Introduction

Molecular Medicine Reports

1791-2997 1791-3004

D.A. Spandidos

10.3892/mmr.2017.8293

mmr-17-03-3591

Articles

Bioinformatics analysis of differentially expressed gene profiles associated with systemic lupus erythematosus

Chengjiang

1 Zhao

Yangjing

1 Lin

2 Yang

Xinxin

1 Yan

Meina

1 Min

Yujiao

1 Pan

Zihui

1 Xia

Sheng

1 Shao

Qixiang

1Department of Immunology, Key Laboratory for Laboratory Medicine of Jiangsu Province, Jiangsu University Medical School, Zhenjiang, Jiangsu 212013, P.R. China 2Center for Computational Science, University of Miami, Coral Gables, FL 33146, USA

Correspondence to: Professor Qixiang Shao, Department of Immunology, Key Laboratory for Laboratory Medicine of Jiangsu Province, Jiangsu University Medical School, 301 Xuefu Road, Zhenjiang, Jiangsu 212013, P.R. China, E-mail: shao_qx@ujs.edu.cn

032018

18122017

17 3 3591 3598 30082017 17112017

2018

This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

DNA microarray and high-throughput sequencing have been widely used to identify the differentially expressed genes (DEGs) in systemic lupus erythematosus (SLE). However, the big data from gene microarrays are also challenging to work with in terms of analysis and processing. The presents study combined data from the microarray expression profile (GSE65391) and bioinformatics analysis to identify the key genes and cellular pathways in SLE. Gene ontology (GO) and cellular pathway enrichment analyses of DEGs were performed to investigate significantly enriched pathways. A protein-protein interaction network was constructed to determine the key genes in the occurrence and development of SLE. A total of 310 DEGs were identified in SLE, including 193 upregulated genes and 117 downregulated genes. GO analysis revealed that the most significant biological process of DEGs was immune system process. Kyoto Encyclopedia of Genes and Genome pathway analysis showed that these DEGs were enriched in signaling pathways associated with the immune system, including the RIG-I-like receptor signaling pathway, intestinal immune network for IgA production, antigen processing and presentation and the toll-like receptor signaling pathway. The current study screened the top 10 genes with higher degrees as hub genes, which included 2′-5′-oligoadenylate synthetase 1, MX dynamin like GTPase 2, interferon induced protein with tetratricopeptide repeats 1, interferon regulatory factor 7, interferon induced with helicase C domain 1, signal transducer and activator of transcription 1, ISG15 ubiquitin-like modifier, DExD/H-box helicase 58, interferon induced protein with tetratricopeptide repeats 3 and 2′-5′-oligoadenylate synthetase 2. Module analysis revealed that these hub genes were also involved in the RIG-I-like receptor signaling, cytosolic DNA-sensing, toll-like receptor signaling and ribosome biogenesis pathways. In addition, these hub genes, from different probe sets, exhibited significant co-expressed tendency in multi-experiment microarray datasets (P<0.01). In conclusion, these key genes and cellular pathways may improve the current understanding of the underlying mechanism of development of SLE. These key genes may be potential biomarkers of diagnosis, therapy and prognosis for SLE.

SLE differentially expressed genes pathway enrichment analysis gene ontology protein-protein interaction network

Introduction

Systemic lupus erythematosus (SLE) is a fatal multisystem autoimmune disease with diverse manifestations, which may lead to severe morbidity and mortality. It is characterized by a wide profile of autoantibodies, widespread precipitation of immune complexes and aberrant innate and adaptive immune responses. SLE preferentially invades skin, joints, kidneys and nervous system leading to various typical symptoms (1–3). Recent studies have revealed that genetic factors have an important role in the susceptibility to SLE (4–6). However, the precise pathogenic mechanism of SLE remains to be elucidated. Therefore, it is important to identify the molecular mechanisms of occurrence and development of SLE.

