Circulating small non-coding RNAs reflect IFN status and B cell hyperactivity in patients with primary Sjögren’s syndrome
Circulating small non-coding RNAs reflect IFN status and B cell hyperactivity in patients with primary SjoÈgren's syndrome
Ana P. Lopes 0 1
Maarten R. Hillen 0 1
Eleni Chouri 0 1
Sofie L. M. Blokland 0 1
Cornelis P. J. Bekker 0 1
Aike A. Kruize 0 1
Marzia Rossato 0
Joel A. G. van Roon 0 1
Timothy R. D. J. Radstake 0 1
0 Editor: Andre van Wijnen, University of Massachusetts Medical School , UNITED STATES
1 Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands, 2 Laboratory of Translational Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands, 3 Functional Genomics Center, University of Verona , Verona , Italy
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
Funding: APL was supported by a PhD grant from
the Portuguese National Funding Agency for
Science, Research and Technology: FundacËão para
a Ciência e a Tecnologia (SFRH/BD/116082/2016).
MRH was supported by the Dutch Arthritis
Association (RF-14-2-301). TR was funded by an
ERC starting grant (ERC-2011-StG, Circumvent).
Ten sncRNAs were differentially expressed between the groups in the array. In the
validation cohort, we confirmed the increased expression of U6-snRNA and miR-661 in
the iSS group as compared to HC. We were unable to validate differential expression of
any miRNAs in the pSS group. However, within this group several miRNAs correlated
with laboratory parameters. Unsupervised clustering distinguished three clusters of pSS
patients. Patients in one cluster showed significantly higher serum IgG, prevalence of
antiSSB autoantibodies, IFN-score, and decreased leukocyte counts compared to the two other
Competing interests: The authors have declared
that no competing interests exist.
We were unable to identify any serum sncRNAs with differential expression in pSS patients.
However, we show that circulating miRNA levels are associated with disease parameters in
pSS patients and can be used to distinguish pSS patients with more severe B cell
hyperactivity. As several of these miRNAs are implicated in the regulation of B cells, they may play a
role in the perpetuation of the disease.
Primary SjoÈgren's syndrome (pSS) is a systemic chronic autoimmune disease characterized by
lymphocytic infiltration of salivary and lacrimal glands, associated with dryness of mouth
(xerostomia) and eyes (keratoconjunctivitis sicca). pSS patients may present with extra-glandular
manifestations such as renal, pulmonary or neurologic involvement and around 5% of the
patients develop lymphoma, primarily of the mucosa-associated lymphoid tissue (MALT) [
]. B cell hyperactivity is one of the hallmarks of pSS, demonstrated by the presence of
hypergammaglobulinemia and autoantibodies against intracellular autoantigens Ro/SjoÈgren's
syndrome associated autoantigen (SS)A and La/SSB, which are expressed by almost all cell types.
The immune complexes formed by autoantibodies lead to innate immune activation and type
I interferon (IFN) production, contributing to the chronicity of the disease. Although the
pathogenesis of pSS is still unknown, a complex interplay of several factors has been implicated
including genetic predisposition, environmental factors, and epigenetic factors [
MicroRNAs (miRNAs) are single-stranded, small non-coding (snc)RNAs of 19±25
nucleotides in length that regulate gene expression at the post-transcriptional level [
studies have demonstrated that miRNAs are expressed in different tissues, cell types, and are
also present in various biological fluids such as saliva, serum and plasma. Circulating miRNAs
can be found in combination with specific carrier proteins or enclosed in different types of
vesicles, including exosomes [
]. miRNAs account for 1±5% of the human genome and can
negatively regulate expression of at least 30% of protein-coding genes at the
post-transcriptional level [
]. A single miRNA can influence many different mRNA targets and conversely,
several different miRNAs can bind to a single mRNA target. This regulation can occur at
different levels, by mediating mRNA cleavage, repressing mRNA translation or causing mRNA
miRNAs are involved in the control of immunologic processes such as cell differentiation,
proliferation, and apoptosis [
]. As such, miRNAs are thought to play a critical role in
autoimmunity and in numerous autoimmune diseases [
]. Recently, several studies in pSS
patients demonstrated the dysregulation of specific miRNAs in salivary glands or PBMCs
from pSS patients [
]. The expression of miR-768-3p and miR-574 in the salivary glands
of patients with pSS is different from those with non-SjoÈgren's syndrome and can distinguish
subsets of pSS patients with low or high grade salivary gland inflammation [
significantly lower expression of miR200b-5p in salivary gland tissue was described in pSS
patients with MALT lymphoma compared to pSS patients without history of lymphoma [
sncRNAs, including miRNAs, are present in serum and circulating miRNA levels are
associated with a range of diseases, including nervous system disorders, metabolic and
autoimmune diseases [
7, 12, 17
]. As serum is easily accessible and collection is relatively easy to
standardize, we investigated whether there are differences in the serum levels of 758 sncRNAs
between pSS patients and incomplete SjoÈgren's Syndrome (iSS) patients or healthy controls
2 / 14
(HC). In addition we assessed if serum sncRNA levels can be used to differentiate patients
with specific disease features.
