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Original article
Discovery of new serum biomarker panels for
systemic lupus erythematosus diagnosis
Hua-Zhi Ling 1,2,3,*, Shu-Zhen Xu1,3,*, Rui-Xue Leng1,3, Jun Wu1,
Hai-Feng Pan1,3, Yin-Guang Fan1, Bin Wang1, Yuan-Rui Xia1, Qian Huang3,
Zong-Wen Shuai4 and Dong-Qing Ye1,3
Abstract
Objective. Clinical diagnosis of SLE is currently challenging due to its heterogeneity. Many autoantibodies are
associated with SLE and are considered potential diagnostic markers, but systematic screening and validation of
such autoantibodies is lacking. This study aimed to systematically discover new autoantibodies that may be good
biomarkers for use in SLE diagnosis.
Methods. Sera from 15 SLE patients and 5 healthy volunteers were analysed using human proteome microarrays
to identify candidate SLE-related autoantibodies. The results were validated by screening of sera from 107 SLE
patients, 94 healthy volunteers and 60 disease controls using focussed arrays comprised of autoantigens corre-
sponding to the identified candidate antibodies. Logistic regression was used to derive and validate autoantibody
panels that can discriminate SLE disease. Extensive ELISA screening of sera from 294 SLE patients and 461 con-
trols was performed to validate one of the newly discovered autoantibodies.
Results. A total of 31, 11 and 18 autoantibodies were identified to be expressed at significantly higher levels in
the SLE group than in the healthy volunteers, disease controls and healthy volunteers plus disease control groups,
respectively, with 25, 7 and 13 of these differentially expressed autoantibodies being previously unreported.
Diagnostic panels comprising anti-RPLP2, anti-SNRPC and anti-PARP1, and anti-RPLP2, anti-PARP1, anti-MAK16
and anti- RPL7A were selected. Performance of the newly discovered anti-MAK16 autoantibody was confirmed by
ELISA. Some associations were seen with clinical characteristics of SLE patients, such as disease activity with the
level of anti-PARP1 and rash with the level of anti-RPLP2, anti-MAK16 and anti- RPL7A.
Conclusion. The combined autoantibody panels identified here show promise for the diagnosis of SLE and for
differential diagnosis of other major rheumatic immune diseases.
Key words: systemic lupus erythematosus, autoantibodies, biomarker, diagnosis
Introduction
SLE is generally a chronic, inflammatory disease that
causes multi-organ injury, including damage to the kid-
neys, blood, brain and skin [1–3]. Features of SLE
include periods of flare-up and remission, dysregulation
of the immune system and development of autoantibod-
ies [4]. The diagnosis of SLE is challenging due to the
heterogeneity of its clinical course, symptoms and
Rheumatology key messages
. Many differentially expressed autoantibodies were newly identified in SLE patients.
. Autoantibody panels discovered in this study may be good biomarkers for SLE diagnosis.
. Some associations exist between the autoantibodies identified in this study and clinical characteristics of SLE
patients.
1Department of Epidemiology and Biostatistics, School of Public
Health, Anhui Medical University, 2Department of Clinical
Laboratory, the First Affiliated Hospital of Anhui Medical University,
3Anhui Province Key Laboratory of Major Autoimmune Diseases and
4Department of Rheumatology and Immunology, the First Affiliated
Hospital of Anhui Medical University, Hefei, Anhui, China
Submitted 16 August 2019; accepted 26 November 2019
*Hua-Zhi Ling and Shu-Zhen Xu contributed equally to this work.
Correspondence to: Dong-Qing Ye, Department of Epidemiology and
Biostatistics, School of Public Health, Anhui Medical University, 81
Meishan Road, Hefei, Anhui 230032, PR China.
E-mail address: ydq@ahmu.edu.cn
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VC The Author(s) 2020. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For permissions, please email: journals.permissions@oup.com
Rheumatology
Rheumatology 2020;59:1416–1425
doi:10.1093/rheumatology/kez634
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disease severity [5–7]. Serum biomarkers used in the
diagnosis of SLE are mainly autoantibodies specific for
intracellular antigens located in the cell nucleus or cyto-
plasm. About 180 autoantibodies have been identified in
SLE patients, 102 of which are reported to have an
organ-specific correlation with SLE disease activity [8].