High-throughput sequencing and microarray technology have been used to screen for differential gene expression in disease. Previous gene expression profiling analyses have been conducted to identify differentially expressed genes (DEGs) and cellular pathways in SLE (7,8). However, at present, no specific gene has been identified to act as a potential diagnosis marker in SLE. The large quantity of data obtained from high-throughput sequencing and microarray technology have not been fully used. Ducreux et al (9) collected whole blood samples from SLE patients and healthy volunteers in order to identify DEGs. However, interactions among DEGs and key genes involved in signal conduction pathway of SLE remain to be elucidated. In addition, previous studies of genetic factors primarily focused on a single gene; however, interactions among multiple genes may result in the characteristics of multisystem invasion observed in SLE (10,11). It is of note that disease-associated gene expression networks have potential roles in immune response, which highlights their mechanism and therapeutic values for SLE (12,13). Therefore, the combination of microarray technology and bioinformatics analysis may aid in the identification of the gene regulatory networks, key genes and their associated pathways in SLE.

The present study analyzed the raw data from the GSE65391 dataset downloaded from Gene Expression Omnibus (GEO; www.ncbi.nlm.nih.gov/geo/), which provides free access to high-throughput gene expression datasets (14). The present study selected GSE65391 due to the large sample quantity of the dataset and the transcriptional data were complemented by demographic, laboratory and clinical data. Additionally, 24 technical replicates of healthy samples were included and used for batch correction purposes. DEGs between SLE and healthy controls were identified in order to identify the potential mechanisms of SLE. Subsequently, functional enrichment, protein-protein interaction (PPI) networks and sub-network analyses were performed to determine the significant pathogenic genes and their critical pathways involved in the mechanism of occurrence and development of SLE.

Materials and methods <sec> <title>Microarray data

The gene expression profile of GSE65391 was downloaded from the GEO database (15). It was established on the platform of GPL10558 Illumina HumanHT-12 V4.0 expression bead chip (Illumina Inc., San Diego, CA, USA). The dataset contained 996 whole blood samples, including 924 SLE samples and 72 healthy controls.

Data preprocessing and identification of DEGs

The original expression datasets following background correction, normalization and probe summarization were converted into expression measures using the R affy package (www.bioconductor.org/packages/release/bioc/html/affy.html). The linear models for microarray data package in Bioconductor (www.bioconductor.org/packages/release/bioc/html/limma.html) was used to identify DEGs according to the cut-off criteria: Adjusted P<0.01 and |log₂ fold-change (FC)|>0.8.

Gene ontology (GO) and pathway enrichment analysis of DEGs

GO is a widely used method for unification of biology which collected structured, defined and controlled vocabulary for a large scale of genes annotation (16). Kyoto Encyclopedia of Genes and Genomes (KEGG) database is a collection of online databases regarding gene functions, enzymatic pathways and associates genomic information with higher-order functional information (17). The Database for Annotation, Visualization and Integrated Discovery (DAVID; david.ncifcrf.gov) provides a comprehensive set of functional annotation tools to identify GO terms and KEGG pathways (18,19). To understand the biological functions and cellular pathways of the DEGs, the present study used DAVID to identify GO categories and KEGG pathways.

Analysis of protein-protein interaction (PPI) network and sub-networks

Search Tool for the Retrieval of Interacting Genes (STRING; string-db.org) (20) is a precomputed global resource designed to evaluate PPI information. In the present study, the STRING online tool was used to analyze the PPI of DEGs and experimentally validated interactions with a combined score >0.4 were selected as significant. Therefore, the degree of connectivity was statistically analyzed in networks using CytoHubba (version 3.4.0) (www.cytoscape.org) to obtain the significant nodes or hub genes in the PPI network (21). CytoHubba, a Java plugin for Cytoscape (version 3.4.0), is capable of predicting important nodes and subnetworks in a given network by several topological algorithms. The Molecular Complex Detection (MCODE) algorithm is an established automated method to find highly interconnected modules as molecular complexes or clusters in large PPI networks (22). Additionally, the Multi Experiment Matrix (MEM; biit.cs.ut.ee/mem/index.cgi) (23) is a web-based multi experiment gene expression query and visualization tool. It gathers hundreds of publicly available gene expression datasets from the ArrayExpress database (24). In order to determine whether these hub genes from different probe sets were co-expressing or not, the present study compared ISG15 ubiquitin-like modifier (ISG15) and the other hub genes using MEM.