Materials and methods
Patients and controls
Two independent cohorts of patients followed up in the department of rheumatology &
clinical immunology at the University Medical Center Utrecht and controls were established: a
discovery cohort (14 pSS, 8 iSS, 8 HC) was used to screen the serum abundance of a large panel of
758 sncRNAs, while a validation cohort (23 pSS, 13 iSS, 9 HC) was used to test the
reproducibility of the results (Fig 1). Donors were allocated to each cohort random. The patients with
pSS were classified according to the AECG criteria [
]. The iSS patients presented with
dryness complaints without a known cause, were not clinically considered to have any generalized
Fig 1. Workflow of discovery and validation approach. sncRNAs were considered to be validated in the validation phase
when they reached the threshold of p<0.05 with a difference in the same direction (ie. up/down regulated) as was observed in
the discovery phase. HC: healthy control; iSS: incomplete SjoÈgren's Syndrome; pSS: primary SjoÈgren's syndrome; sncRNA:
small non-coding RNA.
3 / 14
Values are Median [Range] unless stated otherwise. Groups were compared per cohort using Kruskall Wallis test, Fisher's exact test or Mann-Whitney U test where
appropriate. Significant differences (p<0.05) are depicted in bold. HC: Healthy control; iSS: incomplete SjoÈgren's syndrome; pSS: primary SjoÈgren's syndrome; LFS:
Lymphocyte focus score; ESSDAI: EULAR SjoÈgren's syndrome disease activity index; ESSPRI: EULAR SjoÈgren's syndrome patient reported index; ANA: Anti-nuclear
antibodies; SSA: Anti-SSA/Ro; SSB: Anti-SSB/La; RF: Rheumatoid Factor; ESR: Erythrocyte sedimentation rate; CRP: C-reactive protein, HCQ: Hydroxychloroquine.
Other treatment group includes Azathioprine, alone or in combination with Prednisone (n = 5); Mesalazine (n = 1); HCQ in combination with Prednisone (n = 1);
Prednisone (n = 1).
autoimmune disease including pSS, and did not fulfil the classification criteria for pSS. The
study was approved by the ethics committee of the University Medical Center Utrecht. All
patients gave their written informed consent in accordance with the declaration of Helsinki.
The characteristics of the individuals included in the study are depicted in Table 1.
Serum RNA preparation
Fresh blood samples were collected in Vacutainer SSTII Advance tubes (BD Biosciences,
Franklin Lakes, NJ, USA). Serum was collected as per manufacturer's instructions, snap frozen
in liquid nitrogen and stored at -80ÊC until further use. RNA was extracted from 240uL of
serum using the miRcury RNA isolation kit for biofluids (Exiqon, Vedbaek, Denmark). At the
first step of extraction, 300pg of a synthetic miRNA (Arabidopsis thaliana ath-miR-159a) was
added to each sample as a spike-in.
sncRNA profiling array
sncRNA profiling in the discovery cohort was performed on the OpenArray platform (Life
Technologies, Carlsbad, CA, USA). Profiling was performed as previously described [
were analyzed using ExpressionSuite software (Life Technologies), using the relative threshold
cycle (Crt) and the comparative threshold cycle method. Data were normalized using both the
4 / 14
global mean normalization approach [
] and normalization by ath-miR-159a spike-in [
Low expressed sncRNAs (Crt higher than 27) were set at 27; samples with an amplification
score lower than 1.24 were excluded from all analyses. Relative expression was calculated by
dividing the Crt of each sample by that of a random sample in the healthy control group,
which was set at 1. Differences in sncRNA expression between the groups in the discovery
cohort using global mean normalization with a FC difference of 0.5 or 2.0 at an
uncorrected p-value of p<0.05 between any of the groups were selected for validation analysis.
