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Comprehensive expression profile of long non-coding RNAs

in Peripheral blood mononuclear cells from patients with

neuropsychiatric systemic lupus erythematosus

Linyu Geng1#, Xue Xu1#, Huayong Zhang1#, Chen Chen1, Yayi Hou2, Genhong Yao1, Shiying Wang1,

Dandan Wang1, Xuebing Feng1, Lingyun Sun1, Jun Liang1

1Department of Rheumatology and Immunology, the Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008,

China; 2Institute of Brain Sciences, Medical School, Nanjing University, Nanjing 210093, China

Contributions: (I) Conception and design: L Sun, J Liang; (II) Administrative support: None; (III) Provision of study materials or patients: H Zhang, C Chen, S Wang, D Wang; (IV) Collection and assembly of data: L Geng, X Xu; (V) Data analysis and interpretation: L Geng, X Xu; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

#These three authors contributed equally to this work.

Correspondence to: Jun Liang; Lingyun Sun. Department of Rheumatology and Immunology, the Affiliated Drum Tower Hospital of Nanjing University Medical School, 321 Zhongshan Road, Nanjing 210008, China. Email: 13505193169@139.com; lingyunsun@nju.edu.cn.

Background: Long noncoding RNAs (lncRNAs) are emerging as critical regulators of gene expression in

the immune system, but their impact on neuropsychiatric systemic lupus erythematosus (NPSLE) remains unknown.

Methods: RNA sequencing analysis was used to screen the comprehensive expression profile of lncRNAs

and messenger RNAs (mRNAs) in peripheral blood mononuclear cells (PBMCs) from NPSLE patients, active SLE patients who had never experienced neuropsychiatric manifestations (Non-NPSLE) and healthy controls. Differentially expressed (DE) lncRNA levels were validated by qRT-PCR in 26 NPSLE patients, 31 Non-NPSLE patients and 30 healthy controls. Further, correlations of DE lncRNAs with clinical manifestations of NPSLE patients were analyzed. Finally, a bioinformatic analysis was performed to investigate the potential functions of DE genes.

Results: Four hundred and fifty-one lncRNAs and 272 mRNAs were DE between the NPSLE

patients and Non-NPSLE patients, among which, significantly upregulated expression levels of NONHSAT208182.1, NONHSAT182114.1, NONHSAT106801.2, NONHSAT039491.2, ENST00000356215, NONHSAT087499.2 and NONHSAT207026.1 while downregulated expression levels of NONHSAT001281.2 and NONHSAT024353.2 were further validated in PBMCs from NPSLE patients by qRT-PCR. Bioinformatic analysis suggested several gene ontology (GO) terms and signal pathways may play important roles in NPSLE development. Co-expression networks analysis indicated that 170 lncRNAs and 46 mRNAs were included in the co-expression network. The expression level of NONHSAT039491.2 was associated with the activity of SLE and the presence of anti-dsDNA, anti-RNP antibody, dizziness and headache. NONHSAT087499.2 level correlated with anti-RNA antibody, ENST00000356215 level correlated with olfactory threshold and oral ulcer. NONHSAT208182.1 level correlated with the presence of fever, unstable walking and urinary red blood cells. NONHSAT106801.2 correlated with frequency of B cells and the presence of fever. NONHSAT024353.2 level was associated with serum IgG levels and the presence of anti-SSA and disorder of consciousness.

Conclusions: Our data provided comprehensive evidence regarding the differential expression of lncRNAs

in PBMCs from NPSLE patients, indicating that these DE lncRNAs may play roles in NPSLE development. Our finding shed light on the understanding of the molecular mechanisms of lncRNAs in the pathogenesis of NPSLE.

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Introduction

Systemic lupus erythematosus (SLE) is a prototypic systemic autoimmune disease characterized by autoantibody production and immune complex deposition, resulting in multiple organ damages including kidneys, heart, lungs, and nervous system (1). Neuropsychiatric SLE (NPSLE) is one of the most severe manifestations of SLE and encompasses a variety of neurological and psychiatric disorders, leading to substantial morbidity and mortality (2). Complex interrelated pathogenic mechanisms have been implicated in NPSLE pathogenesis, including genetic factors, vasculopathy, neuroendocrine-immune imbalance, neuronal damage mediated by autoantibodies, inflammatory mediators and blood brain barrier dysfunction (2). Recent studies found that epigenetic alterations, such as altered DNA methylation, histone modifications, and one class of non-coding RNAs (ncRNAs) named microRNAs mediated posttranscriptional gene silencing may affect the initiation and progression of NPSLE (3). However, the potential role of other types of ncRNAs such as long non-coding RNAs (lncRNAs) in etiology and pathogenesis of NPSLE still largely unknown.

LncRNAs are generally defined as non-protein-coding transcripts larger than 200 nucleotides, located within the intergenic stretches or overlapping antisense transcripts of protein coding genes (4). Although without protein coding capability, accumulating evidence suggested that lncRNAs participate in diverse biological processes, including genomic imprinting, chromosome modification, transcriptional activation, protein stability, and miRNAs inhibition. LncRNAs were reported to be involved in pathogenesis of multiple diseases such as neurodegenerative diseases, diabetic mellitus and cancer (5-7). Recently, the roles of lncRNAs in immune responses have aroused increasing attention (8,9). LncRNAs have been found to be differentially expressed (DE) in peripheral blood mononuclear cells (PBMCs), immortalized B cells, and kidney biopsy specimens from SLE patients (10-13), and play crucial roles in autoimmune diseases including SLE (14,15). However, the precise role of lncRNAs in NPSLE pathogenesis remains unclear.

In the present study, we performed high-throughput RNA sequencing assays on PBMCs from NPSLE patients. Outstanding lncRNAs functions were annotated based on co-expression genes and a gene ontology (GO) biological analysis process. The correlations of lncRNAs with disease activity indicators, system and visceral involvement indicators including the nervous system of NPSLE patients were revealed through correlation analyses.

Methods

Study subjects

Totally 57 active SLE patients and 30 age-matched healthy subjects with no history of autoimmune diseases were

included. All patients fulfilled the 1997 American College

of Rheumatology (ACR) revised criteria for SLE (16). SLE disease activity was recorded by Systemic Lupus Erythematosus Disease Activity Index 2000 (SLEDAI) score at the time of blood draw. Patients with SLEDAI ≥8 were categorized as active SLE patients. Characteristically, active SLE patients can be classified into two groups, NPSLE group are 26 patients with acute symptoms of NPSLE, and Non-NPSLE group are 31 SLE patients who never experienced neuropsychiatric manifestations (17). NPSLE was diagnosed if the patent had at least one of 19 neuropsychiatric syndromes according to ACR classification and no other explanation related with SLE. Demographic and clinical characteristics of these patient samples are shown in the Table S1. All subjects were given informed consents for the collection of peripheral blood. This study was approved by the Ethics Committee of the Affiliated Drum Tower Hospital of Nanjing University Medical School (ID: SC201700201) and was conducted in accordance with the principle set forth under the 1989 Declaration of Helsinki.

Cell isolation and RNA extraction

10 ml EDTA-anticoagulated blood were collected from each subject. PBMCs were prepared by Ficoll-Hypaque

Keywords: Long non-coding RNA; neuropsychiatric systemic lupus erythematosus (NPSLE); gene ontology

terms; kyoto encyclopedia of genes and genomes; co-expression networks

Submitted Nov 19, 2020. Accepted for publication Jan 04, 2020. doi: 10.21037/atm.2020.03.25

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density gradient centrifugation. Total RNA was extracted from PBMCs by using Trizol Reagent (Invitrogen, Carlsbad, USA) according to the manufacture’s protocol. The purity and concentration of RNA was evaluated with the NanoDrop ND-1000 (Thermo Fisher Scientific, Wilmington, DE).

