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Expression profile of long noncoding RNAs and mRNAs in peripheral blood mononuclear cells from myasthenia gravis patients
Journal of Neuroimmunology 299 (2016) 124–129
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Journal of Neuroimmunology journal homepage: www.elsevier.com/locate/jneuroim
Expression profile of long noncoding RNAs and mRNAs in peripheral blood mononuclear cells from myasthenia gravis patients☆ Fang Zhang a,1, Guiyou Liu b,1, Yali Bu a, Xiaofeng Ma a, Junwei Hao a,⁎ a b
Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin 300052, China School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
a r t i c l e
i n f o
Article history: Received 24 June 2016 Received in revised form 3 September 2016 Accepted 8 September 2016 Available online xxxx Keywords: Long non-coding RNAs Messenger RNAs Myasthenia gravis Microarray
a b s t r a c t For the epigenetic characterization of myasthenia gravis (MG), we determined whether long noncoding RNAs (lncRNAs) and messenger RNAs (mRNAs) are expressed differentially in subjects with and without MG. Compared with healthy control subjects, the MG patients had 1561 upregulated lncRNAs, 1034 downregulated lncRNAs, 921 upregulated mRNAs, and 806 downregulated mRNAs (fold change N 2.0). Several GO terms including nucleic acid transcription factor activity, inflammatory response, regulation of leukocyte activation, lymphocyte proliferation and regulation of B cell proliferation were enriched in gene lists, suggesting a potential correlation with MG. Pathway analysis then demonstrated that cytokine-cytokine receptor interaction, intestinal immune network for lgA production, NOD-like receptor signaling pathway, NF-kappaB signaling pathway, cell adhesion molecules and TNF signaling pathway play important roles in MG. Co-expression network analysis indicated that 33 lncRNAs were predicted to have 31 cis-regulated target genes, and 65 lncRNAs appeared to regulate the patients' 45 trans target genes among differentially expressed lncRNAs. Our present study identified a subset of dysregulated lncRNAs and mRNAs in patients with MG, which may impact this disease process. © 2016 Published by Elsevier B.V.
1. Introduction Myasthenia gravis (MG) is a T and B cell-mediated autoimmune disease of the neuromuscular junctions (Lisak and Ragheb, 2012; Matsui et al., 2010; Thiruppathi et al., 2012). Muscle weakness and fatigue, as hallmarks of MG, resulting in the elicitation of an antibody-mediated autoimmune response against acetylcholine receptors (AChR) located at neuromuscular junctions. However, the epigenetic characteristics of this disease are not completely understood. LncRNAs, which are N200 nucleotides in length, represent a new class of noncoding RNAs (Necsulea et al., 2014). They contribute to a variety of biological cascades and are reported to be involved in neurodegenerative diseases (Wan et al., 2016), diabetic mellitus (Jae and Dimmeler, 2015; Wessel et al., 2015), autoimmune disease (Duarte,
☆ This work is independent of any sponsors; it was initiated and funded entirely by the investigators. This article has not been submitted for publication elsewhere. All authors have been informed of and approved this submission. We believe this study offers significant theoretical and practical input for practitioners in the field of epigenetics and will be of particular interest to your readership. ⁎ Corresponding author at: Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Anshan Road, Heping District, Tianjin 300052, China. E-mail address: [email protected] (J. Hao). 1 These authors contributed equally to this work.
http://dx.doi.org/10.1016/j.jneuroim.2016.09.005 0165-5728/© 2016 Published by Elsevier B.V.
2015; Xu et al., 2016a; Xu et al., 2016b; Zhang et al., 2016), cancer (Leucci et al., 2016; Schmitt and Chang, 2013), and cardiovascular diseases (Viereck et al., 2016). Noncoding RNAs are emerging as a new regulatory layer that affects both development of the immune system and its function (Huang et al., 2015; Ranzani and Rossetti, 2015; Wang et al., 2014). Although thousands of lncRNAs have been identified in the mammalian genome by bioinformatics analyses of transcriptomic data, their functional characterization is still largely incomplete. Recent studies show widespread changes in the expression of lncRNAs during activation of the innate immune response, particularly noted in T cell development, differentiation, and activation. These lncRNAs control important aspects of immunity such as production of inflammatory mediators, altering differentiation, and management of cell migration by regulating protein–protein interactions or by base pairing with RNA and DNA (Cui et al., 2014; Heward and Lindsay, 2014; Roux and Lindsay, 2015). Although several lncRNAs have been implicated in diverse processes and diseases, few examples of their ability to regulate autoimmune diseases have been described. In the present study, we performed an array of lncRNA and mRNA chip assays on peripheral blood mononuclear cells (PBMCs) from MG patients. Outstanding lncRNA functions were annotated based on co-expression genes and a gene ontology (GO) biological analysis process. The relationships among lncRNAs and mRNAs were revealed through cis and trans analyses. These results provide information for further studies of MG.