For biological validation, miRNA-specific TaqMan RT-qPCR was performed on the samples
from the validation cohort. In the same experiment, all samples from the discovery cohort
were re-measured for technical replication and to allow the merging of the data for studying
associations with clinical parameters and clustering analysis. To this end, the following
sncRNA assays were ordered from Life Technologies: U6-snRNA (ID 001973),
hsa-miR-23a3p (ID 000399), hsa-miR-223-5p (ID 002098), hsa-miR-661 (ID 001606), hsa-miR-143-3p (ID
002249), hsa-miR-342-3p (ID 002260), hsa-miR-150-5p (ID000473), hsa-miR-140-5p (ID
001187), hsa-miR-29c-3p (ID 000587), hsa-miR-212-3p (ID 000515) and for the exogenous
control ath-miR-159a (ID 000338). From 2.5 uL of serum RNA, cDNA was synthesized by
using the individual miRNA-specific RT primers contained in the TaqMan miRNA assays in
the presence of 3.3 U/uL MultiScribe RT enzyme (Life Technologies), by using the following
thermal cycler conditions: 10 min at 4ÊC, 30 min at 16ÊC, 30 min at 42ÊC, 5 min at 85ÊC.
miRNA levels were quantified in duplicate from 3uL of cDNA using TaqMan fast advance
master mix and miRNA-specific primers from the TaqMan miRNA assays, using these
amplification conditions on the Quantstudio 12k Real-Time PCR system (Life Technologies): 2 min
at 50ÊC, 20 sec at 95ÊC, followed by 40 cycles of 1 sec at 95ÊC, 20 sec at 60ÊC. sncRNA
expression was calculated after normalization by ath-miR-159a spike-in (ΔCt = Ct mean target±Ct
mean miR-159a). The relative fold change (FC) of each sample was calculated in comparison
with the ΔCt mean of the HC group (reference) according to the formula FC = 2-ΔΔCt, where
ΔΔCt = ΔCt sampleÐΔCt reference. Technical replication was considered to be successful if
there was a robust correlation between the Ct in the discovery array (Crt) and the Ct in the
single-assay RT-qPCR (r>0.5 and p<0.05). Validation was considered successful if the direction
of the difference (ie. up/downregulation) was identical to what was observed in the discovery
cohort and the difference was significant at an uncorrected p-value of p<0.05.
For unsupervised hierarchical clustering, Euclidian distance with complete linkage was used
on the FC of sncRNA levels to divide the pSS patients into clusters using the Multi Experiment
Viewer online software (http://mev.tm4.org).
Interferon signature quantification
At the time of blood drawing for serum collection, additional blood was drawn from 13 of the
healthy controls and 25 of the pSS patients (randomly selected) to determine the IFN
signature. To this end, mononuclear cells were isolated from heparinized peripheral blood by
density centrifugation using Ficoll-Paque Plus (GE Healthcare, Uppsala, Sweden). CD14+
monocytes were isolated by magnetic-activated cell sorting using CD14+ isolation kit (Miltenyi
Biotec, Bergisch Gladbach, Germany) according to manufacturer's instructions. To confirm
consistent purity of isolated monocytes, cells were stained with the following monoclonal
antibody combination: anti-CD45 Peridinin chlorophyll (clone: HI30; Sony Biotechnology, San
5 / 14
Jose, California, USA), anti-CD16 Phycoerythrin (clone: DJ130C; Agilent, Santa Clara,
California, USA) and anti-CD14 Fluorescein isothiocyanate (clone: TUÈ K4; Miltenyi Biotec) and the
proportion of CD14+ cells within the isolated fraction was measured using Fluorescence
associated cell sorting (FACS) and a FACSCanto II flow cytometer (BD Bioscience, San Jose,
USA). The purity of the monocyte samples was (median [range]) 98% [90±99%], there were
no significant differences in cell purity between the groups. Cells were lysed in RLTPlus buffer
(Qiagen, Venlo, Netherlands) supplemented with 1% of Beta-mercaptoethanol. Total RNA
was purified using AllPrep DNA/RNA/miRNA Universal Kit (Qiagen) according to the
manufacturer's instructions. RNA concentration was assessed with Qubit RNA Kit (Life
Technologies). To determine the IFN-score, the relative expression of 5 Interferon-induced genes
(IFI44L, IFI44, IFIT3, LY6E and MX1) was assessed as previously described [
] relative to the
expression in the healthy control group, using the Quantstudio system (Life Technologies).