Results <sec> <title>Identification of DEGs in SLE

The present study analyzed the GSE65391 microarray data, containing 924 SLE samples and 72 healthy controls. Following data normalization, preprocessing and filtering with the criteria of adjusted P<0.01 and |log₂FC|>0.8 a total of 310 genes were identified as differentially expressed in patients with SLE compared with healthy controls. Among them, 193 were upregulated and 117 were downregulated. The volcano plot of all DEGs is presented in Fig. 1.

Expression and function of DEGs in SLE

Several GO categories of upregulated and downregulated DEGs were enriched in GO terms associated with immune response and immune cell metabolism. The most significant biological process (BP) of upregulated and downregulated DEGs was the immune system process. In addition to the enrichment in immune response, the upregulated DEGs were significantly enriched in the BPs of the defense response to other organisms (e.g., virus), whereas the downregulated DEGs were significantly enriched in immune cell activities, such as leukocytes and lymphocytes (Fig. 2).

DEGs-associated pathways in SLE are enriched in multi-system harm diseases

The most significantly enriched pathways of the DEGs are presented in Fig. 3. The DEGs were enriched in immune diseases and infectious diseases, including SLE, inflammatory bowel disease, rheumatoid arthritis, herpes simplex virus (HSV) infection, influenza A, measles, asthma and primary immunodeficiency. Furthermore, the DEGs were also enriched in signaling pathways associated with the immune system, including the RIG-I-like receptor signaling pathway, intestinal immune network for IgA, antigen processing and presentation and toll-like receptor signaling pathway.

Major PPI networks in SLE

The present study identified 1,514 interactions among the DEGs. There were 338 interactions matching the condition of experimentally validated interaction with a combined score >0.4. The top 15 significant interactions with highest combined score are presented in Table I. Based on the information in the STRING database, the PPI network was constructed (Fig. 4). The present study screened the top 10 genes with higher degrees as hub genes in the PPI network using CytoHubba, including 2′-5′-oligoadenylate synthetase 1 (OAS1), MX dynamin like GTPase 2 (MX2), interferon induced protein with tetratricopeptide repeats 1 (IFIT1), interferon regulatory factor 7 (IRF7), interferon induced with helicase C domain 1 (IFIH1), signal transducer and activator of transcription 1 (STAT1), ISG15, DExD/H-box helicase 58 (DDX58), interferon induced protein with tetratricopeptide repeats 3 (IFIT3), and 2′-5′-oligoadenylate synthetase 2 (OAS2). Additionally, the modules of DEGs were obtained using the MCODE plugin. The presents study selected the top 3 significant modules and analyzed the cellular pathways of the genes involved in the modules (Fig. 5). The current study determined that these hub genes were also involved in the RIG-I-like receptor signaling (Fig. 6), cytosolic DNA-sensing, toll-like receptor signaling and ribosome biogenesis pathways. In addition, it was determined that the hub genes from different probe sets exhibited significant co-expression tendency in multi-experiment microarray datasets (P<0.01; Fig. 7).

Discussion

The cause of SLE has not been fully elucidated. It is believed to involve hormonal, environmental and genetic factors. However, genetic factors are essential to determine the process of SLE occurrence and development (25). However, no single causal gene has been identified. Instead, multiple genes may influence a person's chance of developing SLE when triggered by environmental factors (26). Microarray and high-throughput sequencing may be used to quantify the expression levels of large numbers of genes simultaneously, combined with bioinformatics analysis, this may allow for the identification of the associated pathways and key genes with complex diseases. A total of 310 DEGs that were differently expressed in SLE compared with healthy controls in the present study using the gene expression profile contained in GSE65391.

According to BPs identified of the identified SLE-associated DEGs, a significant portion of upregulated DEGs were enriched in response to virus, type I interferon signaling pathway and negative regulation of viral genome replication. Downregulated DEGs were primarily involved in the immune response, alpha-beta T cell differentiation and regulation of immune response. These findings were in accordance with the features of immune abnormalities belonging to autoimmune diseases. Virus infection is also one of the primary reasons for onset of SLE in patients. Additionally, the DEGs were mainly enriched in pathways associated with susceptibility to immune diseases and infectious diseases including HSV, influenza A, SLE, measles, inflammatory bowel disease, tuberculosis and rheumatoid arthritis. The DEGs were also involved in various signaling pathways, such as RIG-I-like receptor signaling, cytosolic DNA-sensing and toll-like receptor signaling pathways. These findings were consistent with the knowledge that SLE is a type of multiple systemic autoimmune disease which may affect many organ systems including the skin, joints and internal organ (27).