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2.3. RNA extraction and chip arrays
Table 1 Baseline characteristics.
Gender, M/F Age, years, median (range) Age at onset, years, median (range) Disease duration, median (range) Anti-AchR+ (%) Anti-Musk+ (%) Thymoma (%)
125
Control (n = 26)
MG (n = 48)
P value
8/18 37 (24–53) – – – – –
16/32 42 (27–62) 37 (21–59) 10.5 (0.8–17.5) 48/48 (100) 0 (0) 0 (0)
1.00 0.81 – – – – –
MG, myasthenia gravis.
2. Methods 2.1. Study population and trial design During an open enrollment, a total of 48 patients with type IV MG (Myasthenia Gravis Foundation of America, MGFA), who met the criteria of combined fluctuating muscle weakness with a positive neostigmine test, abnormal single-fiber EMG test, and positivity for AchR antibody, were recruited at Tianjin Medical University General Hospital from May 2013 to August 2015. These patients were within the peak timing of manifesting MG and before treatment with glucocorticoid or intravenous immune globulin. Exclusion criteria were the following: (Thiruppathi et al., 2012) co-presence of MG and other diseases of the immune system, (Matsui et al., 2010) presence of tumor(s) and systemic hematologic diseases, (Lisak and Ragheb, 2012) presence of recent infection, and (Necsulea et al., 2014) concomitant use of antineoplastic or immune-modulating therapies prior to blood sampling. We also recruited 20 age- and gender-matched healthy controls for the comparative study. The demographic and clinical features of all the patients and healthy controls are summarized in Table 1. The Tianjin Medical University General Hospital institutional review boards approved the trial protocol and supporting documentation. Informed consent was obtained at enrollment from all patients or legally acceptable surrogates.
2.2. Isolation of mononuclear cells from human peripheral blood (PBMCs) Peripheral blood anticoagulated by ethylene diamine tetraacetic acid (EDTA) was obtained from all MG patients and healthy controls. Human PBMCs were isolated with Ficoll-Hypaque gradients.
Total RNA was extracted from PBMCs using Trizol® reagent (Invitrogen, Grand Island, NY, USA). Approximately 200 ng of total RNA from each sample was used for the lncRNA microarray analyses. LncRNA expression was analyzed using OE_Biotech Human lncRNA chip software, containing 41,000 lncRNAs and 34,000 mRNAs. Those lncRNA and mRNA target sequences were merged from multiple databases: 23,898 from GENCODE/ENSEMBL; 14,353 from the Human LincRNA Catalog; 7760 from RefSeq; 5627 from USCS; 13,701 from ncRNA Expression Database (NRED); 21,488 from LNCipedia; 1038 from H-InvDB; 3019 from LncRNAs-a (Enhancer-like); 1053 from the Antisense ncRNA pipeline; 407 Hox ncRNAs; 962 UCRs; and 848 from the Chen Ruisheng lab (Institute of Biophysics, Chinese Academy of Sciences, Beijing, China). 4974 Agilent control probes. The lncRNA chip experiments were conducted at Capitalbio Corporation in Beijing, China. LncRNAs and mRNAs were analyzed using Cluster 3.0 software. The results were further analyzed using Tree View software. Green indicates low expression, and red indicates high expression in the output for these analyses. 2.4. Quantitative real-time PCR validation Total RNA was extracted from PBMCs with Trizol® reagent (Invitrogen) following the manufacturer's instructions. RNA quantity and quality were assessed using a Nanodrop ND-100 Spectrophotometer (Nanodrop Technologies, Wilmington, USA) and a 2100 Bioanalyzer (Agilent RNA 6000 Nano Kit, Waldbronn, Germany), with a 260:280 ratio of ≥ 1.5 and an RNA integrity number of ≥ 7 for the majority of the samples. For the reverse transcriptase (RT) reaction, SYBR Green RT reagents (Bio-Rad, Indianapolis, USA) were used. The lncRNA PCR results were quantified using the 2ΔΔct method against β-actin for normalization. The data represent the means of three experiments. 2.5. LncRNA co-expression analysis and gene function annotation Volcano plot filtering was used to identify lncRNAs and mRNAs with statistically significant differences in expression. Hierarchical clustering was applied to present the diacritical lncRNA and mRNA expression patterns among the samples. LncRNA classification was carried out to explore the potential function of the differentially expressed lncRNAs. GO analysis and pathway analyses were also performed to describe more fully the roles of the differentially expressed mRNAs. Furthermore,
Fig. 1. LncRNA and mRNA profiles of microarray data. Hierarchical clustering shows a distinguishable (A) lncRNA and (B) mRNA expression profile between the two groups. Plots here represent analysis of RNA extracted from PBMCs obtained from 6 MG patients and 5 healthy control subjects.