High-throughput RNA sequencing and analysis

Briefly, total RNA was extracted using RNeasy mini kit (Qiagen, Germany) according to the manufacturer’s instructions, quantified using NanoDrop ND-2000, and checked for RNA integrity by an Agilent Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA, USA). RNA libraries were generated using TruSeq Stranded RNA Sample Prep Kit (Illumina) following manufacturer’s protocol, quantified with Qubit 2.0 Fluorometer (Life Technologies, USA), validated by Agilent 2100 bioanalyzer (Agilent Technologies, USA) to confirm the insert size and calculate the mole concentration, and subjected to sequencing using Illumina HiSeq 2500 (Illumina, USA). Acquired data were processed to obtain raw reads, which were then extracted for genome mapping with TopHat

(version 2.0.9). After genome mapping, Cufflinks v2.1.1 was

run with a reference annotation to generate FPKM values for known gene models. DE genes were identified using Cuffdiff. The P value significance threshold in multiple tests was set by the false discovery rate (FDR). The fold changes (FC) were also estimated according to the FPKM in each sample. The DE genes were selected using the

following filter criteria: FDR ≤0.01 and FC ≥2. Genes with

differential expression levels were identified using edgeR, with GO enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The library construction and sequencing were performed at Shanghai Biotechnology Corporation.

Screening of DE lncRNAs for validation

We screened DE lncRNAs for further validation using the following approaches to validate the results of sequencing experiments using the same cohort together with another independent cohort and investigate the correlations between gene expression levels and clinical characteristics.

First, lncRNAs were evaluated based on the data revealed by sequencing experiments using the following criteria: (I) the FC of DE lncRNAs (P<0.01) in NPSLE group must be greater than two-fold compared to Non-NPSLE group; (II)

lncRNA levels may have no significant difference between

Non-NPSLE group and HC group; or if the difference is

significant, the change tendencies of abnormally expressed

lncRNAs between these two group should be same as that between NPSLE-Non-NPSLE-HC group; (III) the contexts of lncRNA-mRNA network are associated with immune system; and (IV) genes with lncRNA-mRNA repeated sequences and without information in databases were excluded.

Second, the degree of DE genes between NPSLE patients and active SLE patients was compared based on the co-expression network results. The top 10 genes with high

different degrees and the qualified requirements for signal

values were selected.

Quantitative real-time PCR (qRT-PCR) validation Extracted total RNA of 87 participants was reverse-transcribed to complementary DNA (cDNA) with random primer using a RevertAid First Strand cDNA Synthesis Kit (Thermo Scientific). The qRT-PCR reaction was performed in duplicate with a total volume of 10 μL, including 5 μL Maxima SYBR Green/ROX qPCR master mix (Thermo Scientific), 0.3 μL PCR Forward Primer (10 μM), 0.3 μL PCR Reverse Primer (10 μM), 2 μL cDNA, and 2.4 μL double-distilled water. ViiA 7 Real-Time PCR System (Applied Biosystems) was used and the samples were subjected to the following thermal cycling: 95 ℃ for 10 min; 40 cycles of 95 ℃ for 15 s and annealing/extension for 1 min at 60 ℃, followed by a melting curve from 65 ℃ to 95 ℃ in 0.5 ℃ increments. The qRT-PCR primer sequences were displayed in Table S2. Beta-actin was used as internal control and the relative gene expression was calculated using 2−ΔCt comparative threshold cycle method.

Bioinformatic analysis

Pathway analysis and GO analysis were applied to explore the potential roles that the DE mRNAs played in biological pathways or GO terms, including the following three categories: biological process, cellular component, and molecular function. Fisher’s exact test was used to find if there was more overlap between the DE list and the GO annotation list than would be expected by chance. The P

value denoted the significance of GO terms enrichment in

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of KEGG pathway were similar to the GO analysis. A co-expression analysis was performed by associating the

expression profiles of DE-lncRNAs with DE-mRNAs. This

association was based on the information of the lncRNA-associated coding regions, which was supplied by the sequencing analysis.

Construction of coding-non-coding gene (CNC) coexpression network

Taking the role of lncRNAs in modulating transcription into account, we analyzed the relationship between DE lncRNAs and mRNAs in hope of uncovering possible mRNA targets for differential lncRNAs. Given the normalized signal intensity (>6), FC (≥2), FDR (<0.01), and the relevance of mRNA in SLE, 36 DE lncRNAs and 151 DE mRNAs [these mRNAs were broadly selected for several reasons: (I) their abnormal expressions in SLE are validated in other studies; (II) their corresponding genes are susceptible genes in SLE; (III) their novel functions in immune response are established by others.] were selected for construction of CNC gene coexpression network. The network construction was based on the correlation analysis between these DE lncRNAs and mRNAs. Pearson correlation coefficients over 0.90 were selected for the construction with Cytoscape software (The Cytoscape Consortium, San Diego, USA) and shown in Supplementary material.

Olfactory function assessment by computerized testing

The odor threshold test measures a person’s ability to detect an odor. Thirteen concentrations of an industry standard odorant are used. The participant smells two samples, one blank and one with an odorant. They are then asked to determine which of the two samples is the stronger. The concentrations are decreased or increased depending on

whether the stimulus is identified correctly.

The odor memory test assesses a person’s ability to remember odors that were previously presented. The test begins with the presentation of 10 odors followed by a 10-minute break. Next, 20 odors containing the 10 original and 10 new odors are presented. After each odor presentation, the participant is asked to identify the odor and to determine if the odor is one of the original odors or a new odor. This provides measures of both semantic and episodic odor memory.

All the tests were done according to the manual of

OLFACTTM as described in the website: http://www. osmicenterprises.com/index.html.

Statistical analysis

All data represent mean ± SEM. Differences between two groups were determined by unpaired Student’s t-test

if the variance was normally distributed. Comparisons among three or more groups were conducted using one-way ANOVA. Randomization and blinding strategy were used whenever possible. Data were analyzed and visualized with GraphPad Prism 7.0 (GraphPad Software, La Jolla, CA, USA) and a two-tailed P value <0.05 was considered

statistically significant.

Results

LncRNAs and mRNAs expression profiles in NPSLE patients

To determine whether the expression levels of lncRNAs were associated with SLE pathogenesis, we firstly performed whole-transcriptome sequencing (RNA-Seq) on the peripheral blood mononuclear cells (PBMCs) of 11 SLE patients and 6 healthy controls. A total of 1,515 lncRNAs and 2,098 mRNAs were DE between these two groups using scatter plot and hierarchical clustering analyses (Figure 1, http://cdn.amegroups. cn/static/other/b191243af9a0d8a7f8fa5ee14a9a4620/ atm.2020.03.25-1.xls, http://cdn.amegroups.cn/ static/other/2284aef2c7af028a615bd36c215970a9/ atm.2020.03.25-2.xlsx; FC ≥2, P<0.01). To reveal DE

lncRNA profiles in NPSLE patients, these 11 SLE patients

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HC6

SLE8

SLE vs. HC

SLE vs. HC

Up regulation

−10 −5 5 10

−10 −5 5 10

Down regulation Not significant

FC=−2.0 FC=2.0

FC=−2.0 FC=2.0 Log2 (Fold-change) p=0.01

Lg2 (p-value)

HC5

SLE6

HC3

SLE7

HC4

SLE2

HC2

SLE9

HC1

SLE5

SLE8

SLE10

SLE6

SLE3

SLE7

SLE1

SLE2

SLE11

SLE5

SLE4

SLE9

HC5

SLE10

HC3

SLE11

HC6

SLE4

HC4

SLE3

HC2

SLE1

HC1

3 2 1 0

−1

−2

−3

3 2 1 0

−1

−2

−3

20

15

10

5

20

15

10

5 p=0.01

Lg2 (p-value)

A

C

B

[image:5.595.47.537.83.435.2]

D

Figure 1 Expression profiles of lncRNAs and mRNAs in SLE patients and HCs. (A,C) Heat map showing hierarchical clustering of lncRNAs and mRNAs with expression changes greater than two-fold and P value <0.01. SLE patient group: SLE1-11; healthy control group: HC1-6. Red and green colors represent up- and downregulated genes, respectively; (B,D) Volcano plot of differentially expressed lncRNAs and mRNAs. The horizontal white line represents a P value of 0.01, and vertical white lines represent 2.0-fold changes up and down. X axes are the fold change values (log2 scaled), and Y axes are the P values (log2 scaled). Red and blue plots represent up- and

downregulated genes, respectively.

other/03bf6ea47a10ad0b983eaadb34f49b0b/atm.2020.03.25-6.xls) both in Figure 1 (http://cdn.amegroups.cn/ static/other/b191243af9a0d8a7f8fa5ee14a9a4620/ atm.2020.03.25-1.xls, http://cdn.amegroups.cn/ static/other/2284aef2c7af028a615bd36c215970a9/ atm.2020.03.25-2.xlsx) and Figure 2 (http://cdn.amegroups. cn/static/other/ea6fab857009712577755c3a449f940a/ atm.2020.03.25-3.xlsx, http://cdn.amegroups.cn/static/other/ ae9e70ffaa1ad61a20e131857d1e8749/atm.2020.03.25-4.xlsx), suggesting that these lncRNAs and mRNAs might play roles in NPSLE pathogenesis.