However, with the exception of autoantibodies such as
ANA, anti-dsDNA, anti-Sm and aPL, currently proposed
by the ACR [9] and SLICC [10] for the diagnosis of SLE,
most of these autoantibodies lack sufficient sensitivity
and/or specificity for use in clinical diagnosis. Discovery
of additional autoantibodies with high sensitivity and
specificity is important for early diagnosis and assess-
ment of the prognosis of SLE.
Protein microarrays have been widely used in the
diagnosis, prediction and prognosis of many diseases
and have contributed greatly in the development of
modern medicine. The HuProt human proteome micro-
array, developed at John Hopkins University, is the most
comprehensive and high-throughput human proteome
microarray currently available and has been used to
identify many new biomarkers for different diseases [11–
13]. A small study using HuProt to identify biomarkers
for neuropsychiatric SLE demonstrated the potential of
this proteome microarray for discovering novel autoanti-
bodies suitable for the diagnosis of SLE [14]. However,
additional reports on systematic screening and valid-
ation of autoantibodies for SLE are lacking. Here we
applied a previously reported two-phase strategy [11] to
comprehensively discover and then validate autoanti-
bodies that may be good biomarkers for use in the diag-
nosis of SLE. We then validated one of the previously
unreported autoantibodies, anti-MAK16, using ELISA.
Methods
Study design
A two-phase case–control study design was adopted. A
total of 125 SLE patients, 111 healthy volunteers and 60
disease control patients with RA or primary Sjögren’s
syndrome (pSS) were enrolled in the study. In phase I,
serum samples from 15 SLE patients and 5 healthy vol-
unteers were screened for autoantibodies with human
proteome microarrays to obtain differential serum auto-
antibody profiles. In phase II, focussed microarrays
comprised of the SLE differentially expressed autoanti-
gens identified in phase I were constructed and used to
screen sera from 110 SLE, 30 RA and 30 pSS patients
and 106 healthy volunteers to examine the sensitivity
and specificity of these potential SLE diagnostic bio-
markers. Bioinformatic analyses were performed to iden-
tify autoantibodies with diagnostic potential that could
be used in different comparisons. Combinations of
markers that could form potential SLE diagnostic panels
were identified by modelling and subsequent validation.
Two-thirds of the data from the SLE patients and con-
trols (healthy volunteers, disease controls or healthy
plus disease controls), selected at random, was used for
modelling and the remaining third for validating the po-
tential biomarkers. To validate the performance of the
autoantibody biomarkers discovered for SLE diagnosis,
we selected the autoantibody anti-MAK16 as represen-
tative for further testing by ELISA, first on 94 SLE
patients and 61 healthy volunteers from phase II, then
on a further 600 new samples, including serum samples
from 200 SLE patients, 200 healthy volunteers and 200
patients with other autoimmune diseases (80 RA, 80
pSS, 18 undifferentiated connective tissue disease, 13
dermatomyositis, 5 systemic sclerosis and 4 vasculitis)
(Fig. 1).
Serum samples and general clinical information
All serum samples involved in this study were collected
at the First Affiliated Hospital of Anhui Medical
University or Anhui ProvincialHospital between February
2016 and January 2019. SLE patients included in this
study were selected based on SLICC 2009 criteria for
SLE diagnosis [10] and disease activity was evaluated
using the SLEDAI-2000 (SLEDAI-2K) [15]. Active SLE
disease was defined as a SLEDAI-2K score >10. RA
and pSS patients included in this study as disease con-
trols met the criteria of the joint working group from the
ACR and the European League Against Rheumatism in
2010 [16] and the revised criteria of the European Study
Group on Classification Criteria for SS in 2002 [17], re-
spectively. Other major autoimmune diseases were
diagnosed using the latest clinical diagnostic criteria.
Healthy volunteers were recruited during annual health
checks in the two hospitals. Volunteers that met any of
the diagnostic criteria for SLE, were long-term users of
glucocorticoids or immunosuppressive drugs or had a
history of a major disease or autoimmune disease were
excluded from the study. Healthy volunteers and SLE
patients were matched by gender and age. Serum sam-
ples were obtained by collecting 5 ml of peripheral blood
in a gel coagulation-promoting vacuum tube and were
separated at 3500 rpm for 5 min at room temperature
within 2 h after collection. Sera were then placed in
1.5 ml tubes and stored at �80�C until use.