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3. Results 3.1. LncRNA and mRNA expression profiles in MG To determine the expression levels of lncRNAs associated with MG, we performed lncRNA and mRNA microarray analyses on the PBMCs of MG patients. These data were then compared to those obtained from the PBMCs of age- and gender-matched healthy controls. After separating signal from noise and performing t-tests, we observed up to twofold significant differences in lncRNAs and mRNA expression (P b 0.05). The lncRNA and mRNA expression data were clustered using Cluster 3.0 software and are plotted in Fig. 1. 3.2. Validation of disrupted lncRNA expression in MG Fig. 2. Comparison of lncRNA expression levels as determined by microarray and real-time PCR analyses. Three upregulated and three downregulated differentially expressed lncRNAs were validated by real-time PCR of RNA extracted from PBMCs from 48 MG patients and 20 healthy control subjects. Each sample was analyzed in triplicate. Column heights represent mean fold changes in expression of the MG group. Real-time PCR results are consistent with microarray data. ***P b 0.001: MG group versus healthy control group in real-time PCR validation.
a coding-non-coding gene co-expression network (CNC network) was drawn using Cytoscape, with Pearson coefficient N 0.98. The microarray analysis was performed by Capitalbio Corporation.
To verify the disruption of lncRNA expression in MG patients, we performed real-time PCR, which would determine the up- and downregulation of lncRNAs in each group. As shown in Fig. 2, differences in the expression of 6 lncRNAs were detected in MG patients compared with healthy control subjects. LncRNA XLOC 003810 was the most elevated (11.88-fold higher expression), followed by lncRNA XLOC 005780 (9.47-fold higher expression), and lncRNA ENSG00000259354.1 (8.38-fold higher expression). LncRNA XLOC 000734, lncRNA ATP6VOE2-AS1 and lncRNA ENSG00000250850.2 exhibited 5.37-, 7.68-, and 9.38-fold lower expression, respectively. These results were consistent with the results obtained from the microarray chip analyses.
2.6. Target prediction
3.3. LncRNA function annotation
For each lncRNA, we calculated the Pearson correlation of its expression value with that of each mRNA. The mRNAs that were co-expressed with the lncRNA were defined as having a Pearson coefficient that exceeded 0.98 and a P-value b0.05. We identified the mRNAs as cis-regulated target genes when (Thiruppathi et al., 2012) the mRNA loci were within a 10 kb window up- or downstream of the given lncRNA; and (Matsui et al., 2010) the Pearson correlation of lncRNA-mRNA expression was significant (r N 0.98 and P b 0.05). We then determined which mRNAs were likely to be trans-regulated by the lncRNA of interest. For each lncRNA, we calculated the overlap of the co-expressed mRNA set with transcriptional factor (TF) target genes and used hypergeometric distribution to calculate the significance of this overlap. When the mRNAs co-expressed with a given lncRNA significantly overlapped the target genes of a given TF, this TF presumably (or suggestively) interacted with the lncRNA, denoting that these mRNAs might be the trans-regulated target genes of that particular lncRNA.
To further explore the function of lncRNAs in MG, we subjected the results of the lncRNA and mRNA chip analyses to Pearson's correlation coefficient analysis, in which co-expression was considered at P N 0.98. LncRNA function was annotated using GO and KEGG pathway analyses. LncRNA ENSG00000267280.1 was associated with regulation of the inflammatory response, establishment of synaptic specificity at neuromuscular junction, positive regulation of cell migration, regulation of interleukin-1 beta production, and positive regulation of IkappaB kinase/NF-kappaB cascade (GO:0006954, GO:0007529, GO:0030335, GO:0032651, GO:0043123). LncRNA ENSG00000235138.1 was associated with cytokine activity, interleukin-5 receptor binding, inflammatory response, immune response, cytokine-mediated signaling pathway, positive regulation of B cell proliferation, and positive regulation of immunoglobulin secretion (GO:0005125, GO:0005137, GO:0006954, GO:0006955, GO:0019221, GO:0030890, GO:0051024). Selecting the reliability prediction terms
Fig. 3. (A). Top 30 GO terms for the difference in lncRNA co-expressed genes between MG patients and healthy control subjects. (B). KEGG pathways analysis. Top 30 pathways for the difference in lncRNA co-expressed genes between the MG patients and healthy control subjects.