Based on the PPI network, the present study identified the top 10 hub genes: OAS1, MX2, IFIT1, IRF7, IFIH1, STAT1, ISG15, DDX58, IFIT3 and OAS2. Further analysis revealed that these hub genes, from different probe sets, exhibited significant co-expression tendency in multi-experiment microarray datasets (P<0.01), which indicated that all of these hub genes may work together in the pathogenesis of SLE. OAS1, an IFN-regulated gene, had the highest degree of connectivity, is expressed as part of the innate immune response to viruses and enriched in HSV infection, hepatitis C, measles, influenza A and the NOD-like receptor signaling pathway. Rios et al (28) reported that OAS1 may activate RNase L leading to degradation of cellular and viral RNA, inhibiting thus viral replication. Previous studies revealed that viral infection and antiviral immunity contributed to the development of autoimmunity (29). The second hub gene MX2 was primarily enriched in interferon-g signaling and immune system pathways. GO annotations revealed that MX2 has a BP of GTP binding and GTPase activity. Fernández-Trujillo et al (30) reported that MX2 protein has an antiviral activity against RNA and DNA viruses in vitro. The third hub gene IFIT1, one of IFN-induced antiviral proteins, was the first gene described as a candidate gene for SLE (31). It has been previously reported that IFIT1 may interact with Rho/Rac guanine nucleotide exchange factor, regulate the activation of Rho/Rac proteins and contribute to the pathogenesis of SLE (32). IRF7, a member of the interferon regulatory transcription factor (IRF) family, was involved in multifarious functions and signaling pathways associated with the immune system, including the toll-like receptor signaling, NOD-like receptor signaling, RIG-I-like receptor signaling and cytosolic DNA-sensing pathways. Furthermore, IRF7 may efficiently activate IFN-β and the IFN-α and mediate their induction in the virus-activated, myeloid differentiation primary response 88 (MyD88)-independent pathway and the toll-like receptor-activated, MyD88-dependent pathway. Previous studies had demonstrated that the IFN system had an important role in the etiopathogenesis of SLE (33,34). IFIH1, a member of the RIG-I like receptor (RLR) family, was involved in immune system and cytosolic sensors of pathogen-associated DNA pathway. Lincez et al (35) reported that reduced expression of IFIH1 may prevent autoimmune diabetes. In addition, a previous study demonstrated that the gene polymorphism of IFIH1 was associated with increased sensitivity to IFN-α and serologic autoimmunity in patients with lupus (36). Another hub gene, STAT1, was reported to be involved in perturbing regulatory T cell homeostasis, which may be a possible marker of SLE disease severity by augmenting STAT1 signaling (37). ISG15, a member of the Ub1 family, is a 17 kDa secreted protein and has a significant sequence homology to ubiquitin (38). ISG15 may induce the synthesis and secretion of IFN-γ from B-cell-depleted lymphocytes. It has been previously reported that ISG15 acts as a cytokine modulator in immune response (39,40). It has also been previously reported that IFN-α/IFN-γ may induce the activation of the JAK-STAT1 pathway, regulate proliferation and activation of immunocytes and lead to the abnormal activation of the immune system, which may affect the occurrence and development of SLE (41–43). Therefore, ISG15 may be involved in the abnormal immune response of SLE. DDX58 was also enriched in the RIG-I-like receptor signaling pathway and had a synthetic function as viral double-stranded RNA recognition and the regulation of immune response. However, the specific function of DDX58 in SLE requires further investigation. IFIT3 and OAS2 are interferon-inducible genes whose encoding proteins are involved in the innate immune response to viral infection, which may lead to poor prognosis of SLE.