Demographic and general clinical information on the
study population in the two phases of this study is
presented in Supplementary Table S1, available at
Rheumatology online.
This study was approved by the Ethics Committee of
Anhui Medical University and informed consent was
obtained from all patients and volunteers prior to the
start of the study.
Proteome microarrays and serum profiling assays
HuProt arrays version 3.0 (CDI Laboratories, Baltimore,
MD, USA), each comprising 20 240 unique human full-
length proteins, were used. Each serum sample was
diluted 1:200 using incubation buffer (1% BSA in PBS
buffer with 0.1% Tween 20) to give a total volume of
3 ml and the arrays were blocked using blocking buffer
(3% BSA in PBS buffer with 0.1%Tween 20) for 3 h at
SLE biomarker panels
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room temperature. Blocked HuProt arrays were placed
in the diluted serum samples and were incubated for
12 h at 4�C with humidity and shaking. Subsequent pro-
cedures were as described previously [11–13].
Construction of SLE focussed microarrays and
serum profiling assays
Candidate human IgG protein biomarkers identified in
the HuProt array experiments from phase I were chosen
to construct SLE focussed arrays on slides with a 2�7
subarray format. To avoid cross-contamination, a 14-
chamber rubber gasket divided each slide into 14 indi-
vidual chambers so that 14 individual serum assays
could be performed at the same time. The screening
procedure was similar to that in phase I, except that
serum samples were diluted 1:100 and 200 ll diluted
serum samples were added to each block of the array.
Selection of SLE diagnostic biomarker panels
While several dozen autoantibodies were found to be
related to SLE, no single autoantibody showed excellent
sensitivity and specificity for SLE. We therefore eval-
uated different combinations of biomarkers by logistic
regression to obtain a biomarker panel with good per-
formance in diagnosing SLE. Data were randomly div-
ided into two sets for each of the three comparisons,
with one set serving as a training set and the other as a
test set. The model was established by stepwise logistic
regression and then checked by leave-one-out cross-
validation [18]. A test set of samples was used to verify
the first set using stepwise logistic regression and the
model established was then evaluated using a receiver
operator characteristics (ROC) curve.
ELISA validation of autoantibody anti-MAK16
The human anti-MAK16 autoantibody was detected
using indirect ELISAs. A 96-well plate was coated with
purified MAK16 protein and serum samples and horse-
radish peroxidase–labelled anti-human IgG were added
sequentially to form an antigen-antibody complex.
Optical density (OD) values were read using a micro-
plate reader (see Supplementary materials 1, available
at Rheumatology online).
Microarray assay quality control
In experiments in phase I, glutathione-S-transferase
(GST) fusion proteins were dotted on microarrays (dupli-
cate spots for each protein) and anti-GST antibody and
Cy3-labelled secondary antibody were then added suc-
cessively to assess the quality of each array. In phase II,
in addition to the quality control experiment performed
in phase I, we incubated different arrays with a mixed
sample of 10 different sera, chosen at random, to evalu-
ate the stability and reproducibility of different
experiments.
Data processing and analysis for assays performed
in phases I and II
The median foreground and background signal inten-
sities were obtained for each spot on the protein arrays
using GenePix Pro 6.0 software (Molecular Devices,
Sunnyvale, CA, USA). After extraction, data were
FIG. 1 Study design
HC: healthy controls; DC: disease controls.
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processed and analysed (see Supplementary materials,
available at Rheumatology online).
Statistical analysis
The t test (when the dataset was normally distributed) or
Mann–Whitney U test was used to compare continuous
data and the v2 or Fisher’s exact test was used for com-
paring categorical variables. The diagnostic value of po-
tential biomarkers was assessed by performing ROC
analysis and calculating the area under the curve (AUC).
Logistic regression was performed to build models for
SLE biomarker panels. The cut-off for statistical signifi-
cance was set at P< 0.05. All data analyses were per-
formed with R statistical software (version 3.3.3; R
Foundation, Vienna, Austria) and related packages.
Results
Reproducibility and reliability of protein microarray
assays
In phase I, signal-to-noise ratios (SNRs) were calculated
for all proteins and compared with those of the negative
controls to evaluate the detection rate of proteins
(Supplementary Fig. S1A, available at Rheumatology
online). Statistical analysis showed that >99% of the
proteins were detectable. Linear fitting was performed
using the mean foreground value for duplicate spots
hybridized with anti-GST. The fitted regression equation
obtained was y¼ 0.9447xþ0.0335 with R2¼ 0.9598
(Supplementary Fig. S1B, available at Rheumatology on-
line), indicating good reproducibility between arrays.