Statistical analyses were performed using GraphPad Prism software version 6.02 (GraphPad,
Lo Jolla, CA, USA) and IBM SPSS version 21 (IBM Corp, Armonk, NY. USA). Mann-Whitney
U-test was used to compare groups in the discovery and validation analyses, without
correction for multiple testing. Kruskal-Wallis H test with post-hoc Dunn's test of multiple
comparisons was used to compare clusters. For correlations, Spearman's rho was used and p-values
were corrected for multiple testing using B&H FDR. Fisher's exact test was used to compare
categorical variables. Two-sided testing was performed for all analyses. Differences and
correlations were considered statistically significant at p<0.05.
Discovery of sncRNAs using OpenArray-based miRNA profiling
OpenArray-based analysis of 758 sncRNAs was performed in the serum of pSS patients, iSS
patients, and healthy controls (HC) from the discovery cohort (n = 30). All differences in sncRNA
abundance between the groups that were significant when using spike-in normalization were also
significant when using global mean normalization (S1 Table). We based our further analysis on
the data from the global mean normalization to be as inclusive as possible and because this
methodology is considered to be the gold standard [
]. When global mean normalization was used,
the levels of three sncRNAs were significantly different in pSS patients and nine sncRNAs were
significantly different in patients with iSS as compared to HC. Two of these sncRNAs (U6-snRNA
and miR-29c-3p) were different in both patient groups compared to HC. There were no
differences in sncRNA abundance between pSS and iSS patients that met the set thresholds (Table 2).
For technical replication, we measured the ten differentially expressed sncRNAs in all of the
donors included in the discovery cohort using single-assay RT-qPCR. Nine out of these ten
sncRNAs (all but miR-212-3p) showed a robust correlation between the relative expression
measured in the OpenArray and in the single-assay RT-qPCR, and were therefore included in
the validation phase (S2 Table).
Validation of the selected sncRNAs in an independent cohort
The nine sncRNAs that were technically replicated were measured in an independent
validation cohort (n = 45) using single-assay RT-qPCR. None of the sncRNAs that were identified as
differentially expressed in the pSS group in the discovery cohort were validated. Of the
sncRNAs that were differentially expressed in the iSS group compared to HC in the discovery
cohort, two were validated: U6-snRNA and miRNA-661 (Table 2).
6 / 14
Results are expressed as mean FC (p-value). Differences between groups that met the threshold for the corresponding analysis (for discovery: FC difference of 0.5 or
2.0 at p-value of p<0.05; for validation: FC difference in same direction as seen in discovery at p<0.05) are indicated in bold. Mann±Whitney U test was used to test
all comparisons. No differences that met the set thresholds were observed between pSS and iSS in the discovery cohort. miR-212-3p was not technically replicated and
therefore was not included in the validation cohort analysis.
Serum sncRNA expression is associated with laboratory disease parameters
in pSS patients
Although the sncRNAs included in the validation phase were not differentially expressed in
pSS patients within the validation cohort, we observed a large spread in expression for all nine
of the sncRNAs within this group (Fig 2). This observation prompted us to investigate whether
sncRNA abundance was related to clinical or laboratory parameters within the pSS patients.
For this, we used the single-assay RT-qPCR data from both cohorts. None of the sncRNAs
showed a significant association with demographic data (sex, age) or clinical features (ESSDAI,
ESSPRI, Schirmer), yet several of them showed correlations with laboratory parameters
including LFS (Table 3). Interestingly, many of the parameters known to be associated with
high disease activity (i.e. low C3/C4, decreased leukocyte count, high lymphocytic focus score)
were negatively correlated with the abundance of the sncRNAs investigated (Table 3). In
addition, pSS patients who are positive for anti-Ro (SSA) and/or anti-La (SSB) showed decreased
expression of several of the sncRNAs when compared to the antibody-negative pSS patients
(S3 Table). Thus, the spread in sncRNA expression in the pSS group is related to their
heterogeneity in disease parameters.
Since each sncRNA was associated with a distinct set of laboratory parameters, we next
investigated whether specific patterns of sncRNA expression could distinguish subsets of patients
with a certain disease phenotype within the pSS group. To this end, unsupervised hierarchical
clustering was used to group the most similar pSS patients on the basis of their expression of
each of the nine sncRNAs measured by single-assay RT-qPCR. This allowed the identification
of three distinct clusters of patients based on different sncRNA patterns (Fig 3A). Clustering
analysis showed that one group of patients (cluster 3) had an overall decreased expression of
all nine sncRNAs in their serum, while two groups (clusters 1 and 2) had higher serum levels
for at least one of the measured miRNAs. Comparison of clinical parameters between clusters
showed that patients in cluster 3 presented with higher serum IgG and IFN-score, as well as
7 / 14
Fig 2. RT-qPCR data of all nine sncRNAs included in the validation phase. Serum sncRNAs were measured using single
Taqman qRT-PCR in the validation cohort (n = 45). ΔCt per sample was calculated using the expression of an exogenous
spiked-in Arabidopsis thaliana miRNA to correct for technical variation. The relative expression of each sample was
calculated as fold change (FC) in comparison with the ΔCt mean of the HC group in the respective cohort. Medians ± IQR are
decreased leukocyte counts compared to cluster 1. In addition, an increased frequency of
patients in this cluster was positive for anti-La (SSB) (Fig 3B).