Through module analysis of the PPI network, the present study determined that the development of SLE was closely associated with the RIG-I-like receptor signaling, cytosolic DNA-sensing, toll-like receptor signaling and ribosome pathways. A previous study proposed that patients with SLE had an increased expression of type I IFN and the production of IFN depended on the RIG-I-like receptor signaling pathway (44). The RIG-I-like receptor signaling pathway could also trigger innate immune responses against viral infections that may limit viral replication and stimulate adaptive immunity (45). Further analysis revealed that 4 hub genes were involved in the RIG-I-like receptor signaling pathway and had core positions in the pathway (Fig. 6). The cytosolic DNA-sensing pathway was reported to signal via stimulator of interferon genes that mediate immunity to pathogens and promoted autoimmune pathology in DNase II- and DNase III-deficient mice. Previous studies declared that polymorphisms of genes involved in toll-like receptor signaling pathway had a link with the development of SLE (46,47). Module 2 was presented in Fig. 5 and enriched in ribosome biogenesis pathway. This pathway was involved in pre-rRNA processing, ribosome assembly and the synthetic processing of >200 proteins. Ribosomal P protein was a main component part of ribosome (48). Additionally, molecular biomarkers for the diagnosis of SLE were anti-ribosomal P antibodies (49), which was sufficient to confirm that this pathway had a significant effect on the development of SLE.

In conclusion, these hub genes may have various roles in the occurrence and development of the SLE, leading to damage of multiple systems in SLE. Combined with bioinformatics analysis, the current study identified key genes and cellular pathways involved in the occurrence and development of SLE. The present study may provide a basis for an improved understanding of SLE. However, the current findings are limited by the lack of experimental verification in vivo and in vitro. Therefore, future experimental studies should be conducted to confirm the expression and function of the identified genes at the protein level, which may be an area of future research.

Acknowledgements

The present study was supported by grants from Chinese National Natural Science Foundation Grant (grant nos. 81671541, 81273202 and 31400773), Clinical Medicine Science & Technology Project of Jiangsu Province of China (grant no. BL2013024), Postgraduate Research & Practice Innovation Program of Jiangsu Province (grant no. KYCX17_1820) and China Scholarship Council, Program of Innovative Research Team of Jiangsu Province, Project of the Priority Academic Program Development of Jiangsu Higher Education Institutions and Project of the Key Academic Program Development of Jiangsu University (grant no. 1291270019).

Abbreviations

SLE

systemic lupus erythematosus

DEG

differentially expressed genes

gene ontology

PPI

protein-protein interaction

KEGG

Kyoto Encyclopedia of Genes and Genomes

biological process

fold-change

IBD

inflammatory bowel disease

HSV

herpes simplex virus

OAS1

2′-5′-oligoadenylate synthetase 1

MX2

MX dynamin like GTPase 2

IFIT1

interferon induced protein with tetratricopeptide repeats 1

IRF7

interferon regulatory factor 7

IFIH1

interferon induced with helicase C domain 1

STAT1

signal transducer and activator of transcription 1

ISG15

ISG15 ubiquitin-like modifier

DDX58

DExD/H-box helicase 58

IFIT3

interferon induced protein with tetratricopeptide repeats 3

OAS2

2′-5′-oligoadenylate synthetase 2

IFN

interferon

References 1

Weidenbusch

Kulkarni

Anders

The innate immune system in human systemic lupus erythematosus

Clin Sci (Lond)1316256342017

10.1042/CS20160415

28351959

Guo

Luo

Wei

Wang

Lan

Wei

Association of CD40 polymorphisms and haplotype with risk of systemic lupus erythematosus

Rheumatol Int3645522016

10.1007/s00296-015-3345-7

26289938

Mok

Kwok

Yip

Effect of renal disease on the standardized mortality ratio and life expectancy of patients with systemic lupus erythematosus

Arthritis Rheum65215421602013

10.1002/art.38006

23754671

Yang

Lau

Solving the genetic puzzle of systemic lupus erythematosus

Pediatr Nephrol30173517482015

10.1007/s00467-014-2947-8

25239301

Dang

Xin

Shan

Bian

Yuan

Liu

Gene-gene interaction of ATG5, ATG7, BLK and BANK1 in systemic lupus erythematosus

Int J Rheum Dis19128412932016

10.1111/1756-185X.12768

26420661

Wang

Liu

Zhao

Qin

P-glycoprotein gene MDR1 polymorphisms and susceptibility to systemic lupus erythematosus in Guangxi population: A case-control study