The same analysis was repeated on data from phase
II. The fitted regression equation obtained was
y¼0.9808xþ 144.26 with R2¼ 0.9804 (Supplementary
Fig. S1C, available at Rheumatology online). To evaluate
stability and reproducibility between experiments, the
same mixed serum sample was assayed a total of 14
times. The average correlation coefficient between
experiments was 0.97 (Supplementary Fig. S2, available
at Rheumatology online), indicating that results between
experiments had good overall stability and reproducibil-
ity (Supplementary Fig. S3, available at Rheumatology
online).
Differentially expressed serum autoantibodies
A total of 74 differentially expressed IgG autoantibodies
were identified in phase I, and serum samples from 110
SLE patients, 106 healthy volunteers and 60 disease con-
trols were then assayed using focussed microarrays to
validate these autoantibodies in phase II. Signal back-
grounds for most samples were low, and sampleswith
high backgrounds (3 SLE patients and 12 healthy volun-
teers) were excluded by setting a cut-off value to elimin-
ate signal interference (Supplementary Fig. S4, available
at Rheumatology online). A total of 31 potential bio-
markers were selected by comparing SLE patient data
with that of healthy volunteers (Table 1) and 11 potential
biomarkers were selected by comparing SLE patients
with disease controls (Supplementary Table S2, available
at Rheumatology online). There were significant differen-
ces between the biomarkers for the SLE and healthy
volunteer (Fig. 2) and SLE and disease control
(Supplementary Fig. S5, available at Rheumatology on-
line) comparisons; nonetheless, a few samples from SLE
patients with low SNRs were misclassified into the con-
trol groups during the clustering process (Supplementary
Fig. S6A and B, available at Rheumatology online). When
we combined data for the healthy volunteers and disease
controls into one group, 18 potential biomarkers were
identified (Supplementary Table S3); nonetheless, a few
samples from SLE patients with low SNRs were still mis-
classified into the control group (Supplementary Fig. S6C,
available at Rheumatology online). Of note, 25, 7 and 13
of the autoantibodies that were differentially expressed in
the SLE vs healthy volunteers, SLE vs disease controls
and SLE vs healthy volunteers plus disease controls com-
parisons were previously unreported.
Efficiency of the biomarker panel in SLE diagnosis
Since a diverse range of autoantibodies are associated
with SLE, we combined individual markers using logistic
regression to identify a panel of markers with optimal
diagnostic value. We used bootstrapping (�1000) to se-
lect the model with optimal age and gender matching in
the case control group. Demographics of study partici-
pants in the training and testing sets are shown in
Supplementary Table S4, available at Rheumatology on-
line. Models for the training sets were built using the lo-
gistic regression models: logit(P¼SLE) ¼�3.080þ 0.061
RPLP2-IgGþ 0.048 PARP1-IgGþ0.135 SNRPC-IgG for
the SLE vs healthy volunteer comparison; logit(P¼SLE)
¼�0.970þ0.015 RPLP2-IgGþ 0.023 PARP1-IgGþ 0.378
MAK16-IgG�0.681 RPL7A-IgG for the SLE vs disease
control comparison and logit(P¼SLE) ¼�2.258þ
0.020 RPLP2-IgGþ 0.049 PARP1-IgGþ0.516 MAK16-
IgG� 0.878 RPL7A-IgG for the SLE vs healthy volunteer
plus disease control comparison (Supplementary Table
S5, available at Rheumatology online). The diagnostic
value of the data models derived from the training sets
was validated with data from the test sets by examining
ROC curves. The AUC value of the ROC curve was
improved from 0.648–0.932, 0.597–0.828 and 0.656–
0.888 with single biomarkers to 0.973, 0.911 and 0.940
with the biomarker panel for the SLE vs healthy volunteer
comparison, SLE vs disease control comparison and SLE
vs healthy volunteer plus disease control comparison, re-
spectively (Fig. 3).