In the present study, we investigated whether serum sncRNAs can be used to distinguish pSS
patients from iSS patients and HC. Using two independent cohorts with patients and controls,
we were unable to identify any sncRNAs that reproducibly differ between pSS patients and
patients with iSS or HC. However, we did show that circulating sncRNA levels reflect disease
parameters in pSS patients and can be used to distinguish pSS patients with higher markers of
B cell hyperactivity.
We chose to measure circulating miRNAs in serum, as it is easily accessible and its
preparation is well standardized. Measurements of miRNAs in plasma may yield different results, as a
recent study showed that around 6% of studied miRNAs show differences in expression
between the 2 fluids [
]. Future studies need to be conducted to investigate whether more
clear differences in miRNA levels can be found in plasma from pSS patients. However, levels
8 / 14
Spearman's correlation coefficients (ρ) and B&H FDR-corrected p-values are shown. sIgG: serum immunoglobulin G; LFS: lymphocytic focus score. Correlations that
are significant at p<0.05 are depicted in bold.
of eight of the nine sncRNAs included in our validation phase (all but miR-661) were
previously compared between serum and plasma and none of them exhibited significant differences
]. As such, these miRNAs should be similarly expressed in plasma measurements.
Our discovery-validation approach allowed us to measure a large number of sncRNAs in
the discovery cohort and follow nine of these up in the validation cohort. Technical replication
showed that the differences found in the discovery cohort were not artifacts of the array. In
addition, we compared two methods of data normalization for the discovery cohort to ensure
optimal data analysis. We chose to use global mean normalization as it was the most inclusive
and appropriately corrects for limitations intrinsic to the qPCR methodology [
]. As such,
any serum sncRNAs that were included in the array and are robustly dysregulated in pSS
patients compared to iSS or HC should have been identified here. Using this approach, we
validated that two sncRNAs, U6-snRNA and miR-661, are increased in iSS patients compared to
HC. To our knowledge, these sncRNAs have not previously been described in any
autoimmune disease and future studies on the function of these sncRNAs should clarify what their
role is in the disease. Although the iSS patients who were studied presented with mild local
and systemic parameters of inflammation, these features may explain the increased sncRNA
levels. In-line with this hypothesis, data showed an association between increased circulating
U6-sncRNA and markers of inflammation in a range of inflammatory conditions .
However, the lack of differences between pSS patients and iSS patients or HC in sncRNAs
can be largely attributed to the heterogeneity of this group. The expression of the nine
sncRNAs measured in the validation phase was strongly overlapping between pSS patients, iSS
patients and HC. As our analyses show an association between serum sncRNA levels and
several biological disease parameters, the large variation in the pSS group seems to be related to
differences in markers of inflammation. In particular, autoantibody presence was an important
9 / 14
Fig 3. pSS patients with increased B cell hyperactivity can be identified using hierarchical clustering of serum
sncRNA expression levels. Nine sncRNAs were selected based on their differential expression in the discovery array and
subsequent technical replication. These sncRNAs were measured using single-assay RT-qPCR in samples from both
cohorts (n = 75). Unsupervised hierarchical clustering was performed on the expression of the nine selected sncRNAs in
all 37 pSS patients. Grey fields depict unavailable data points (A). Clinical parameters and frequency of positivity for
antiLa (SSB) autoantibodies were compared between the three clusters (B). The patients in each cluster were compared using
Kruskal-Wallis H test with post-hoc Dunn's test of multiple comparisons and Fisher's exact test. For dot plots,
medians ± IQR are shown.