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Fig. 4. LncRNAs' enrichment degree in different functions, such as biological process, cellular component and molecular function. The blue, green and red bars represent GO terms of biological process, cellular component and molecular function respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
(according to P-value and enrichment) produced a total of 30 enrichment GO terms (Fig. 3A). LncRNA KEGG pathways are listed in Fig. 3B, which included pathways for the intestinal immune network for IgA production, cytokine-cytokine receptor interaction, NF-kappaB signaling, NOD-like receptor signaling, cell adhesion molecules, TNF signaling and primary immunodeficiency. All these pathways are associated with immune and inflammatory responses. These results from the GO and KEGG pathway analyses confirmed the likelihood that lncRNAs are important players in such immune system functions as inflammation, immune response, cell differentiation, and proliferation (Fig. 4). 3.4. Cis analysis of the expression of lncRNAs and adjacent co-expression genes Evidence suggests that several lncRNAs regulate their own transcription in cis-regulatory fashion, as well as that of nearby genes, by recruiting remodeling factors to local chromatin. Prompted by this self-regulation evidence, we identified chromosomal co-expression genes 10 kbp upstream and downstream of the differentially expressed lncRNAs to determine potential lncRNA cis genes. Comparing MG patients to healthy controls, we found 31 cis genes (Table 2). 3.5. Trans mechanism of aberrant lncRNAs Currently, known trans regulation mechanisms involve factors that mediate chromatin regulation and transcription. We calculated the lncRNA co-expression genes of chromatin regulators and transcription factors (TF) in ENCODE to identify common genes involved in lncRNA regulation. Compared to healthy controls, in MG patients 45 trans genes were identified (Table 3). 4. Discussion In this study, we systematically screened the genome-wide expression pattern of lncRNAs as well as mRNAs in PBMCs obtained from MG patients (IV subtype, MGFA) and healthy controls. As a result, we identified 1561 upregulated and 1034 downregulated lncRNAs, and 921 upregulated and 806 downregulated mRNAs. Among them, 34 lncRNAs had a N6-fold difference, and 6 lncRNAs had a N 8-fold difference in expression level between the MG and control groups, suggesting the pivotal role of these lncRNAs in MG. Several candidate lncRNAs that
we identified were chosen for real-time PCR validation. Real-time PCR revealed the same direction of regulation in lncRNAs expression in MG and healthy controls. Therefore, our results from the real-time PCR analysis confirmed the microarray data to some extent. MG is traditionally regarded as an autoimmune antibody-mediated disorder of neuromuscular synaptic transmission whose complex pathogenesis is not well understood. Nevertheless, cytokines and chemokines may play a crucial role in the tissue damage and Table 2 “Cis” genes of aberrant lncRNAs. LncRNA
mRNA
Cis-regulation
Correlation
P value
p33678 p14386 p20717 p29417 p27434 p9911 p2099 p25807 p33842 p7144 p8336 p13834 p12967 p33413 p1630 p11734 p24849 p24849 p8846 p717 p26352 p17665 p8846 p13711 p8546 p26080 p30215 p778 p11275 p29405 p13399 p11144 p1657
BCL2A1 CLDN20 SELV LOC101928858 BCL11A AFF3 ITPRIP RNF214 ZNF322 ZNF594 ZNF573 LOC101927041 LOC101929911 RNASE4 DDIT4 SATB1-AS1 DKFZp667F0711 DKFZp667F0711 FCAR CHRM3-AS2 PSMG4 BTBD19 FCAR GTF2H2C_2 ZNF177 KIR3DX1 KIR3DX1 LOC284600 SRGAP3 CKMT2 SLC22A4 LOC102724900 LOC101929574
Sense Intergenic(10k) Intergenic(10k) Sense Antisense Bidirectional Antisense Intergenic(10k) Sense Intergenic(10k) Intergenic(10k) Sense Sense Sense Antisense Sense Sense Sense Intergenic(10k) Sense Sense Intergenic(10k) Intergenic(10k) Intergenic(10k) Intronic Sense Sense Sense Sense Antisense Antisense Sense Sense
0.988423097 0.958197566 0.969579552 0.976956168 0.983434175 0.977217313 0.984670267 0.988416613 0.958771416 0.950495739 0.963420815 0.975324743 0.974222322 0.975533386 0.995041452 0.965470243 0.985573234 0.984301318 0.98760845 0.991860409 0.956595064 0.965967646 0.989528739 0.974393426 0.950633518 0.973742323 0.962405174 0.981015774 0.992119054 0.968365167 0.977215819 0.967901851 0.987971836
1.11E-08 3.44E-06 8.37E-07 2.42E-07 5.54E-08 2.3E-07 3.92E-08 1.12E-08 3.24E-06 7.28E-06 1.9E-06 3.29E-07 4E-07 3.17E-07 2.47E-10 1.47E-06 2.98E-08 4.36E-08 1.51E-08 2.29E-09 4.07E-06 1.38E-06 7.09E-09 3.88E-07 7.19E-06 4.34E-07 2.15E-06 1.02E-07 1.98E-09 9.96E-07 2.3E-07 1.06E-06 1.32E-08
LncRNAs, long noncoding RNAs.