Associations of autoantibodies in the biomarker
panel with the clinical characteristics of SLE
patients
The expression level of serum PARP1-IgG in the SLE
disease-active group was higher than that in the inactive
group (Z¼�3.186, P¼0.001) and levels of serum
MAK16-IgG, RPLP2-IgG and RPL7A-IgG in SLE patients
who had a rash were higher than in those with no rash
SLE biomarker panels
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(P<0.05). The expression levels of serum PARP1-IgG
and RPLP2-IgG were higher in the thrombocytopenia
group than in the normal platelet group (P< 0.05). There
were no statistically significant associations between the
expression levels of serum PARP1-IgG, MAK16-IgG,
RPLP2-IgG and RPL7A-IgG and other clinical character-
istics of SLE patients (P>0.05; Table 2).
Validation of anti-MAK16 using ELISAs
The demographics of the study population used for
ELISA screening are shown in Supplementary Table S6,
available at Rheumatology online. We normalized the
SNR values from the protein microarray assays so that
they could be shown in the same figure as the OD val-
ues for sera from phase II. Results showed that there
was good consistency between the protein microarray
and ELISA methods (Supplementary Fig. S7, available at
Rheumatology online). OD values and boxplots for the
comparisons between SLE patients and healthy volun-
teers and SLE patients and disease controls for newly
collected serum samples showed that there were signifi-
cant differences between SLE patients and controls
(Fig. 4A and 4B). The AUC of the ROC curve for ELISA
tests performed on newly collected sera for the SLE vs
healthy volunteer and SLE vs disease control compari-
sons was 0.698 and 0.671, respectively (Fig. 4C and
4D), demonstrating the potential of the anti-MAK16
autoantibody for the diagnosis and differential diagnosis
of SLE. However, some differences were apparent in
AUC values at the two stages of ELISA testing due to
differences in the samples used.
Discussion
SLE, a typical systemic autoimmune disease, is character-
ized by the production of multiple autoantibodies, immune
complex deposition and activation of complement that to-
gether lead to multi-organ injury [19]. Many distinctive
autoantibodies have been identified in SLE patients and
detection of autoantibodies is one important criteria in
SLE diagnosis [10]. However, the number of clinically
valuable autoantibodies is limited. Here, to comprehen-
sively identify specific autoantibodies for SLE diagnosis,
we used a two-phase strategy to assay serum samples
from SLE patients and controls using the most compre-
hensive and highest-throughput human proteome micro-
array available, as well as SLE-focussed microarrays.
After statistical and bioinformatic analyses with defined
stringent criteria, we identified some autoantibodies that
were differentially expressed between SLE patients and
controls and thus had potential as biomarkers.
The 11 differentially expressed proteins identified in the
SLE vs disease control comparison were identical with
those differentially expressed in the SLE vs healthy
FIG. 2 ROC curves forthe three best single SLE diagnostic biomarkers identified in this study and their boxplots for
the comparison between SLE patients and healthy volunteers
The AUC values from the ROC curves in (A–C) are all >0.9 and the mean SNR of the three IgGs in SLE patients in
(D–F) are all higher than those in healthy volunteers (P<0.001), demonstrating that these are good biomarkers for the
diagnosis of SLE.
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volunteer comparison, except that DCX (the neuronal mi-
gration protein doublecortin) was differentially expressed
in the SLE vs disease control comparison but not in the
SLE vs healthy volunteer comparison. All 18 proteins dif-