PLOS ONE | https://doi.org/10.1371/journal.pone.0193157
10 / 14
parameter in this regard, as a range of sncRNAs showed significant differences between
autoantibody positive and negative pSS patients. As such, the increased prevalence of SSA and SSB
positivity in the validation cohort compared to the discovery cohort, although not statistically
significant, may have contributed to the lack of validated targets. However, both cohorts
presented with an autoantibody presence that is within acceptable range to those reported in very
large cohorts of pSS patients [
Within the nine sncRNAs measured in the validation phase, we observed an overall trend
of increased expression in the pSS patients from clusters 1 and 2 compared to the patients in
cluster 3. The pSS patients in cluster 3 showed an overall decrease in serum levels of the
measured sncRNAs and presented with more pronounced autoimmune activity, including
increased B cell hyperactivity, as measured by sIgG and autoantibody positivity, and a higher
IFN-score. The IFN-score was previously shown to correlate with the disease activity and
autoantibody presence in pSS patients [
], which is in-line with the increased serum IgG and
SSBpositivity we observed in the patients in this cluster. The association of lower sncRNA levels
with higher parameters of B cell hyperactivity may be explained by a change in the
composition of circulating B cell pool. In line with this hypothesis, three of miRNAs analyzed in the
validation phase (miR-150-5p, miR-223-5p, and miR-342-3p) are highly expressed by naïve B
cells while their expression is down-regulated upon B cell activation. In addition, these
miRNAs were implicated in the regulation of B cell differentiation [
]. As such, this set of
miRNAs may be involved in the increased B cell hyperactivity observed in the patients of cluster 3.
Alternatively, the lower sncRNA levels observed in cluster 3 may be a reflection of changes in
the composition of circulating cells, as these patients also have a decreased leukocyte count.
Possibly, the decreased levels of sncRNAs can be explained by migration of the leukocytes
responsible for the production of these sncRNAs to sites of inflammation. This is supported by
the correlation of leukocyte counts with the expression of the majority of the sncRNAs
measured in the validation phase in the pSS group as a whole. In line with this, levels of circulating
miRNAs that are expressed by leukocyte subsets correlate with the presence of these cells in
the blood [
In conclusion, we validated increased expression of two serum sncRNAs in iSS patients
compared with HC, U6-snRNA and miR-661, but did not find any differences in serum sncRNA
levels between pSS patients and iSS patients or HC. Furthermore, we show that the
heterogeneity in sncRNA expression within the pSS patients is associated with differences in clinical and
laboratory parameters. Moreover, pSS patients with a higher IFN score and signs of increased
B cell hyperactivity can be distinguished on the basis of overall lower expression levels of the
sncRNAs studied. These lower serum sncRNA levels may be related to migration of the
miRNA-producing cells from the circulation towards the site of inflammation. In addition, as
several of these miRNAs are implicated in B cell activation and differentiation, they may play a
role in the more pronounced B cell hyperactivity observed in these patients.
S1 Table. Comparison between global mean and spike-in normalization in the discovery
cohort. Results are expressed as mean FC. Differences between groups that met the threshold
for the corresponding analysis (FC difference of 0.5 or 2.0 at p-value of p<0.05) are
indicated in bold. Mann±Whitney U test was used to test all comparisons.
11 / 14
S2 Table. Correlation between array and single RT-qPCR results in the discovery cohort.
Correlation between Crt in profiling array and CT measured with single-assay Taqman
RTqPCR in patients and controls from the discovery cohort (n = 30). Spearman's correlation
coefficients (ρ) and p-values are shown. Correlations that are significant at p<0.05 are depicted
S3 Table. Differences between SSA/SSB positive and negative pSS patients in circulating
sncRNA abundance. sncRNAs were measured using RT-qPCR in all pSS patients from the
discovery and validation cohort (n = 37). Fold changes (FC) were calculated as compared to
the mean of the healthy control group in the corresponding cohort. Results are expressed in
FC as median [range]. Statistically significant differences (Mann±Whitney U test) between
autoantibody positive and negative pSS patients are indicated in bold. SSA: anti-Ro/SjoÈgren's
syndrome antigen A; SSB: anti-La/SjoÈgren's syndrome antigen B. and
differences at p<0.05 and p<0.01 respectively.
The authors would like to thank Drs. Emmerik Leijten, Drs. Lucas van der Hoogen, and Dr.
Jonas Kuiper for interesting discussions and critical reading of the manuscript. In addition, we
are grateful to Dr. Sarita Hartgring and Kim van der Wurff-Jacobs BSc. for their assistance
with sample collection.
P. J. Bekker.
Conceptualization: Ana P. Lopes, Maarten R. Hillen, Eleni Chouri, Aike A. Kruize, Joel A. G.
van Roon, Timothy R. D. J. Radstake.
Formal analysis: Ana P. Lopes, Maarten R. Hillen, Marzia Rossato, Joel A. G. van Roon.
Investigation: Ana P. Lopes, Maarten R. Hillen, Eleni Chouri, Sofie L. M. Blokland, Cornelis
Writing ± original draft: Ana P. Lopes, Maarten R. Hillen.