The top 20 DE lncRNAs/mRNAs between NPSLE

and Non-NPSLE groups are listed in Table 1 and Table 2,

respectively. The RNA-seq data have been submitted to NCBI Gene Expression Omnibus and are accessible with the GEO Series accession number GSE90524 (https://www. ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE90524).

Verification of lncRNA expression profiles and their correlations with clinical characteristics

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NPSLE4

NPSLE3

NPSLE6

NPSLE1

NPSLE4

NPSLE5

NPSLE2

Non-NPSLE2

Non-NPSLE5

Non-NPSLE4

Non-NPSLE3

Non-NPSLE1

NPSLE1

NPSLE5

NPSLE2

NPSLE6

NPSLE3

Non-NPSLE5

Non-NPSLE2

Non-NPSLE3

Non-NPSLE4

Non-NPSLE1

NPSLE VS No-NPSLE

NPSLE VS No-NPSLE

Up regulation

−10 −5 5 10

−10 −5 5 10

Down regulation Not significant

FC=−2.0 FC=2.0 3

2 1 0

−1

−2

−3

3 2 1 0

−1

−2

−3 20

15

10 p=0.01

Lg2 (p-value)

p=0.01

Lg2 (p-value)

FC=−2.0 FC=2.0 Log2 (Fold-change)

A

C

B

[image:6.595.42.544.83.597.2]

D

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[image:7.595.51.549.95.444.2]

Table 1 Top 20 DE lncRNAs between NPSLE and Non-NPSLE patients.

LncRNA_ID FPKM (NPSLE) FPKM (Non-NPSLE) P value Up/Down

ENST00000414046 24.265 1.364 1.16E-07 Up

ENST00000619625 2.566 0.002 2.31E-08 Up

NONHSAT002346.2 15.147 0.000 1.09E-07 Up

NONHSAT094223.2 0.031 4.643 7.89E-08 Down

NONHSAT169171.1 0.268 5.482 6.46E-08 Down

ENST00000602495 0.197 5.993 3.70E-07 Down

NONHSAT218538.1 12.362 0.060 1.20E-06 Up

NONHSAT076583.2 0.481 0.000 2.32E-06 Up

NONHSAT118801.2 0.000 0.977 2.77E-06 Down

NONHSAT106801.2 2.179 0.000 4.75E-06 Up

NONHSAT147938.2 5.265 0.000 6.07E-06 Up

NONHSAT179347.1 0.000 13.139 6.94E-06 Down

NONHSAT167098.1 0.962 0.000 8.61E-06 Up

NONHSAT001188.2 1.186 0.000 1.08E-05 Up

NONHSAT083781.2 0.271 27.809 1.87E-05 Down

ENST00000499762 0.000 0.548 2.17E-05 Down

NONHSAT068072.2 0.239 5.485 2.06E-05 Down

NONHSAT147817.2 0.000 0.221 2.36E-05 Down

NONHSAT152857.1 0.247 0.000 2.55E-05 Up

NONHSAT044057.2 1.112 0.000 2.88E-05 Up

DE, differentially expressed; NPSLE, neuropsychiatric systemic lupus erythematosus.

DE lncRNAs for validation”). In this way, 10 aberrantly expressed lncRNAs were selected for qRT-PCR validation in PBMCs from 26 NPSLE patients, 31 Non-NPSLE patients and 30 healthy subjects (Figure 3).

To determine the specific roles of lncRNAs in NPSLE,

correlations of these DE lncRNA expression levels with experimental characteristics of NPSLE patients were analyzed. As shown in Figure 4A,B,C,D,E,F, the expression levels of NONHSAT039491.2, NONHSAT106801.2, NONHSAT024353.2 and NONHSAT207026.1 were negatively associated with SLEDAI scores, frequency of

CD19+ B cells, serum IgG levels, and glomerular filtration

rate (GFR), respectively; whereas NONHSAT207026.1 and NONHSAT208182.1 levels positively correlated with serum uric acid (UA) and urinary red blood cells (RBC) counts, respectively; no significant correlation existed between these DE lncRNAs and characteristics including

age, disease duration, serum immunoglobulin (Ig) E, IgA, complement 3, complement 4, C-reactive protein, 24-hour

proteinuria, albumin and IgG in cerebrospinal fluid (CSF,

the detailed correlation analysis results was shown in

Table S3). Further, the role of these DE genes in visceral involvement were analyzed, and found the expression levels of NONHSAT208182.1, NONHSAT024353.2,

N O N H S AT 1 0 6 8 0 1 . 2 ( r = − 0 . 0 5 , P = 0 . 0 4 9 ) a n d

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Table 2 Top 20 DE mRNAs between NPSLE and Non-NPSLE patients

Gene id Gene name FPKM(NPSLE) FPKM(Non-NPSLE) P value Up/Down

ENSG00000000971 CFH 5.430 0.335 5.07E-05 Up

ENSG00000174944 P2RY14 6.522 0.303 2.06E-05 Up

ENSG00000204422 XXbac-BPG32J3.20 0.000 2.234 6.65E-05 Down

ENSG00000279339 CTD-2373H9.3 0.205 0.005 5.22E-05 Up

ENSG00000067798 NAV3 0.068 0.003 4.26E-04 Up

ENSG00000112299 VNN1 29.434 5.851 4.55E-04 Up

ENSG00000131203 IDO1 1.159 0.036 2.61E-04 Up

ENSG00000133063 CHIT1 2.497 0.511 4.00E-04 Up

ENSG00000140379 BCL2A1 188.683 37.180 3.90E-04 Up

ENSG00000157551 KCNJ15 7.988 0.454 1.53E-04 Up

ENSG00000170486 KRT72 0.262 2.305 1.13E-04 Down

ENSG00000180596 HIST1H2BC 89.945 14.199 2.25E-04 Up

ENSG00000186049 KRT73 0.419 2.315 2.58E-04 Down

ENSG00000204936 CD177 89.236 9.899 4.48E-04 Up

ENSG00000205611 LINC01597 0.000 0.129 2.57E-04 Down

ENSG00000211675 IGLC1 10.934 0.177 3.71E-04 Up

ENSG00000215837 SDHAP2 0.344 5.894 1.87E-04 Down

ENSG00000229644 NAMPTP1 96.435 18.623 4.10E-04 Up

ENSG00000242616 GNG10 48.562 12.134 4.69E-04 Up

ENSG00000244270 RPL32P29 6.749 0.470 3.68E-04 Up

DE, differentially expressed; NPSLE, neuropsychiatric systemic lupus erythematosus.

NONHSAT001281.2 with olfactory memory (Figure 4K,L), respectively.