ferentially expressed in the SLE vs healthy volunteer plus
disease control comparison were identical with those
TABLE 1 Proteins corresponding to the 31 differentially expressed autoantibodies identified using SLE-focussed arrays
when comparing the SLE and healthy volunteer groups
Gene name Cut-off Fold change P-value SLE-positive rate (%) Sensitivity Specificity AUC New
RPLP1 21.324 15.157 <0.001 67.29 0.682 0.979 0.932 No
PARP1 31.026 8.307 <0.001 69.16 0.692 0.957 0.929 No
RPLP2 36.616 16.072 <0.001 63.55 0.645 0.979 0.923 No
RPLP0 10.606 13.035 <0.001 66.36 0.664 0.979 0.905 No
SNRPC 6.195 12.227 <0.001 66.36 0.729 0.989 0.898 No
HIST1H1C 16.721 4.896 <0.001 55.14 0.551 0.968 0.867 Yes
H1F0 21.804 4.351 <0.001 48.60 0.486 0.979 0.860 Yes
TROVE2 29.708 6.164 <0.001 58.88 0.617 0.957 0.853 No
HK1 10.426 6.471 <0.001 52.34 0.523 0.957 0.833 Yes
SRSF7 46.279 4.988 <0.001 41.12 0.439 0.979 0.827 Yes
RPL14 4.105 2.657 <0.001 54.21 0.570 0.957 0.816 Yes
MAK16 4.036 4.201 <0.001 55.14 0.579 0.968 0.810 Yes
SRSF6 6.077 4.803 <0.001 50.47 0.505 0.947 0.800 Yes
RPL23A 4.723 3.377 <0.001 49.53 0.505 0.957 0.800 Yes
SP5 7.056 2.203 <0.001 40.19 0.421 0.957 0.792 Yes
USF1 6.244 2.758 <0.001 34.58 0.364 0.957 0.779 Yes
FOXC2 11.892 1.955 <0.001 23.36 0.299 0.926 0.769 Yes
NFATC2 4.485 2.344 <0.001 31.78 0.458 0.957 0.750 Yes
RPL35 4.319 2.429 <0.001 44.86 0.477 0.947 0.748 Yes
RELL1 5.671 1.792 <0.001 28.97 0.364 0.947 0.727 Yes
RPL7A 2.987 2.245 <0.001 38.32 0.383 0.979 0.724 Yes
PIK3CG 7.567 1.716 <0.001 25.23 0.336 0.915 0.710 Yes
JUND 2.669 1.680 <0.001 34.58 0.421 0.968 0.700 Yes
NOC3L 5.289 1.618 <0.001 28.04 0.308 0.947 0.697 Yes
DBP 3.930 1.637 <0.001 31.78 0.318 0.947 0.680 Yes
DGCR8 3.669 1.528 <0.001 20.56 0.224 0.968 0.679 Yes
CDK19 3.856 1.664 <0.001 28.04 0.280 0.957 0.675 Yes
GABRA4 4.129 1.661 <0.001 30.84 0.318 0.968 0.670 Yes
LLPH 3.565 1.570 <0.001 26.17 0.262 0.979 0.667 Yes
MAST3 4.163 1.504 <0.001 20.56 0.243 0.968 0.648 Yes
TBX1 4.083 1.727 <0.001 22.43 0.243 0.947 0.630 Yes
FIG. 3 ROC curves for the biomarker panel using the test set samples
(A) SLE vs healthy volunteer comparison. (B) SLE vs disease control comparison. (C) SLE vs healthy volunteer plus
disease control comparison. The AUC value of the ROC curve for each biomarker panel was higher than that for any
single biomarker in each of the different comparisons.
SLE biomarker panels
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differentially expressed in the SLE vs healthy volunteer
comparison. These include autoantibodies that are com-
mon in autoimmune diseases (SNRPC and PARP1) and
others that are specific to SLE patients (RPLP0, RPLP1,
RPLP2, TROVE2) [14, 20–25]. A total of 25, 7 and 13 of
the differentially expressed autoantibodies were identified
for the first time in the three comparisons performed here
(Table 1, Supplementary Tables S2 and S3, available at
Rheumatology online), reflecting the diversity of autoanti-
bodies produced by SLE patients. Of note, seven of the
differentially expressed proteins from the SLE vs healthy
volunteer and SLE vs disease control comparisons were
components of the 60S ribosomal protein. Analysis of
protein–protein interaction networks of the 18 differentially
expressed proteins in the SLE patients vs healthy volun-
teer comparison indicated that 60S ribosomal protein
components showed multiple interactions with each other
and with MAK16 (Supplementary Fig. S8, available at
Rheumatology online), suggesting that antibodies to the
60S ribosomal protein play an important role in the SLE
pathogenic process and that protein interactions between
MAK16 and these proteins may be important. RPLP2,
RPLP1 and PRLP0 IgG antibodies were among the top
four autoantibodies in all the comparisons in terms of
their AUC values in ROC curves and are autoantibodies
that target highly conserved ribosomal phosphoproteins