Writing ± review & editing: Sofie L. M. Blokland, Cornelis P. J. Bekker, Aike A. Kruize,
Marzia Rossato, Joel A. G. van Roon, Timothy R. D. J. Radstake.
12 / 14
13 / 14
1. Nocturne G , Mariette X. Advances in understanding the pathogenesis of primary Sjogren's syndrome . Nat Rev Rheumatol . 2013 ; 9 : 544 ± 56 . https://doi.org/10.1038/nrrheum. 2013 .110 PMID: 23857130
2. Dong L , Chen Y , Masaki Y , Okazaki T , Umehara H . Possible Mechanisms of Lymphoma Development in Sjogren's Syndrome . Curr Immunol Rev . 2013 ; 9 : 13 ± 22 . https://doi.org/10.2174/ 1573395511309010003 PMID: 23853604
3. Holdgate N , St Clair EW . Recent advances in primary Sjogren's syndrome . F1000Res . 2016 ; 5 .
4. Konsta OD , Thabet Y , Le Dantec C , Brooks WH , Tzioufas AG , Pers JO , et al. The contribution of epigenetics in Sjogren's Syndrome . Front Genet . 2014 ; 5 : 71 . https://doi.org/10.3389/fgene. 2014 .00071 PMID: 24765104
5. Sayed D , Abdellatif M. MicroRNAs in development and disease . Physiol Rev . 2011 ; 91 : 827 ± 87 . https:// doi.org/10.1152/physrev.00006. 2010 PMID: 21742789
6. Gilad S , Meiri E , Yogev Y , Benjamin S , Lebanony D , Yerushalmi N , et al. Serum microRNAs are promising novel biomarkers . PLoS One . 2008 ; 3:e3148 . https://doi.org/10.1371/journal.pone. 0003148 PMID: 18773077
Wang J , Chen J , Sen S. MicroRNA as Biomarkers and Diagnostics . J Cell Physiol . 2016 ; 231 : 25 ± 30 . https://doi.org/10.1002/jcp.25056 PMID: 26031493
8. Macfarlane LA , Murphy PR . MicroRNA: Biogenesis, Function and Role in Cancer . Curr Genomics . 2010 ; 11 : 537 ± 61 . https://doi.org/10.2174/138920210793175895 PMID: 21532838
9. Kim VN . MicroRNA biogenesis: coordinated cropping and dicing . Nat Rev Mol Cell Biol . 2005 ; 6 : 376 ± 85 . https://doi.org/10.1038/nrm1644 PMID: 15852042
10. Hu R , O'Connell RM. MicroRNA control in the development of systemic autoimmunity . Arthritis Res Ther . 2013 ; 15 : 202 . https://doi.org/10.1186/ar4131 PMID: 23379780
11. Bartel DP . MicroRNAs: target recognition and regulatory functions . Cell . 2009 ; 136 : 215 ± 33 . https://doi. org/10.1016/j.cell. 2009 . 01 .002 PMID: 19167326
12. Qu Z , Li W , Fu B. MicroRNAs in autoimmune diseases . Biomed Res Int . 2014 ; 2014 :527895. https:// doi.org/10.1155/ 2014 /527895 PMID: 24991561
13. Chen JQ , Papp G , Szodoray P , Zeher M. The role of microRNAs in the pathogenesis of autoimmune diseases . Autoimmun Rev . 2016 ; 15 : 1171 ± 80 . https://doi.org/10.1016/j.autrev. 2016 . 09 .003 PMID: 27639156
Williams AE , Choi K , Chan AL , Lee YJ , Reeves WH , Bubb MR , et al. Sjogren's syndrome-associated microRNAs in CD14(+) monocytes unveils targeted TGFbeta signaling . Arthritis Res Ther . 2016 ; 18 : 95 . https://doi.org/10.1186/s13075-016 -0987-0 PMID: 27142093
15. Alevizos I , Alexander S , Turner RJ , Illei GG . MicroRNA expression profiles as biomarkers of minor salivary gland inflammation and dysfunction in Sjogren's syndrome . Arthritis Rheum . 2011 ; 63 : 535 ± 44 . https://doi.org/10.1002/art.30131 PMID: 21280008
16. Gourzi VC , Kapsogeorgou EK , Kyriakidis NC , Tzioufas AG . Study of microRNAs (miRNAs) that are predicted to target the autoantigens Ro/SSA and La/SSB in primary Sjogren's Syndrome . Clin Exp Immunol . 2015 ; 182 : 14 ± 22 . https://doi.org/10.1111/cei.12664 PMID: 26201309