Given the multitude of clinical manifestations related to NPSLE, the correlations of expression levels of DE lncRNAs with clinical manifestations were analyzed. Compared with patients without such symptoms, expression levels of NONHSAT106801.2 and NONHSAT208182.1, NONHSAT207026.1, and NONHSAT039491.2 were

significantly downregulated in those with fever (Figure 5A,B),

arthralgia (Figure 5C), dizziness and headache (Figure 5D), respectively; while expression levels of ENST00000356215, NONHSAT024353.2 and NONHSAT208182.1 were upregulated in patients with oral ulcer (Figure 5E), consciousness disorder (Figure 5F) and unstable walking (Figure 5G), respectively.

Available studies on the significance of different autoantibodies in NPSLE have shown controversial results (19).

In our study, NONHSAT106801.2 was significantly upregulated while NONHSAT024353.2 was downregulated in anti-SSA+ patients compared to anti-SSA- patients (

Figure 5H,I);

NONHSAT087499.2 was significantly upregulated in

anti-ribonucleoprotein antibody (anti-RNP)+ patients compared to anti-RNP- patients while NONHSAT039491.2 was down-regulated both in anti-RNP+ and anti-dsDNA+ patients compared to anti-RNP- and anti-dsDNA- patients, respectively (Figure 5J,K,L); no difference of lncRNA levels was found between groups positive and negative for anti-cardiolipin antibody, anti-nuclear antibody, anti-smith antibody, anti-histone antibody, anti-ribosomal P antibody and anti-nucleosome antibody, respectively.

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*** ** *** *** * * * * *** *** *** *** * * * * ** ** *** *** ** * * * * ns ns ns ns ns NPSLE Relative NONHSA T208182.1 level Relative NONHSA T106801.2 level Relative NONHSA T039491.2 level Relative NONHSA T207026.1 level

Relative ENST00000356215 level Relative ENST00000414046 level

[image:9.595.55.539.83.436.2]

Relative NONHSA T087499.2 level Relative NONHSA T001281.2 level Relative NONHSA T024353.2 level Relative NONHSA T182114.1 level NPSLE NPSLE NPSLE NPSLE NPSLE NPSLE NPSLE NPSLE NPSLE No-NPSLE No-NPSLE No-NPSLE No-NPSLE No-NPSLE No-NPSLE No-NPSLE No-NPSLE No-NPSLE No-NPSLE HC HC HC HC HC HC HC HC HC HC 25 20 15 10 5 0 8 6 4 2 0 8 6 4 2 0 40 30 20 10 0 20 15 10 5 0 8 6 4 2 0 30 20 10 0 2.5 2.0 1.5 1.0 0.5 0.0 2.0 1.5 1.0 0.5 0.0 8 6 4 2 0

A

E

I

B

F

J

C

G

D

H

Figure 3 Relative expression levels of ten selected lncRNAs in PBMCs of NPSLE patients, Non-NPSLE patients and HCs identified using qRT-PCR. (A,D,E,F,G,I,J) the expression levels of seven lncRNAs (NONHSAT208182.1, NONHSAT182114.1, NONHSAT106801.2, NONHSAT039491.2, ENST00000356215, NONHSAT087499.2 and NONHSAT207026.1) were significantly upregulated NPSLE patients compared to Non-NPSLE patients; (B,C) the expression levels of two lncRNAs (NONHSAT001281.2 and NONHSAT024353.2) were significantly downregulated in NPSLE patients compared to Non-NPSLE patients; (H) the difference in lncRNA ENST00000414046 was not significant. n(NPSLE)= 26, n(Non-NPSLE) =31, n (HC) =30. *, P<0.05; **, P<0.01; ***, P<0.001.

dosage of glucocorticoid (GLU) and immunosuppressive a g e n t s i n c l u d i n g h y d r o x y c h l o r o q u i n e ( H C Q ) , cyclophosphamide (CYC), and mycophenolate (MMF). It was found that only NONHSAT024353.2 was highly expressed in CTX-treated patients compared with patients without CTX treatment (0.232±0.07019, n=9 vs.

0.5652±0.06684, n=17, P=0.0042), but no relationship was found in the expression of other lncRNAs between immunosuppressive and non-immunosuppressive group, as well as different doses of corticosteroids treatment group (Table S4), these above findings indicating that these

abnormally expressed lncRNAs between NPSLE and

Non-NPSLE group might not be influenced by clinical therapy.

GO and KEGG pathway analyses of DE mRNAs

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Relative NONHSA T039491.2 level Relative NONHSA T106801.2 level Relative NONHSA T024353.2 level Relative NONHSA T207026.1 level Relative NONHSA T207026.1 level Relative NONHSA T024353.2 level

Relative ENST00000356215 level Relative NONHSA

T039491.2 level Relative NONHSA T039491.2 level Relative NONHSA T001281.2 level Relative NONHSA T208182.1 level

Relative ENST00000356215 level

Relative NONHSA

T208182.1 level

10 15 20 25

0 200 400 600 800

20 40 60 80 0 5 10 15 0 2 4 6 8 10 0 2 4 6 8 10 Olfactory memory Olfactory memory Olfactory threshold

Pulmonary arterial pressure (mmHg)

0 200 400 600 800 20 40 60 80 40 60 80 Serum UA (umol/L) Urinary RBC count (Per uL) Pulmonary arterial pressure (mmHg) Pulmonary arterial pressure (mmHg)

0.0 0.1 0.2 0.3 0.4 0.5 0 10 20 30 40 50 100 200 300 Serum lgG (g/L) GFR (ml/min) CD19+ B cell numbers (109/L)

SLEDAI r=−0.44 p=0.02

r=0.46 p=0.02

r=0.60

p=0.03 r=0.64

p=0.046 r=p=0.01−0.75

r=−0.68 p=0.03 r=−0.62

p=0.02 r=0.58 p=0.04 r=0.76

p<0.0001 r=−0.83 p=0.01

r=−0.41

p=0.04 r=0.45

[image:10.595.54.548.76.460.2]

p=0.02 8 6 4 2 0 −2 3 2 1 0 1.5 1.0 0.5 0.0 40 30 20 10 0 −10 40 30 20 10 0 1.5 1.0 0.5 0.0 20 15 10 5 0 8 6 4 2 0 8 6 4 2 0 1.5 1.0 0.5 0.0 25 20 15 10 5 0 20 15 10 5 0 25 20 15 10 5 0 −5

A

E

I

B

F

J

C

G

K

D

H

L

Figure 4 Correlation between DE lncRNAs and experimental characteristics in NPSLE patients. (A,B,C,D) The expression levels of NONHSAT039491.2, NONHSAT106801.2, NONHSAT024353.2 and NONHSAT207026.1 were negatively associated with systemic lupus erythematosus disease activity index (SLEDAI) scores, frequency of CD19+ B cells, serum IgG levels, and glomerular filtration rate (GFR), respectively. (E,F) NONHSAT207026.1 and NONHSAT208182.1 levels were positively correlated with serum uric acid and urinary red blood cells counts, respectively; (G,H,I) NONHSAT208182.1, NONHSAT024353.2 and ENST00000356215 showed significant association with pulmonary arterial pressure levels of NPSLE patients; (J,K,L) the negative association of ENST00000356215 with olfactory threshold, NONHSAT039491.2 and NONHSAT001281.2 with olfactory memory.

data demonstrated that the most significantly enriched cellular components of DE mRNAs in PBMCs of NPSLE were the external side of the axon, intrinsic and integral component of plasma membrane (Figure 6A, http://cdn. amegroups.cn/static/other/5984c3b0471eb93f988c76e7 878328c7/atm.2020.03.25-7.xls). The most significantly enriched molecular functions of DE mRNAs were carbon hydrate binding, protein homodimerization activity and substrate-specific transmembrane transporter activity (Figure 6A, http://cdn.amegroups.cn/static/other/5984c

3b0471eb93f988c76e7878328c7/atm.2020.03.25-7.xls);

The most significantly enriched biological processes of DE

mRNAs were leukocyte chemotaxis and migration, and cell chemotaxis (Figure 6A, http://cdn.amegroups.cn/static/oth er/5984c3b0471eb93f988c76e7878328c7/atm.2020.03.25-7.xls); these together reveal the roles of significantly dysregulated mRNAs in NPSLE.