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Table 3 “Trans” mechanism of the aberrant lncRNAs. LncRNA
mRNA
Trans-regulation
Correlation
P value
p25234 p14884 p25234 p38187_v4 p37987_v4 p33704 p23505 p11081 p12139 p5400 p37036_v4 p1247 p8336 p15306 p2187 p4554 p19691 p10261 p99 p10261 p25234 p30177 p29036 p39396_v4 p1122 p10995 p21292 p106 p42164_v4 p25474 p8641 p39396_v4 p1076 p15933 p33415 p33414 p28307 p16958 p17624 p16958 p28526 p33413 p3949 p37690_v4 p39396_v4 p4074 p29405 p39396_v4 p39877_v4 p12560 p2878 p3643 p23670 p1247 p25940 p5543 p12682 p16288 p10845 p17770 p354 p28908 p33415 p33414 p41410_v4
C8orf44 C8orf44 TCAIM GINS2 FRA10AC1 FRA10AC1 BCKDHB EPHA10 USHBP1 USHBP1 ZFP14 ZFP14 ZFP14 ANKK1 ANKK1 CNR2 KLHDC4 MARVELD3 CLDN11 CLDN11 ZNF394 ZNF418 SCN2B TRAF5 TRAF5 PAQR8 NRIP3 MPV17L STK32C ZNF785 ZNF785 ZNF780B GEMIN2 GEMIN2 RNASE2 RNASE2 ZNF543 IAPP IAPP SEMA5A SEMA5A RNASE4 METTL21A METTL21A ZNF780B ZNF813 VSIG1 VSIG1 VSIG1 VSIG1 ZNF286B C21orf62 C21orf62 C21orf62 FAM178A ZNF234 ZNF345 ZNF345 DTWD2 RRP15 SSR1 ZNF550 RNASE2 RNASE2 ZNF417
miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration
0.979184076 0.967051673 0.950237997 0.955350047 0.969318114 −0.952529884 0.959270142 0.952529554 0.954738903 0.9513335 −0.957830902 0.966718485 0.961383732 0.950318927 −0.955676099 0.968166389 0.961728717 0.958409961 0.960502988 0.971992201 −0.96320495 0.953296975 0.957576275 0.953407368 0.95770186 0.951919235 −0.955885577 0.955756501 0.980183668 0.956998089 0.97744378 0.963352026 0.964345084 0.964600151 0.993152061 0.985149413 0.971432714 0.965658447 0.960150568 0.960495387 0.9646042 0.975533386 0.950844908 0.954049799 0.974040167 0.952496808 0.954029368 0.950775573 0.960626242 0.953291531 0.962158254 0.951695009 0.953493403 0.953057247 −0.96289371 −0.954129547 0.969430182 0.980898596 −0.954788502 0.953157303 −0.963927896 0.957974677 0.996203737 0.996220954 −0.950812641
1.54E-07 1.19E-06 7.45E-06 4.61E-06 8.69E-07 6.05E-06 3.07E-06 6.05E-06 4.9E-06 6.75E-06 3.58E-06 1.25E-06 2.42E-06 7.4E-06 4.46E-06 1.02E-06 2.32E-06 3.36E-06 2.67E-06 5.79E-07 1.95E-06 5.63E-06 3.67E-06 5.57E-06 3.63E-06 6.4E-06 4.37E-06 4.43E-06 1.23E-07 3.9E-06 2.2E-07 1.92E-06 1.7E-06 1.64E-06 1.05E-09 3.4E-08 6.32E-07 1.44E-06 2.78E-06 2.68E-06 1.64E-06 3.17E-07 7.06E-06 5.23E-06 4.13E-07 6.07E-06 5.24E-06 7.1E-06 2.64E-06 5.63E-06 2.21E-06 6.53E-06 5.52E-06 5.75E-06 2.03E-06 5.19E-06 8.55E-07 1.05E-07 4.87E-06 5.7E-06 1.79E-06 3.52E-06 7.45E-11 7.3E-11 7.08E-06
LncRNAs, long noncoding RNAs.