17. Cortez MA , Bueso-Ramos C , Ferdin J , Lopez-Berestein G , Sood AK , Calin GA. MicroRNAs in body fluidsÐthe mix of hormones and biomarkers . Nat Rev Clin Oncol . 2011 ; 8 : 467 ± 77 . https://doi.org/10. 1038/nrclinonc. 2011 .76 PMID: 21647195
18. Vitali C , Bombardieri S , Jonsson R , Moutsopoulos HM , Alexander EL , Carsons SE , et al. Classification criteria for Sjogren's syndrome: a revised version of the European criteria proposed by the AmericanEuropean Consensus Group . Ann Rheum Dis. 2002 ; 61 : 554 ±8. https://doi.org/10.1136/ard.61.6.554 PMID: 12006334
19. Cuppen BV , Rossato M , Fritsch-Stork RD , Concepcion AN , Schenk Y , Bijlsma JW , et al. Can baseline serum microRNAs predict response to TNF-alpha inhibitors in rheumatoid arthritis? Arthritis Res Ther . 2016 ; 18 : 189 . https://doi.org/10.1186/s13075-016-1085-z PMID: 27558398
20. Mestdagh P , Van Vlierberghe P , De Weer A , Muth D , Westermann F , Speleman F , et al. A novel and universal method for microRNA RT-qPCR data normalization . Genome Biol . 2009 ; 10 :R64. https://doi. org/10.1186/gb-2009 -10-6-r64 PMID: 19531210
21. Marabita F , de Candia P , Torri A , Tegner J , Abrignani S , Rossi RL . Normalization of circulating microRNA expression data obtained by quantitative real-time RT-PCR . Brief Bioinform . 2016 ; 17 : 204 ± 12 . https://doi.org/10.1093/bib/bbv056 PMID: 26238539
22. Brkic Z , Maria NI , van Helden-Meeuwsen CG , van de Merwe JP, van Daele PL , Dalm VA , et al. Prevalence of interferon type I signature in CD14 monocytes of patients with Sjogren's syndrome and association with disease activity and BAFF gene expression . Ann Rheum Dis . 2013 ; 72 : 728 ± 35 . https://doi. org/10.1136/annrheumdis-2012 -201381 PMID: 22736090
23. Schwarzenbach H , da Silva AM , Calin G , Pantel K . Data Normalization Strategies for MicroRNA Quantification . Clin Chem . 2015 ; 61 : 1333 ± 42 . https://doi.org/10.1373/clinchem. 2015 .239459 PMID: 26408530
24. Bockmeyer CL , Sauberlich K , Wittig J , Esser M , Roeder SS , Vester U , et al. Comparison of different normalization strategies for the analysis of glomerular microRNAs in IgA nephropathy . Sci Rep . 2016 ; 6 : 31992 . https://doi.org/10.1038/srep31992 PMID: 27553688
25. Cheng HH , Yi HS , Kim Y , Kroh EM , Chien JW , Eaton KD , et al. Plasma processing conditions substantially influence circulating microRNA biomarker levels . PLoS One . 2013 ; 8:e64795 . https://doi.org/10. 1371/journal.pone. 0064795 PMID: 23762257
26. Benz F , Roderburg C , Vargas Cardenas D , Vucur M , Gautheron J , Koch A , et al. U6 is unsuitable for normalization of serum miRNA levels in patients with sepsis or liver fibrosis . Exp Mol Med . 2013 ; 45 : e42. https://doi.org/10.1038/emm. 2013 .81 PMID: 24052167
27. Brito-Zeron P , Acar-Denizli N , Zeher M , Rasmussen A , Seror R , Theander E , et al. Influence of geolocation and ethnicity on the phenotypic expression of primary Sjogren's syndrome at diagnosis in 8310 patients: a cross-sectional study from the Big Data Sjogren Project Consortium . Ann Rheum Dis. 2016 .
28. Iqbal J , Shen Y , Liu Y , Fu K , Jaffe ES , Liu C , et al. Genome-wide miRNA profiling of mantle cell lymphoma reveals a distinct subgroup with poor prognosis . Blood . 2012 ; 119 : 4939 ± 48 . https://doi.org/10. 1182/blood-2011 -07-370122 PMID: 22490335
29. Pritchard CC , Kroh E , Wood B , Arroyo JD , Dougherty KJ , Miyaji MM , et al. Blood cell origin of circulating microRNAs: a cautionary note for cancer biomarker studies . Cancer Prev Res (Phila) . 2012 ; 5 : 492 ± 7 .