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* *** *** * * * * * * ** ** * Relative NONHSA T106801.2 level

Relative ENST00000356215 level

Relative NONHSA T024353.2 level Relative NONHSA T087499.2 level Relative NONHSA T039491.2 level Relative NONHSA T039491.2 level Relative NONHSA T024353.2 level Relative NONHSA T208182.1 level Relative NONHSA T106801.2 level Relative NONHSA T039491.2 level Relative NONHSA T208182.1 level Relative NONHSA T207026.1 level Fever

Oral ulcer No-oral ulcer

Fever

No-fever No-fever Arthralgia No-arthralgia Dizziness and headache

Consciousness

disorder Unstable walking SSA+

SSA+ RNP+ RNP RNP+ RNP ds-DNA+ ds-DNA

No-consciousness

disorder walkingStable SSA−

[image:11.595.51.545.84.438.2]

SSA− No-dizziness and headache 8 6 4 2 0 20 15 10 5 0 1.5 1.0 0.5 0.0 25 20 15 10 5 0 8 6 4 2 0 8 6 4 2 0 1.5 1.0 0.5 0.0 25 20 15 10 5 0 8 6 4 2 0 8 6 4 2 0 25 20 15 10 5 0 40 30 20 10 0

A

E

I

B

F

J

C

G

K

D

H

L

Figure 5 Correlation between DE lncRNAs and clinical characteristics in NPSLE patients. (A,B) NONHSAT106801.2 and NONHSAT208182.1 expression levels in NPSLE patients with and without fever; (C) NONHSAT207026.1 expression level in NPSLE patients with and without arthralgia; (D) NONHSAT039491.2 expression level in NPSLE patients with and without dizziness and headache symptom; (E) ENST00000356215 expression level in NPSLE patients with and without oral ulcer; (F) NONHSAT024353.2 expression level in NPSLE patients with and without consciousness disorder; (G) NONHSAT208182.1 expression level in NPSLE patients with and without unstable walking symptom; (H,I) NONHSAT106801.2 and NONHSAT024353.2 expression levels in anti-SSA+ and anti-SSA- NPSLE patients; (J,K) NONHSAT087499.2 and NONHSAT039491.2 expression levels in anti-RNP+ and anti-RNP- NPSLE patients; (L) NONHSAT039491.2 expression level in anti-dsDNA+ and anti-dsDNA- NPSLE patients. *, P<0.05; **, P<0.01; ***, P<0.001.

revealed that the DE mRNAs in PBMCs of NPSLE were significantly involved in hematopoietic cell lineage, staphylococcus aureus infection, and osteoclast differentiation (Figure 6B, http://cdn.amegroups.cn/ static/other/a916f2ba1dfe9c79658b5d5b12c5539b/ atm.2020.03.25-8.xls). Our data showed that the DE mRNAs were each individually associated with 11 pathways, among them, the metabolic pathway, neurotrophin signaling pathway, and chemokine signaling pathway were three of the most enriched pathways within the set of DE mRNAs (Figure 6B, http://cdn.amegroups.cn/static/other/a916f2ba1d

fe9c79658b5d5b12c5539b/atm.2020.03.25-8.xls), suggesting that these pathways may be involved in the development of NPSLE.

lncRNA-mRNA co-expression network

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Top 30 of GO Enrichment Top 30 of Pathway enrichment

4 6 8 0 10 20 30 Rich factor Gene_number Gene_number q_value q_value GO_domain biological_process cellular_component molecular_function 4 1 8 2 12 3 16 4 1.00 1.00 0.75 0.75 0.50 0.50 0.25 0.25 Rich factor carbohydrate binding synapse intrinsic component of plasma membrane integral component of plasma membrane axon response to other organism response to molecule of bacterial origin response to cytokine response to biotic stimulus response to bacterium regulation of locomotion regulation of cellular component movement regulation of cell motility regulation of cell migration negative regulation of cell proliferation leukocyte migration leukocyte chemotaxis inflammatory response immune system process immune response endocytosis defense response to other organism defense response cytokine-mediated signaling pathway chemotaxis chemical homeostasis cellular response to cytokine stimulus cell motility cell migration cell chemotaxis

Tuberculosis Transcriptional misregulation in cancer Systemic lupus erythematosus Synaptic vesicle cycle Staphylococcus aureus infection Sphingolipid metabolism SNARE interactions in vesicular transport Renin-angiotensin system Protein digestion and absorption Phagosome Osteoclast differentiation NOD-like receptor signaling pathway Neurotrophin signaling pathway Natural killer cell mediated cytotoxicity Metabolic pathways Measles MAPK signaling pathway Leishmaniasis Insulin resistance Influenza A Inflammatory mediator regulation of TRP channels HTLV-I infection Hematopoietic cell lineage Folate biosynthesis Cytokine-cytokine receptor interaction Complement and coagulation cascades Chemokine signaling pathway Bile secretion Apoptosis Amoebiasis

[image:12.595.55.545.84.277.2]

A

B

Figure 6 Enrichment analysis of GO terms and pathways for DE mRNAs between NPSLE and Non-NPSLE group. (A) The top 30 GO analyses which consisted of significant molecular function, cellular component and biological process of DE mRNAs; (B) significantly enriched pathways of DE mRNAs. P <0.01, FC >2.0 or <−2.0.

cn/static/other/a7791bdae56436daf1f955a9b6288064/ atm.2020.03.25-9.xls). In addition, our data showed that the co-expression network was composed of 216 network nodes and 251 connections. To further address DE mRNAs as possible predicted target of these DE lncRNAs, the expression levels of top 10 DE mRNAs that have characterized proteins were performed using RT-PCR. Compared with that of Non-NPSLE patients, the relative expression levels of CFH, P2RY14, VNN1, IDO1, BCL2A1and KCNJ15 were significantly increased in PBMCs from NPSLE patients (Figure S1). Considering of the co-expression network of DE lncRNAs with DE mRNAs (http://cdn.amegroups.cn/static/other/a7791b dae56436daf1f955a9b6288064/atm.2020.03.25-9.xls), and the negative correlated role of IDO1 and BCL2A1 with NONHSAT001281.2 (Table S3), our data support that differential lncRNAs might operate via modulating expressions of their correlated, relevant mRNAs in NPSLE.

Discussion

Recent discoveries have revealed that lncRNAs are aberrantly expressed in a broad spectrum of autoimmune diseases (AD) including SLE (14,15,20), and thus, play key roles in promoting and maintaining AD initiation and progression, demonstrating their clinical potential as biomarkers and therapeutic targets. Studies have shown that serum lnc-gas5, lnc0597, lnc-DC could be used as

diagnostic markers for SLE (12). LncRNA NEAT1 acted as

an inflammatory regulator to activate the MAPK pathway

involved in SLE pathogenesis (21). LncRNA MALAT1 could affect the expression level of IL-21 in monocytes by regulating SIRT1 signaling and participate in SLE development (13). lncRNA-Gas5 binds to the DNA binding region of the glucocorticoid receptor, preventing the receptor from interacting with the glucocorticoid response element of genomic DNA, thereby affecting the patient's response to glucocorticoids (22). These above

findings supported the pathogenic role of lncRNAs in SLE

pathogenesis (23), but the roles of lncRNAs in NPSLE remain largely unknown.

Growing evidence showed that blood–brain barrier (BBB) dysfunction may be essential to the development of NPSLE (24,25), allowing the passive diffusion of auto-reactive antibodies and cytokines in CSF of SLE patients.