Rheumatol Int375375452017

10.1007/s00296-017-3652-2

28154898

Borrebaeck

Sturfelt

Wingren

Recombinant antibody microarray for profiling the serum proteome of SLE

Methods Mol Biol113467782014

10.1007/978-1-4939-0326-9_6

24497355

Zhu

Luo

Yan

Zuo

Autoantigen microarray for High-throughput autoantibody profiling in systemic lupus erythematosus

Genomics Proteomics Bioinformatics132102182015

10.1016/j.gpb.2015.09.001

26415621

4610965

Ducreux

Houssiau

Vandepapelière

Jorgensen

Lazaro

Spertini

Colaone

Roucairol

Laborie

Croughs

Interferon α kinoid induces neutralizing anti-interferon α antibodies that decrease the expression of interferon-induced and B cell activation associated transcripts: Analysis of extended follow-up data from the interferon α kinoid phase I/II study

Rheumatology (Oxford)55190119052016

10.1093/rheumatology/kew262

27354683

5034220

Smith

Fernando

Seo

Ní Gabhann

Piskareva

McCarthy

Howard

O'Connell

Conway

MicroRNA-302d targets IRF9 to regulate the IFN-induced gene expression in SLE

J Autoimmun791051112017

10.1016/j.jaut.2017.03.003

28318807

Humrich

Riemekasten

Clinical trials: The rise of IL-2 therapy-a novel biologic treatment for SLE

Nat Rev Rheumatol126956962016

10.1038/nrrheum.2016.173

27761021

Deng

Tsao

Genetic susceptibility to systemic lupus erythematosus in the genomic era

Nat Rev Rheumatol66836922010

10.1038/nrrheum.2010.176

21060334

3135416

Bentham

Morris

Graham

DSC

Pinder

Tombleson

Behrens

Martín

Fairfax

Knight

Chen

Genetic association analyses implicate aberrant regulation of innate and adaptive immunity genes in the pathogenesis of systemic lupus erythematosus

Nat Genet47145714642015

10.1038/ng.3434

26502338

4668589

Clough

Barrett

The gene expression omnibus database

Methods Mol Biol1418931102016

10.1007/978-1-4939-3578-9_5

27008011

4944384

Edgar

Domrachev

Lash

Gene expression omnibus: NCBI gene expression and hybridization array data repository

Nucleic Acids Res302072102002

10.1093/nar/30.1.207

11752295

99122

Ashburner

Ball

Blake

Botstein

Butler

Cherry

Davis

Dolinski

Dwight

Eppig

Gene ontology: Tool for the unification of biology. The gene ontology consortium

Nat Genet2525292000

10.1038/75556

10802651

3037419

Kanehisa

Goto

KEGG: Kyoto encyclopedia of genes and genomes

Nucleic Acids Res2827302000

10.1093/nar/28.1.27

10592173

102409

Huang da

Sherman

Lempicki

Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources

Nat Protoc444572009

10.1038/nprot.2008.211

19131956

Huang da

Sherman

Lempicki

Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists

Nucleic Acids Res371132009

10.1093/nar/gkn923

19033363

Szklarczyk

Morris

Cook

Kuhn

Wyder

Simonovic

Santos

Doncheva

Roth

Bork

The STRING database in 2017: Quality-controlled protein-protein association networks, made broadly accessible

Nucleic Acids Res45D362D3682017

10.1093/nar/gkw937

27924014

Chin

Chen

Lin

CytoHubba: Identifying hub objects and sub-networks from complex interactome

BMC Syst Biol8Suppl 4S112014

10.1186/1752-0509-8-S4-S11

25521941

4290687

Bader

Hogue

An automated method for finding molecular complexes in large protein interaction networks

BMC Bioinformatics422003

10.1186/1471-2105-4-2

12525261

149346

Adler

Kolde

Kull

Tkachenko

Peterson

Reimand

Vilo

Mining for coexpression across hundreds of datasets using novel rank aggregation and visualization methods

Genome Biol10R1392009

10.1186/gb-2009-10-12-r139

19961599

2812946

Rustici

Kolesnikov

Brandizi

Burdett

Dylag

Emam

Farne

Hastings

Ison

Keays

ArrayExpress update-trends in database growth and links to data analysis tools

Nucleic Acids Res41Database IssueD987D9902013

23193272

Yusuf

Kaliyaperumal

Jayaraman

Ramanathan

Devaraju

Genetic selection pressure in TLR9 gene may enforce risk for SLE in Indian Tamils