called ribosomal P proteins [21]. The anti-ribosomal P
antibodies have been shown to be specific to SLE and
are related to the injury of organs such as the kidney,
liver and nervous system [21, 26]. The prevalence of
these antibodies is reported to range from 6 to 46% in
SLE patients and varies with race [26], these antibodies
being detected more frequently in Asians than in Africans
and Caucasians. While the AUC values of ROC curves
TABLE 2 Associations of autoantibodies in the biomarker panel with clinical characteristics of SLE patients
Clinical characteristic Yes/no PARP1 MAK16 RPLP2 RPL7A
Active SLE patients 75/32
Median (yes/no) 67.159/37.569 7.691/10.128 91.021/91.084 2.467/2.094
P-value 0.002 0.844 0.721 0.541
Lupus nephritis 67/40
Median (yes/no) 58.764/54.174 7.685/9.496 91.021/92.527 2.357/2.533
P-value 0.923 0.337 0.638 0.321
Vasculitis 5/102
Median (yes/no) 126.555/54.174 9.844/7.870 168.735/87.349 2.615/2.391
P-value 0.690 0.497 0.184 0.345
Arthritis 31/76
Median (yes/no) 63.401/48.849 9.757/7.527 99.901/71.687 2.914/2.289
P-value 0.161 0.157 0.343 0.122
Myositis 9/98
Median (yes/no) 48.152/55.391 10.182/7.870 110.234/90.958 4.207/2.391
P-value 0.487 0.141 0.582 0.151
Pleuritis 3/104
Median (yes/no) 137.095/54.174 7.863/7.941 34.091/91.148 2.467/2.410
P-value 0.497 0.895 0.940 0.485
Pericarditis 21/86
Median (yes/no) 43.327/55.391 6.674/8.751 34.091/92.717 2.425/2.391
P-value 0.695 0.259 0.347 0.754
Rash 67/40
Median (yes/no) 55.523/52.404 9.712/5.364 110.234/27.302 2.823/1.903
P-value 0.347 0.010 0.021 0.001
Alopecia 17/90
Median (yes/no) 53.088/ 56.685 7.877/7.941 117.520/86.895 2.531/2.312
P-value 0.701 0.683 0.195 0.413
Oral ulcer 8/99
Median (yes/no) 113.214/52.678 18.742/7.863 164.704/83.804 4.915/2.376
P-value 0.110 0.232 0.100 0.260
Low complement 90/17
Median (yes/no) 59.454/42.876 8.043/5.443 92.717/46.376 2.446/2.100
P-value 0.226 0.380 0.562 0.603
Leukopenia 22/85
Median (yes/no) 46.189/60.144 7.897/8.119 102.631/80.899 2.313/2.425
P-value 0.823 0.775 0.328 0.616
Thrombocytopenia 26/81
Median (yes/no) 28.449/67.159 7.125/8.859 28.734/110.234 1.983/2.536
P-value 0.005 0.093 0.024 0.148
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for the three antibodies determined here in the three
comparisons were all >0.75, and the positive rate was
>45%, their higher prevalence in our study may have
resulted from the use of different detection methods and
different sample populations. The anti-PARP1 antibody
targeting protein poly (ADP-ribose) polymerase 1 is a nu-
cleoprotein found widely in eukaryotic cells and is highly
expressed in proliferative nuclei. Many studies have
shown that PARP1 is overexpressed in neoplastic dis-
easessuch as endometrial cancer [27] and neuroblast-
oma [28]. In addition, PARP1 plays an important role in
DNA damage repair, gene transcription and the mainten-
ance of normal cell apoptosis [29]. Compared with
healthy volunteers, the positive rate of serum PARP1-IgG
in the SLE group was as high as 69.16% and the ROC
curve showed an AUC value of 0.929. These results sug-
gest that serum PARP1-IgG is a marker of SLE and plays
an important role in SLE progression.
In the diagnostic biomarker panel for the SLE vs healthy
volunteer comparison, in addition to RPLP2-IgG and
PARP1-IgG, SNRPC-IgG was a good marker for SLE
screening and had an AUC value of 0.898 and an SLE
positive detection rate of 66.36%. The SNRPC protein
(U1 small nuclear ribonucleoprotein C) is a component of
the spliceosomal U1 small nuclear ribonucleoprotein
(snRNP) and is directly involved in initial 5’ splice-site rec-
ognition for both constitutive and regulated alternative
splicing [22]. Detection of U1-snRNP autoantibodies using
haemagglutination assays in another study showed a
positive detection rate of 53.5% in patients with SLE [30].
