 20 40 60 80
N I
gG
Age
r=0.751
p<0.01
r=0.627
p<0.01
Female Female
16
17
18
19
20
21
22
23
24
0 50 100
Days a
fte
r o
nse
t
Age
0
20,000
40,000
0 40 80
N I
gG
Age
r=0.629
p=0.029
r=-0.101
p=0.756
r=0.177p=0.625
r=0.559p=0.093
Female
0
20
40
60
80
0 20 40 60
Ag
e
Ly%
0
15,000
30,000
45,000
0 20 40 60
N Ig
G
Ly%
r=-0.573
p=0.02
r=-0.626
p<0.01
Female Female
A. B. C.
D. E. F.
G. H. I.
J. K. L.
Female (partial)
Female (partial)
0
6,000
12,000
18,000
200 400 600 800 1,000
S1
Ig
G
Peak LDH
Female
0
10,000
20,000
30,000
40,000
200 400 600 800
N Ig
G
Peak LDH
r=0.806
p<0.01
Female (partial)
r=0.699
p<0.01
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Table 1. Serum samples tested in this study.
Patient Group n=29
GenderMale 13
Female 16
Age 42.3±13.8
Severitymild cases 3
common cases 26
Days after onset 22.3±5.4
hospital stay (days) 17.9±5.7
Control group n=21
Lung cancer patients 10
GenderMale 5
Female 5
Age 55.9±7.3
sample collection data (year) 2017
Health control 11
GenderMale 6
Female 5
Age 45.1±12.9
sample collection data (year) 2017-2018
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