Lupus263073102017

10.1177/0961203316659151

27432810

Armstrong

Zidovetzki

Alarcón-Riquelme

Tsao

Criswell

Kimberly

Harley

Sivils

Vyse

Gaffney

GWAS identifies novel SLE susceptibility genes and explains the association of the HLA region

Genes Immun153473542014

10.1038/gene.2014.23

24871463

4156543

Fattal

Shental

Mevorach

Anaya

Livneh

Langevitz

Zandman-Goddard

Pauzner

Lerner

Blank

An antibody profile of systemic lupus erythematosus detected by antigen microarray

Immunology1303373432010

10.1111/j.1365-2567.2010.03245.x

20201986

2913213

Rios

Perelygin

Long

Lear

Zharkikh

Brinton

Adelson

Characterization of the equine 2′-5′ oligoadenylate synthetase 1 (OAS1) and ribonuclease L (RNASEL) innate immunity genes

BMC Genomics83132007

10.1186/1471-2164-8-313

17822564

2048516

Getts

Chastain

Terry

Miller

Virus infection, antiviral immunity, and autoimmunity

Immunol Rev2551972092013

10.1111/imr.12091

23947356

3971377

Fernández-Trujillo

García-Rosado

Alonso

Castro

Álvarez

Béjar

Mx1, Mx2 and Mx3 proteins from the gilthead seabream (Sparus aurata) show in vitro antiviral activity against RNA and DNA viruses

Mol Immunol566306362013

10.1016/j.molimm.2013.06.018

23911421

Relógio

Schwager

Richter

Ansorge

Valcárcel

Optimization of oligonucleotide-based DNA microarrays

Nucleic Acids Res30e512002

10.1093/nar/30.11.e51

12034852

117213

Pang

Shen

Chen

Hua

Qian

Rao

Using GST-tag to capture protein interaction of an interferon-inducible systemic lupus associated gene, IFIT1

Zhonghua Yi Xue Za Zhi837707732003(In Chinese)

12899756

Rönnblom

Alm

An etiopathogenic role for the type I IFN system in SLE

Trends Immunol224274312001

10.1016/S1471-4906(01)01955-X

11473831

Rönnblom

The importance of the type I interferon system in autoimmunity

Clin Exp Rheumatol344 Suppl 98S21S242016

Lincez

Shanina

Horwitz

Reduced expression of the MDA5 gene IFIH1 prevents autoimmune diabetes

Diabetes64218421932015

10.2337/db14-1223

25591872

Robinson

Kariuki

Franek

Kumabe

Kumar

Badaracco

Mikolaitis

Guerrero

Utset

Drevlow

Autoimmune disease risk variant of IFIH1 is associated with increased sensitivity to IFN-α and serologic autoimmunity in lupus patients

J Immunol187129813032011

10.4049/jimmunol.1100857

21705624

3304466

Goropevšek

Gorenjak

Gradišnik

Dai

Holc

Hojs

Krajnc

Pahor

Avčin

Increased levels of STAT1 protein in blood CD4 T cells from systemic lupus erythematosus patients are associated with perturbed homeostasis of activated CD45RA-FOXP3hi regulatory subset and follow-up disease severity

J Interferon Cytokine Res372542682017

10.1089/jir.2016.0040

28256939

Zuo

Sheng

Xia

Ouyang

ISG15 in the tumorigenesis and treatment of cancer: An emerging role in malignancies of the digestive system

Oncotarget774393744092016

10.18632/oncotarget.11911

27626310

5342061

Recht

Borden

Knight

A human 15-kDa IFN-induced protein induces the secretion of IFN-gamma

J Immunol147261726231991

1717569

Care

Stephenson

Barnes

Fan

Zougman

El-Sherbiny

Vital

Westhead

Tooze

Doody

Network analysis identifies proinflammatory plasma cell polarization for secretion of ISG15 in human autoimmunity

J Immunol197144714592016

10.4049/jimmunol.1600624

27357150

4974491

Dong

You

Fan

Ding

Sun

Hou

STS-1 promotes IFN-α induced autophagy by activating the JAK1-STAT1 signaling pathway in B cells