Mesa et al. [23] reported that the anti-U1 snRNP IgG anti-
body can distinguish SLE and MCTD from healthy volun-
teers with an accuracy of 94.1%. MAK16-IgG and
RPL7A-IgG were included here in the biomarker panel for
the SLE vs disease control and healthy volunteer plus dis-
ease control comparisons, in addition to RPLP2-IgG and
PARP1-IgG. MAK16 protein is located in the nucleolus
and is involved in the key processes of RNA cleavage,
translocation, localization, editing and post-transcriptional
regulation of mRNA [31, 32]. The ROC curve for the com-
parison of SLE patients and healthy volunteers gave AUC
values >0.8, indicating that MAK16-IgG antibodies may
be good biomarkers for the diagnosis of SLE. Our study
is the first to show that anti-MAK16 antibodies are associ-
ated with SLE (results from microarray experiments, vali-
dated by ELISA), although some differences exist among
different populations. It will be important to explore the
function of these antibodies in the pathogenesis of SLE
and tissue damage in future studies. Ribosomal protein
L7A (RPL7A) is a component of the ribosomal 60S large
subunit and is closely related to cell growth and differenti-
ation [33]. Compared with controls, the levels of SLE
serum RPL7A-IgG were significantly higher (P< 0.01),
suggesting that RPL7A-IgG may also be a valuable SLE
diagnostic marker. Previous studies have suggested that
abnormal expression of RPL7A may be related to some
cancers [34, 35], but this is the first study to look for a
correlation between RPL7A expression and SLE.
Fig. 4 ELISA validation of anti-MAK16 IgG
(A) OD values and (B) boxplots for ELISAs for the three groups (600 newly collected serum samples, 200 each from
SLE patients, healthy volunteers and disease controls). ROC curves for the newly collected samples for the (C) SLE
vs healthy volunteer and (D) SLE vs disease control comparisons.
SLE biomarker panels
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Detection of ANA, anti-dsDNA, anti-Sm and aPL is an
important criterion in the evaluation of SLE. ANA detec-
tion is commonly performed in clinical practice using a
fluorescent assay as the gold standard. However, �20–
30% of the healthy population is positive for ANA and
some SLE patients are ANA negative [36], limiting its
use in the diagnosis of SLE. In addition, different detec-
tion methods result in different ANA-positive detection
rates [37], further reducing its value as an independent
test. Anti-dsDNA, anti-Sm and aPL can only be detected
in 40–60% [38], �25% [39, 40] and 30–40% [41] of
patients with SLE, respectively. Here we optimized a
panel of biomarkers with three autoantibodies, obtaining
an AUC of 0.973 for SLE diagnosis, and a biomarker
panel of four autoantibodies, obtaining an AUC of 0.911
for SLE differential diagnosis. Use of these newly dis-
covered panels of autoantibodies should improve diag-
nosis and differential diagnosis of SLE.
Our study has some limitations. We aimed to examine
autoantibodies to human protein components but did
not take DNA, RNA or lipid components into account.
These components should be examined in a separate
study. The study population included in phase I (15 SLE
patients and 5 healthy volunteers) was small due to the
cost of performing proteome microarray profiles. In
phase II, the disease types selected in the disease con-
trol group were not comprehensive enough and the
number of samples we could test was low. Further
experiments with more disease types and larger sample
sizes will be necessary in the future.
In summary, we have systematically profiled human
autoantibodies against SLE using the most comprehen-
sive and highest-throughput human proteome micro-
array currently available and evaluated their potential as
biomarkers for SLE using an SLE-focussed protein
microarray, identifying 31 autoantibodies with potential
as biomarkers for diagnosis and 11 for differential diag-
nosis of SLE. After data modelling and statistical ana-
lysis, we selected biomarker panels comprising three
and four biomarkers with high AUC values for SLE diag-
nosis and differential diagnosis, respectively, validating
one of these new biomarkers, anti-MAK16, using
ELISAs. Further validation and application of the com-
bined biomarker panels in clinical practice is expected.
Funding: This work was supported by grants from the
Research Project of Anhui Provincial Institute of transla-
tional medicine (no. 2017zhyx21 to D.-Q.Y.), the National
Natural Science Foundation of China (no. 81872693 to D.-
Q.Y.) and the Key Research and Development Plan
Project of Anhui Province (no. 1804h08020228 to Z.-W.S.).
Disclosure statement: The authors declare no conflicts
of interest.
Supplementary data
Supplementary data are available at Rheumatology online.
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