Considering of the specific link between PBMCs, serum and

CSF (26), CSF are rare, difficult to obtain and traumatic, while peripheral blood is easy to obtain, with more clinical utility. In our study, compared with that of Non-NPSLE patients, hundreds of DE lncRNAs were found by RNA-seq and 10 of them were validated by qRT-PCR in PBMCs from NPSLE patients (Figure 3, Figure S1), and the correlation of expression levels of DE lncRNAs with experimental indicators in CSF from NPSLE patients were

explored. Unfortunately, there was no significant correlation

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[image:13.595.64.523.82.562.2]

Figure 7 Network graph of DE lncRNA-related mRNAs between NPSLE and Non-NPSLE group. Octagon and circles represent DE lncRNAs and their related DE genes, respectively. Red and green represent upregulated and downregulated lncRNAs or genes, respectively. P<0.01, FC >2.0 or <−2.0.

in CSF of NPSLE patients (Table S3). Since protein or IgG levels in CSF positively associated with SLEDAI of NPSLE patients, while in our study, none of these lncRNAs displayed significantly association with SLEDAI except NONHSAT039491.2, thus the link between these lncRNAs with NPSLE activity still needs further investigation.

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as well as unstable walking, respectively (Figure 5D,F,G). To predict their possible target gene, except for lncRNA-mRNA co-expression network analysis (Figure 7), the correlations of DE lncRNAs with DE mRNAs confirmed by RT-PCR were analyzed, and found negative correlation of expression levels IDO1 and BCL2A1 with that of NONHSAT208182.1 in PBMCs from NPSLE patients (Table S3), together with the positive association of NONHSAT208182.1 levels with unstable walking (Figure 5G), it is likely that NONHSAT208182.1 may be involved in the development of unstable walking related-NPSLE through targeting IDO1 or BCL2A1, further RNA-pulldown and CHIP experiments will be performed to support the possible target of NONHSAT001281.2 in our next experiments.

Olfactory disturbance was one of the manifestations of NPSLE (28). Recently, the correlation between decreased olfactory abilities and disease activity, especially CNS manifestations of NPSLE patients is supported by increasing studies, indicating that smell decrement might be a useful and easy tool for the physician in early diagnosis of CNS involvement in autoimmune diseases (18). Interestingly, negative association of ENST00000356215 with olfactory threshold, NONHSAT039491.2 and NONHSAT001281.2 with olfactory memory were found in our NPSLE cohort. Since accumulating studies found that decreased sense of smell was associated with history of neuropsychiatric manifestations and higher disease activity, ENST00000356215, NONHSAT039491.2 and NONHSAT001281.2 may be used as predictors of diffuse disorders in NPSLE patients but still need further investigation.

As one of the major causes of death in SLE, SLE related PAH could also be postulated as vasculitis led by immune-mediated vasculopathy (29), a potential link between PAH and NPSLE manifestations, however, have never been studied. In our study, NONHSAT208182.1, NONHSAT024353.2, NONHSAT106801.2 and ENST00000356215 all showed significant association with PAH levels of these NPSLE patients. Furthermore, NONHSAT106801.2 and NONHSAT024353.2 levels were also associated with the presence of anti-SSA, which had been reported to be an independent variable with PAH in SLE (30), supporting that some specific lncRNAs together with autoantibodies could be recommended in early diagnosis and precise management of NPSLE-associated PAH patients.

Auto-antibody mediated vasculopathy is one of the mechanisms for NPSLE (19). To date, more than 116 auto-antibodies have been reported in SLE, and at least 20 of them, including 11 brain-specific and 9 systemic auto-antibodies,

have been controversially associated with NPSLE. Among

these auto-antibodies, ACA and anti-RNP were significantly

associated with specific manifestations of neuropsychiatric disease attributed to SLE (19). In our study, our data support important correlations of NONHSAT106801.2, N O N H S AT 0 2 4 3 5 3 . 2 , N O N H S AT 0 3 9 4 9 1 . 2 a n d

NONHSAT087499.2 in NPSLE patients with some specific

autoantibodies including SSA, RNP, and anti-dsDNA, respectively (Figure 5H,I,J,K,L). Although the

detection of auto-antibodies in serum or cerebrospinal fluid

has not been included as a diagnostic marker within the

ACR classification (19,27), our data indicate that lncRNAs

together with their specific autoantibodies might be used

for precise management of NPSLE. Various inflammatory

cytokines and chemokines, including IL-6, IL-1, IL-8, IL-10, tumor necrosis factor-α (TNF-α), interferon-α

(IFN-α), IP-10/CXCL10, MCP-1/CCL2, normal T-cell expressed and secreted (RANTES)/CCL5 as well as CX3CR1, have been reported to increase the permeability of the BBB, providing access for circulating autoantibodies and leukocytes into the CNS, and thus, involved in the pathogenesis of NPSLE (31-33). It is worth noting that, several GO terms were enriched in gene lists through a bioinformatics analysis. Since lncRNAs have been reported to play important roles in immune cell homeostasis (34), and chemokines are crucial for NPSLE development, our results suggest important effects of these lncRNAs together with chemokines in the diagnosis of NPSLE.

Plenty of studies have shown that lncRNAs are expressed

in a highly lineage-specific manner. Cao et al. elucidated for

the first time that DCs-specific lncRNA lnc-DC can inhibit

SHP1-mediated de-phosphorylation by directly binding to the structural region near the STAT3 phosphorylation site Tyr705, thereby enhancing STAT3 signaling and participating in DCs differentiation and functional maintenance (35). Chen et al. found that PU.1 regulated lnc-MC can regulate mononuclear macrophage differentiation by "sponge" adsorption of miRNA-199a-5p (36). In our study, compared with that of Non-NPSLE patients, NONHSAT208182.1 and NONHSAT087499.2 were specifically highly expressed while NONHSAT001281.2 were reduced in CD19+ B cells from NPSLE patients, NONHSAT182.114.1 and ENST00000356215 were specifically highly expressed in CD4+ T cells, while

NONHSAT024353.2 were significantly reduced in CD19+

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In summary, to the best of our knowledge, this is the first study that describes the expression profiles of human lncRNAs in NPSLE using RNA-seq. Altered lncRNAs may play potential roles in the pathogenesis and/or development of “the most clinical challenging ‘visceral’ involvement of SLE”. To accurately and comprehensively elucidate the role of lncRNAs in NPSLE, more comprehensive laboratory and clinical researches will be needed to confirm whether these lncRNAs play essential roles in the pathogenesis, treatment, and prognosis of NPSLE.

Conclusions

We have demonstrated a comprehensive lncRNA expression

profile in PBMCs of NPSLE patients, whose deregulation

could be related to the pathophysiology of the disease.

More specifically, this study has highlighted some pathways

that could be crucial in the onset or the maintenance of the

disease. Our finding shed light on the understanding of the

molecular mechanisms of lncRNAs in the pathogenesis of NPSLE.

Acknowledgments

The authors thank the patients who were involved in this study and the individuals from the rheumatology departments who took part in the care of these patients.

Funding: The work was supported by the Major International (Regional) Joint Research Project (No. 81720108020), National Natural Science Foundation of China (No. 81871283, 81571586, 81501347 and 81601418), Research Project of Jiangsu Province Health Committee (H2019060), National Natural Science Foundation of Jiangsu (BK20150098), Jiangsu Provincial Special Program of Medical Science (BE2015602), Medical Science and Technology Development Key Project of Nanjing (ZKX15018), and Jiangsu Province Six Talent Peaks Project (No. 2016-WSN-148).