perpetuation of this disease (Berrih-Aknin et al., 2014; Berrih-Aknin and Le Panse, 2014; Le Panse and Berrih-Aknin, 2013). Here, we drew conclusions about lncRNA functions based on co-expressed gene relationships identified in GO and pathway analyses. In a comparison between MG patients and healthy control subjects, the most enriched GO terms in the predicted target genes of the lncRNAs were inflammatory
response, sequence-specific DNA binding transcription factor activity, nucleic acid binding transcription factor activity, response to lipid, leukocyte proliferation, regulation of inflammatory response, regulation of leukocyte proliferation and regulation of mononuclear cell proliferation. Many were associated with the immune pathology of MG. Therefore, these results provide significant new information as a foundation for further studies on MG. Using a bioinformatics approach, we predicted that the differentially expressed lncRNAs were likely to execute their functions by regulating gene expression in both a cis and trans fashion. Cis-regulation is identified as occurring when the transcription of an lncRNA affects the expression of its neighbor genes. By screening the co-expressed genes located near the differentially expressed lncRNAs, we discovered some possible cis-regulatory target genes. For instance, BCL2A1 might be the target of lncRNA ST20, which is upregulated by 2.64 fold in PBMCs of MG patients. BCL2A1 has been reported to control mitochondrial apoptosis, and its members exert critical cell type and differentiation stage-specific functions, acting as barriers against autoimmunity or transformation (Czabotar et al., 2014; Tait and Green, 2013). Bcl2 family members play important role in the maintenance of mature B-cell homeostasis (Sochalska et al., 2016). Therefore, we hypothesized that lncRNAs ST20 might be involved in the pathophysiology of MG by regulating BCL2A1 gene expression, in a cis fashion. LncRNAs can also act on their target genes through long range trans-regulation in conjunction with other TFs. For example, the lncRNA AL163636.6 can affect the transportation of RNASE4, thereby influencing the expression of RNASE4 target genes. In our study, to identify which TFs exert co-regulatory effects on differentially expressed lncRNAs, we overlapped the coding genes co-expressed with lncRNAs and the genes targeted by TFs. In summary, this study described the expression profile of lncRNAs in MG patients using a RNA microarray method. Bioinformatics approaches were used to predict the target genes and potential functions of the differentially expressed lncRNAs. These findings suggest that the differentially expressed lncRNAs may important in the process of MG and warrant further exploration to establish the specific molecular mechanisms and biological functions of these lncRNAs in the pathogenesis of MG. Funding sources This work was financially supported by the National Natural Science Foundation of China (81571600, 81322018, 81273287, and 81100887 to J. W. H.); the Youth Top-notch Talent Support Program; and the National Key Clinical Specialty Construction Project of China. Conflict of interest None. Acknowledgements We thank our patients for participating in this study. We also thank S. W. Zhang for technical support. References Berrih-Aknin, S., Le Panse, R., 2014. Myasthenia gravis: a comprehensive review of immune dysregulation and etiological mechanisms. J. Autoimmun. 52, 90–100. Berrih-Aknin, S., Frenkian-Cuvelier, M., Eymard, B., 2014. Diagnostic and clinical classification of autoimmune myasthenia gravis. J. Autoimmun. 48–49, 143–148. Cui, H., Xie, N., Tan, Z., Banerjee, S., Thannickal, V.J., Abraham, E., Liu, G., 2014. The human long noncoding RNA lnc-IL7R regulates the inflammatory response. Eur. J. Immunol. 44 (7), 2085–2095. Czabotar, P.E., Lessene, G., Strasser, A., Adams, J.M., 2014. Control of apoptosis by the BCL2 protein family: implications for physiology and therapy. Nat. Rev. Mol. Cell Biol. 15 (1), 49–63. Duarte, J.H., 2015. Connective tissue diseases: large intergenic noncoding RNA linked to disease activity and organ damage in SLE. Nat. Rev. Rheumatol. 11 (7), 384.
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Contents lists available at ScienceDirect
Journal of Neuroimmunology journal homepage: www.elsevier.com/locate/jneuroim
Expression profile of long noncoding RNAs and mRNAs in peripheral blood mononuclear cells from myasthenia gravis patients☆ Fang Zhang a,1, Guiyou Liu b,1, Yali Bu a, Xiaofeng Ma a, Junwei Hao a,⁎ a b
Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin 300052, China School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
a r t i c l e
i n f o
Article history: Received 24 June 2016 Received in revised form 3 September 2016 Accepted 8 September 2016 Available online xxxx Keywords: Long non-coding RNAs Messenger RNAs Myasthenia gravis Microarray
a b s t r a c t For the epigenetic characterization of myasthenia gravis (MG), we determined whether long noncoding RNAs (lncRNAs) and messenger RNAs (mRNAs) are expressed differentially in subjects with and without MG. Compared with healthy control subjects, the MG patients had 1561 upregulated lncRNAs, 1034 downregulated lncRNAs, 921 upregulated mRNAs, and 806 downregulated mRNAs (fold change N 2.0). Several GO terms including nucleic acid transcription factor activity, inflammatory response, regulation of leukocyte activation, lymphocyte proliferation and regulation of B cell proliferation were enriched in gene lists, suggesting a potential correlation with MG. Pathway analysis then demonstrated that cytokine-cytokine receptor interaction, intestinal immune network for lgA production, NOD-like receptor signaling pathway, NF-kappaB signaling pathway, cell adhesion molecules and TNF signaling pathway play important roles in MG. Co-expression network analysis indicated that 33 lncRNAs were predicted to have 31 cis-regulated target genes, and 65 lncRNAs appeared to regulate the patients' 45 trans target genes among differentially expressed lncRNAs. Our present study identified a subset of dysregulated lncRNAs and mRNAs in patients with MG, which may impact this disease process. © 2016 Published by Elsevier B.V.