Eur J Immunol45237723882015

10.1002/eji.201445349

25959715

Han

Liu

Huang

Luo

Yin

Dipyrithione inhibits IFN-gamma-induced JAK/STAT1 signaling pathway activation and IP-10/CXCL10 expression in RAW264.7 cells

Inflamm Res598098162010

10.1007/s00011-010-0192-6

20372968

Zhao

Xie

Zhu

Chen

Activation of JAK-STAT1 signal transduction pathway in lesional skin and monocytes from patients with systemic lupus erythematosus

Zhong Nan Da Xue Xue Bao Yi Xue Ban361091152011

21368418

Rönnblom

Pascual

The innate immune system in SLE: Type I interferons and dendritic cells

Lupus173943992008

10.1177/0961203308090020

18490415

3694565

Dixit

Kagan

Intracellular pathogen detection by RIG-I-like receptors

Adv Immunol117991252013

10.1016/B978-0-12-410524-9.00004-9

23611287

3947775

Chai

Chua

Lim

Phipps

Insight into gene polymorphisms involved in toll-like receptor/interferon signalling pathways for systemic lupus erythematosus in South East Asia

J Immunol Res20145291672014

10.1155/2014/529167

24741605

3987947

Castiblanco

Varela

Castaño-Rodríguez

Rojas-Villarraga

Hincapié

Anaya

TIRAP (MAL) S180L polymorphism is a common protective factor against developing tuberculosis and systemic lupus erythematosus

Infect Genet Evol85415442008

10.1016/j.meegid.2008.03.001

18417424

Kressler

Hurt

Bassler

Driving ribosome assembly

Biochim Biophys Acta18036736832010

10.1016/j.bbamcr.2009.10.009

19879902

Tan

Cohen

Fries

Masi

McShane

Rothfield

Schaller

Talal

Winchester

The 1982 revised criteria for the classification of systemic lupus erythematosus

Arthritis Rheum25127112771982

10.1002/art.1780251101

7138600

Figure 1.

Volcano plot of 310 DEGs. Red, upregulated DEGs with log₂ FC>0.8 and adjusted P<0.01. Green, downregulated DEGs with log₂FC<-0.8 and adjusted P<0.01. FC, fold-change; DEGs, differentially expressed genes.

Figure 2.

Biological processes analysis of upregulated and downregulated differentially expressed genes associated with systemic lupus erythematosus. GO, gene ontology.

Figure 3.

Kyoto Encyclopedia of Genes and Genomes pathway analysis of differentially expressed genes associated with systemic lupus erythematosus. P-values for each pathway presented in brackets. NF-κB, nuclear factor-κB.

Figure 4.

Protein-protein interaction network of DEGs. Node size indicates the connectivity degree; larger circles indicate a higher degree. Circles indicate genes. Edges indicate the interaction between genes Red circles, upregulated DEGs. Green circles, downregulated DEGs. Squares, pathways associated with systemic lupus erythematosus.

Figure 5.

Top 3 modules from the protein-protein interaction network and the enriched cellular pathways of module genes. (A) Module 1 and (B) respective enriched pathway. (C) Module 2 and (D) respective enriched pathway. (E) Module 3 and (F) respective enriched pathway. FDR, false discovery rate.

Figure 6.

Kyoto Encyclopedia of Genes and Genomes pathway of hsa04622: RIG-I-like receptor signaling pathway. Red indicates upregulated DEGs, green indicates downregulated DEGs and purple indicates genes not identified as DEGs in systemic lupus erythematosus. Ellipses represent hub genes.

Figure 7.

Co-expression of hub genes from different probe sets in multi-experiment microarray datasets.

Table I.

Top 15 significant interactions experimentally validated with highest combined score.

Node1	Node2	Combined score
RPL23A	RPL7	0.999
RPL23A	RPS23	0.999
RPL23A	RPL14	0.999
RPL14	RPL7	0.999
RPL14	RPL13	0.999
RPL7	RPL13	0.999
RPL23A	RPL13	0.999
RPL7	RPS23	0.999
ISG15	USP18	0.999
RPS23	RPS4Y1	0.999
RPL14	RPS23	0.999
RPL13	RPS23	0.999
RPL23A	RPL22	0.999
HIST1H2AC	HIST1H2BD	0.999
RPL22	RPL14	0.998