Footnote

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form and declare: the authors have no

conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was

approved by the Ethics Committee of the Affiliated Drum

Tower Hospital of Nanjing University Medical School (SC201700201) and was conducted in accordance with the principle set forth under the 1989 Declaration of Helsinki.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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[image:17.595.49.549.105.892.2]

Table S1 Demographic and clinical characteristics of SLE patients

Characteristics SLE, n=57 Non-NPSLE, n=31 NPSLE, n=26 HC, n=30

Demographic

Sex (female/male) 50/7 25/6 25/1 24/6

Age, yrs 37.8±2.5 37.7±2.3 38.0±2.7 34.7±1.5

Current smoking habit (%) 7.3 10.1 4.0 16.7

Age at onset, yrs 33.5±2.6 33.2±2.5 33.8±2.8 –

Disease duration, month 54.3±10.4 53.7±8.8 55.1±12.4 –

SLEDAI 11.8 ±0.9 10.7±0.8 13.2±0.9 –

Clinical chart review (%) –

Arthritis 52.6 54.8 50.0 –

Malar rash 40.3 38.7 42.3 –

Discoid lesions 10.5 9.6 11.5 –

Photosensitivity 22.8 25.8 19.2 –

Oral ulcers 12.3 12.9 11.5 –

Pericarditis 31.6 32.3 30.7 –

Pleuritis 21.0 22.5 19.2 –

Proteinuria 56.2 64.5 46.2 –

Leuco/erythrocyturia 28.1 32.3 23.1 –

Thrombopenia 35.1 38.7 30.8 –

Leucopenia 26.3 29.0 23.1 –

Fever 66.6 64.5 69.2 –

History of consciousness disorder 14.0 0 30.8 –

History of dizziness and headache 21.0 6.4 38.5 –

History of unstable walking 5.2 0.0 11.5 –

History of convulsion 10.5 0.0 23.1 –

History of unclear vision 3.5 3.2 3.9 –

History of reynold phenomenon 8.7 9.6 7.7 –

History of weak limbs 31.6 29.0 34.6 –

History of limb numbness 17.6 19.4 15.4 –

History of urinary retention 1.7 0 3.8 –

Autoantibodies

Anti-nuclear antibody 89.5 90.3 88.5 –

Anti-SSA antibody 67.9 70.0 65.4 –

Anti-SSB antibody 17.6 19.4 15.4 –

Anti-ds-DNA 45.6 48.4 42.3 –

Anti-Sm antibody 40.3 38.7 42.3 –

Anti-cardiolipin antibody 17.8 12.9 23.7 –

Anti-ribonucleoprotein antibody 36.9 35.5 38.5 –

Anti-histone antibody 43.9 41.9 46.2 –

Anti-ribosomal P antibody 19.3 19.4 19.2 –

Anti-nucleosome antibody 36.8 38.7 34.6 –

Current medication (%)

Acetylsalicylic acid 75.4 64.5 88.4 –

Antidepressant 17.6 12.9 23.1 –

Benzodiazepine 7.0 3.2 11.5 –

Hydroxychloroquine 80.7 83.9 76.9 –

Immunosuppressant 70.2 64.5 76.9 –

Prednisone 94.7 90.3 100.0 –

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LncRNA ID Forward primer Reverse primer

NONHSAT208182.1 TGTCCTTTGAATCGGGTCAT TCCATGCCAAGAAGCCTTAC

NONHSAT001281.2 CTGGGACAGCTAAGGACGAC AATGAGGGCAATCCAAACAG

NONHSAT024353.2 AGCACAGGGTGGAGACACTT CTTGGCTTCTCGTCCAATTT

NONHSAT182114.1 CGTAACAACTTCGGCAAGGT TGAGGGAGAAAGACGACCAG

NONHSAT106801.2 GCCGTGACTTCCAAACAACT CGAAGCGATGTATGGTTTGA

NONHSAT039491.2 ACGTGCAGCTCAAGGAAAG CCAGGTGCGTGACCATCTAT

ENST00000356215 GTGAAGCCCTAACCCACCTT GGTGATCCTTGATGCTCCTT

ENST00000414046 CTGTGGATGACATGGCCTTA GATGCCAGCTTTGAGTGGA

NONHSAT087499.2 CCCTGGAAAGTACCGTGTGT ATGACATGGAAGTGCTGCTG

NONHSAT207026.1 CCTGGCGCATTACAACAAG TTTCTCTGGAAGCGTGAGTG

β-actin GTGGCCGAGGACTTTGATTG CCTGTAACAACGCATCTCATATT

CFH TTGTCTCCTGACCTCCCAA TCCACCACTTCACTGTGTC

P2RY14 ATCACTCAGCAGATCATTCC TCCAGAAGATGGCCACGAAG

NAV3 GCTGCATCACAATTTCAGGTGGGT TTGAGATGATGCCACGTCTTCGGA

VNN1 GAACCCAGTATGTCTTTCCT CATACAACCTCCCAAACAGA

IDO1 GGTCATGGAGATGTCCGTAA ACCAATAGAGAGACCAGGAAGAA

CHIT1 ACCCTGTTAGCCATCGGAGCTGG TGCACAGCAGCATCCACCTTGAGG

BCL2A1 TCATATTTTGTTGCGGAGTTC TCCAGCCAGATTTAGGTTCAA

KCNJ15 CCACATCAGAACTCCCTTCAAACA AGTTCACTTTCAGACGAAGCACCTA

KRT27 CAAGAGGCCTCAAGGGATCT GGCTCGGAGAGGTTATGACC

HIST1H2BC ACGAGGAGTAGACCTGATGATGT GTATCACCTATTTCCATTGTCTCAATTGC

[image:18.595.45.543.96.488.2]
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Clinical characteristics r vs. P NONHSAT208182.1 NONHSAT001281.2 NONHSAT024353.2 NONHSAT182114.1 NONHSAT106801.2 NONHSAT039491.2 ENST00000356215 NONHSAT087499.2 NONHSAT207026.1