1. Introduction Myasthenia gravis (MG) is a T and B cell-mediated autoimmune disease of the neuromuscular junctions (Lisak and Ragheb, 2012; Matsui et al., 2010; Thiruppathi et al., 2012). Muscle weakness and fatigue, as hallmarks of MG, resulting in the elicitation of an antibody-mediated autoimmune response against acetylcholine receptors (AChR) located at neuromuscular junctions. However, the epigenetic characteristics of this disease are not completely understood. LncRNAs, which are N200 nucleotides in length, represent a new class of noncoding RNAs (Necsulea et al., 2014). They contribute to a variety of biological cascades and are reported to be involved in neurodegenerative diseases (Wan et al., 2016), diabetic mellitus (Jae and Dimmeler, 2015; Wessel et al., 2015), autoimmune disease (Duarte,
☆ This work is independent of any sponsors; it was initiated and funded entirely by the investigators. This article has not been submitted for publication elsewhere. All authors have been informed of and approved this submission. We believe this study offers significant theoretical and practical input for practitioners in the field of epigenetics and will be of particular interest to your readership. ⁎ Corresponding author at: Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Anshan Road, Heping District, Tianjin 300052, China. E-mail address: [email protected] (J. Hao). 1 These authors contributed equally to this work.
http://dx.doi.org/10.1016/j.jneuroim.2016.09.005 0165-5728/© 2016 Published by Elsevier B.V.
2015; Xu et al., 2016a; Xu et al., 2016b; Zhang et al., 2016), cancer (Leucci et al., 2016; Schmitt and Chang, 2013), and cardiovascular diseases (Viereck et al., 2016). Noncoding RNAs are emerging as a new regulatory layer that affects both development of the immune system and its function (Huang et al., 2015; Ranzani and Rossetti, 2015; Wang et al., 2014). Although thousands of lncRNAs have been identified in the mammalian genome by bioinformatics analyses of transcriptomic data, their functional characterization is still largely incomplete. Recent studies show widespread changes in the expression of lncRNAs during activation of the innate immune response, particularly noted in T cell development, differentiation, and activation. These lncRNAs control important aspects of immunity such as production of inflammatory mediators, altering differentiation, and management of cell migration by regulating protein–protein interactions or by base pairing with RNA and DNA (Cui et al., 2014; Heward and Lindsay, 2014; Roux and Lindsay, 2015). Although several lncRNAs have been implicated in diverse processes and diseases, few examples of their ability to regulate autoimmune diseases have been described. In the present study, we performed an array of lncRNA and mRNA chip assays on peripheral blood mononuclear cells (PBMCs) from MG patients. Outstanding lncRNA functions were annotated based on co-expression genes and a gene ontology (GO) biological analysis process. The relationships among lncRNAs and mRNAs were revealed through cis and trans analyses. These results provide information for further studies of MG.
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2.3. RNA extraction and chip arrays
Table 1 Baseline characteristics.
Gender, M/F Age, years, median (range) Age at onset, years, median (range) Disease duration, median (range) Anti-AchR+ (%) Anti-Musk+ (%) Thymoma (%)
125
Control (n = 26)
MG (n = 48)
P value
8/18 37 (24–53) – – – – –
16/32 42 (27–62) 37 (21–59) 10.5 (0.8–17.5) 48/48 (100) 0 (0) 0 (0)
1.00 0.81 – – – – –
MG, myasthenia gravis.
2. Methods 2.1. Study population and trial design During an open enrollment, a total of 48 patients with type IV MG (Myasthenia Gravis Foundation of America, MGFA), who met the criteria of combined fluctuating muscle weakness with a positive neostigmine test, abnormal single-fiber EMG test, and positivity for AchR antibody, were recruited at Tianjin Medical University General Hospital from May 2013 to August 2015. These patients were within the peak timing of manifesting MG and before treatment with glucocorticoid or intravenous immune globulin. Exclusion criteria were the following: (Thiruppathi et al., 2012) co-presence of MG and other diseases of the immune system, (Matsui et al., 2010) presence of tumor(s) and systemic hematologic diseases, (Lisak and Ragheb, 2012) presence of recent infection, and (Necsulea et al., 2014) concomitant use of antineoplastic or immune-modulating therapies prior to blood sampling. We also recruited 20 age- and gender-matched healthy controls for the comparative study. The demographic and clinical features of all the patients and healthy controls are summarized in Table 1. The Tianjin Medical University General Hospital institutional review boards approved the trial protocol and supporting documentation. Informed consent was obtained at enrollment from all patients or legally acceptable surrogates.
2.2. Isolation of mononuclear cells from human peripheral blood (PBMCs) Peripheral blood anticoagulated by ethylene diamine tetraacetic acid (EDTA) was obtained from all MG patients and healthy controls. Human PBMCs were isolated with Ficoll-Hypaque gradients.