SLEDAI r –0.17 –0.09 –0.28 –0.07 –0.36 –0.44 0.09 0.09 0.30

P 0.40 0.65 0.16 0.72 0.07 0.02 0.66 0.67 0.14

Age r –0.001 –0.22 –0.20 0.04 –0.03 0.15 –0.19 –0.11 –0.12

P 0.99 0.27 0.32 0.86 0.89 0.47 0.35 0.58 0.56

Duration of disease r 0.24 0.26 0.01 0.02 0.07 0.35 0.08 –0.07 –0.23

P 0.23 0.20 0.98 0.91 0.73 0.08 0.69 0.73 0.26

Olfactory threshold r –0.25 –0.35 0.23 –0.33 –0.21 0.43 –0.64 –0.16 0.09

P 0.49 0.32 0.52 0.35 0.57 0.22 0.05 0.66 0.81

Olfactory memory r 0.13 –0.68 –0.27 0.07 –0.53 –0.75 –0.20 –0.23 0.04

P 0.73 0.03 0.44 0.86 0.11 0.01 0.58 0.52 0.92

WBC (CSF) r –0.07 0.15 –0.56 –0.33 –0.16 0.26 –0.25 –0.36 0.17

P 0.82 0.63 0.06 0.29 0.62 0.42 0.43 0.26 0.60

Protein (CSF) r 0.18 –0.14 –0.40 0.50 –0.03 –0.07 –0.09 0.18 –0.34

P 0.58 0.65 0.20 0.10 0.92 0.82 0.78 0.58 0.28

Albumin (CSF) r 0.36 0.08 –0.36 0.50 0.29 0.01 0.04 0.28 –0.39

P 0.28 0.81 0.28 0.12 0.38 0.98 0.90 0.41 0.24

IgG (CSF) r 0.01 –0.22 –0.36 0.39 –0.26 –0.21 –0.10 0.03 –0.39

P 0.99 0.51 0.28 0.24 0.44 0.54 0.76 0.93 0.24

C3 r –0.02 0.02 0.08 –0.11 0.11 0.24 –0.25 –0.09 –0.14

P 0.93 0.92 0.70 0.62 0.61 0.26 0.25 0.66 0.52

C4 r –0.18 –0.04 0.20 0.02 –0.11 0.10 –0.25 –0.10 –0.16

P 0.41 0.84 0.34 0.93 0.60 0.64 0.24 0.64 0.46

Globulin r –0.08 –0.21 –0.39 0.02 –0.10 –0.14 –0.23 0.03 –0.04

P 0.70 0.29 0.05 0.92 0.64 0.49 0.25 0.87 0.86

Albumin r 0.09 0.26 0.22 –0.20 0.29 0.38 0.06 –0.16 –0.02

P 0.67 0.20 0.27 0.32 0.15 0.05 0.77 0.45 0.91

IgG r 0.01 –0.33 –0.41 0.11 –0.18 –0.10 –0.17 –0.14 –0.12

P 0.96 0.11 0.04 0.60 0.38 0.64 0.40 0.50 0.57

IgA r –0.13 0.08 –0.14 0.0008 –0.03 –0.08 –0.20 0.26 0.07

P 0.52 0.71 0.51 1.00 0.90 0.69 0.34 0.20 0.75

IgM r –0.18 0.05 0.01 –0.29 –0.07 0.10 –0.15 0.17 0.18

P 0.40 0.82 0.98 0.17 0.74 0.63 0.47 0.42 0.39

IgE r –0.18 –0.40 –0.26 0.24 –0.03 –0.02 –0.04 0.04 0.03

P 0.40 0.05 0.21 0.24 0.87 0.91 0.86 0.85 0.88

24 h proteinuria r 0.22 –0.19 –0.37 –0.02 –0.28 –0.30 0.10 0.11 0.40

P 0.31 0.37 0.08 0.93 0.19 0.17 0.65 0.61 0.06

Cr r 0.03 0.17 0.19 –0.05 –0.15 –0.26 0.12 0.09 0.23

P 0.90 0.41 0.36 0.79 0.47 0.20 0.55 0.68 0.26

BUN r 0.09 –0.30 –0.08 –0.04 –0.18 –0.11 –0.07 –0.11 –0.09

P 0.68 0.13 0.71 0.85 0.37 0.58 0.72 0.58 0.66

UA r 0.06 0.09 0.15 –0.11 –0.20 –0.21 0.12 0.12 0.46

P 0.79 0.65 0.47 0.60 0.33 0.29 0.54 0.57 0.02

GFR r –0.03 –0.18 –0.02 –0.08 0.19 0.15 –0.30 –0.14 –0.45

P 0.90 0.37 0.94 0.71 0.35 0.46 0.14 0.48 0.02

CRP r –0.32 –0.20 –0.12 –0.06 –0.35 –0.004 –0.16 –0.13 –0.18

P 0.11 0.32 0.55 0.78 0.08 0.99 0.43 0.52 0.38

ESR r –0.24 –0.32 –0.20 –0.17 –0.30 –0.27 –0.28 0.10 0.09

P 0.26 0.12 0.35 0.41 0.15 0.19 0.18 0.63 0.66

WBC r –0.09 0.29 –0.05 –0.14 –0.11 0.07 0.11 –0.21 –0.09

P 0.68 0.15 0.83 0.49 0.61 0.74 0.60 0.31 0.67

HB r 0.03 0.21 0.35 –0.15 0.22 0.38 –0.19 –0.04 –0.02

P 0.89 0.31 0.08 0.46 0.28 0.05 0.35 0.85 0.91

PLT r –0.10 0.25 –0.16 –0.02 –0.08 0.04 –0.05 0.07 0.31

P 0.61 0.23 0.43 0.92 0.69 0.86 0.80 0.73 0.12

Urinary RBC r 0.72 0.26 –0.32 0.24 0.30 –0.04 0.17 0.35 –0.10

P <0.0001 0.19 0.11 0.23 0.14 0.85 0.41 0.08 0.63

D-Dmier r 0.17 0.08 0.0008 –0.16 –0.09 0.05 –0.23 –0.19 –0.14

P 0.45 0.73 1.00 0.47 0.69 0.83 0.30 0.39 0.53

CD4+ T cells r –0.60 0.18 0.28 –0.24 –0.14 0.08 –0.51 0.05 –0.03

P 0.12 0.67 0.50 0.56 0.74 0.84 0.20 0.90 0.94

CD8+ T cells r –0.55 0.08 0.27 0.29 0.06 0.36 0.03 –0.03 –0.16

P 0.15 0.84 0.52 0.48 0.89 0.38 0.95 0.94 0.71

NK cells r 0.11 0.36 0.35 0.05 0.41 0.58 –0.05 –0.26 0.002

P 0.79 0.38 0.40 0.90 0.31 0.13 0.91 0.53 1.00

B cells r –0.44 –0.31 –0.58 0.04 –0.83 –0.00008 –0.45 –0.04 –0.01

P 0.79 0.38 0.40 0.90 0.31 0.13 0.91 0.53 1.00

PAH r –0.58 –0.41 0.60 –0.41 –0.55 –0.08 –0.62 –0.52 –0.46

P 0.04 0.16 0.03 0.17 0.05 0.80 0.02 0.07 0.12

CFH r –0.10 0.08 0.31 –0.32 –0.03 –0.50 0.43 0.21 0.03

P 0.24 0.45 0.14 0.21 0.13 0.12 0.08 0.13 0.90

P2RY14 r –0.34 0.23 0.32 0.34 –0.34 –0.38 0.42 –0.06 –0.32

P 0.24 0.34 0.35 0.12 0.09 0.34 0.23 0.92 0.13

VNN1 r 0.34 0.24 0.03 0.08 0.23 0.53 –0.23 –0.024 0.03

P 0.22 0.25 0.48 0.83 0.32 0.39 0.07 0.98 0.83

IDO1 r –0.61 –0.09 –0.28 0.08 –0.43 0.09 0.32 –0.05 –0.54

P 0.02 0.19 0.32 0.92 0.09 0.84 0.34 0.67 0.09

BCL2A1 r 0.45 0.38 0.42 0.37 –0.33 0.93 –0.92 –0.43 –0.42

P 0.03 0.05 0.32 0.22 0.06 0.10 0.35 0.93 0.23

KCNJ15 r –0.34 0.54 0.23 –0.84 –0.03 0.23 –0.33 0.45 –0.32

P 0.50 0.14 0.06 0.32 0.94 0.24 0.09 0.34 0.32

[image:19.992.43.914.120.1426.2]
(20)

Clinical treatment r vs. P NONHSAT208182.1 NONHSAT001281.2 NONHSAT024353.2 NONHSAT182114.1 NONHSAT106801.2 NONHSAT039491.2 ENST00000356215 NONHSAT087499.2 NONHSAT207026.1

Pred r 0.27 0.27 -0.15 -0.09 -0.02 -0.01 0.03 -0.17 -0.22

P 0.18 0.19 0.45 0.66 0.93 0.98 0.90 0.41 0.29

HCQ r -0.09 0.10 0.13 -0.05 -0.05 -0.24 0.14 0.34 0.35

P 0.68 0.63 0.52 0.80 0.81 0.25 0.49 0.09 0.08

CTX r 0.35 0.17 -0.54 0.05 -0.19 -0.21 0.27 0.12 0.21

P 0.08 0.40 0.004 0.81 0.36 0.31 0.19 0.57 0.29

MMF r -0.004 -0.13 0.16 -0.30 -0.01 -0.09 -0.07 -0.21 -0.20

P 0.99 0.52 0.44 0.13 0.98 0.67 0.73 0.30 0.32

[image:20.992.51.936.96.243.2]
(21)

Relative mRNA expr

ession levels

CFH P2R

Y14 NAV3

VNN1 IDO1 CHIT1

BCL2A1 KCNJ15 KR T27

HIST1H2BC

**

** **

** ** *

*

* ***

***

*** *** ***

* *

ns ns

ns ns ns

ns ns ns

ns

ns ns ns

ns ns ns 150

100

50

3

2

1

[image:21.595.117.487.81.253.2]

0

Figure

Figure 1 Expression profiles of lncRNAs and mRNAs in SLE patients and HCs. (A,C) Heat map showing hierarchical clustering of lncRNAs and mRNAs with expression changes greater than two-fold and P value <0.01
Figure 2 Expression profiles of lncRNAs and mRNAs in NPSLE patients and Non-NPSLE patients
Table 1 Top 20 DE lncRNAs between NPSLE and Non-NPSLE patients.
Figure 3 Relative expression levels of ten selected lncRNAs in PBMCs of NPSLE patients, Non-NPSLE patients and HCs identified using qRT-PCR
+7

References

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