Total RNA was extracted from PBMCs using Trizol® reagent (Invitrogen, Grand Island, NY, USA). Approximately 200 ng of total RNA from each sample was used for the lncRNA microarray analyses. LncRNA expression was analyzed using OE_Biotech Human lncRNA chip software, containing 41,000 lncRNAs and 34,000 mRNAs. Those lncRNA and mRNA target sequences were merged from multiple databases: 23,898 from GENCODE/ENSEMBL; 14,353 from the Human LincRNA Catalog; 7760 from RefSeq; 5627 from USCS; 13,701 from ncRNA Expression Database (NRED); 21,488 from LNCipedia; 1038 from H-InvDB; 3019 from LncRNAs-a (Enhancer-like); 1053 from the Antisense ncRNA pipeline; 407 Hox ncRNAs; 962 UCRs; and 848 from the Chen Ruisheng lab (Institute of Biophysics, Chinese Academy of Sciences, Beijing, China). 4974 Agilent control probes. The lncRNA chip experiments were conducted at Capitalbio Corporation in Beijing, China. LncRNAs and mRNAs were analyzed using Cluster 3.0 software. The results were further analyzed using Tree View software. Green indicates low expression, and red indicates high expression in the output for these analyses. 2.4. Quantitative real-time PCR validation Total RNA was extracted from PBMCs with Trizol® reagent (Invitrogen) following the manufacturer's instructions. RNA quantity and quality were assessed using a Nanodrop ND-100 Spectrophotometer (Nanodrop Technologies, Wilmington, USA) and a 2100 Bioanalyzer (Agilent RNA 6000 Nano Kit, Waldbronn, Germany), with a 260:280 ratio of ≥ 1.5 and an RNA integrity number of ≥ 7 for the majority of the samples. For the reverse transcriptase (RT) reaction, SYBR Green RT reagents (Bio-Rad, Indianapolis, USA) were used. The lncRNA PCR results were quantified using the 2ΔΔct method against β-actin for normalization. The data represent the means of three experiments. 2.5. LncRNA co-expression analysis and gene function annotation Volcano plot filtering was used to identify lncRNAs and mRNAs with statistically significant differences in expression. Hierarchical clustering was applied to present the diacritical lncRNA and mRNA expression patterns among the samples. LncRNA classification was carried out to explore the potential function of the differentially expressed lncRNAs. GO analysis and pathway analyses were also performed to describe more fully the roles of the differentially expressed mRNAs. Furthermore,
Fig. 1. LncRNA and mRNA profiles of microarray data. Hierarchical clustering shows a distinguishable (A) lncRNA and (B) mRNA expression profile between the two groups. Plots here represent analysis of RNA extracted from PBMCs obtained from 6 MG patients and 5 healthy control subjects.
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3. Results 3.1. LncRNA and mRNA expression profiles in MG To determine the expression levels of lncRNAs associated with MG, we performed lncRNA and mRNA microarray analyses on the PBMCs of MG patients. These data were then compared to those obtained from the PBMCs of age- and gender-matched healthy controls. After separating signal from noise and performing t-tests, we observed up to twofold significant differences in lncRNAs and mRNA expression (P b 0.05). The lncRNA and mRNA expression data were clustered using Cluster 3.0 software and are plotted in Fig. 1. 3.2. Validation of disrupted lncRNA expression in MG Fig. 2. Comparison of lncRNA expression levels as determined by microarray and real-time PCR analyses. Three upregulated and three downregulated differentially expressed lncRNAs were validated by real-time PCR of RNA extracted from PBMCs from 48 MG patients and 20 healthy control subjects. Each sample was analyzed in triplicate. Column heights represent mean fold changes in expression of the MG group. Real-time PCR results are consistent with microarray data. ***P b 0.001: MG group versus healthy control group in real-time PCR validation.
a coding-non-coding gene co-expression network (CNC network) was drawn using Cytoscape, with Pearson coefficient N 0.98. The microarray analysis was performed by Capitalbio Corporation.
To verify the disruption of lncRNA expression in MG patients, we performed real-time PCR, which would determine the up- and downregulation of lncRNAs in each group. As shown in Fig. 2, differences in the expression of 6 lncRNAs were detected in MG patients compared with healthy control subjects. LncRNA XLOC 003810 was the most elevated (11.88-fold higher expression), followed by lncRNA XLOC 005780 (9.47-fold higher expression), and lncRNA ENSG00000259354.1 (8.38-fold higher expression). LncRNA XLOC 000734, lncRNA ATP6VOE2-AS1 and lncRNA ENSG00000250850.2 exhibited 5.37-, 7.68-, and 9.38-fold lower expression, respectively. These results were consistent with the results obtained from the microarray chip analyses.
2.6. Target prediction
3.3. LncRNA function annotation
For each lncRNA, we calculated the Pearson correlation of its expression value with that of each mRNA. The mRNAs that were co-expressed with the lncRNA were defined as having a Pearson coefficient that exceeded 0.98 and a P-value b0.05. We identified the mRNAs as cis-regulated target genes when (Thiruppathi et al., 2012) the mRNA loci were within a 10 kb window up- or downstream of the given lncRNA; and (Matsui et al., 2010) the Pearson correlation of lncRNA-mRNA expression was significant (r N 0.98 and P b 0.05). We then determined which mRNAs were likely to be trans-regulated by the lncRNA of interest. For each lncRNA, we calculated the overlap of the co-expressed mRNA set with transcriptional factor (TF) target genes and used hypergeometric distribution to calculate the significance of this overlap. When the mRNAs co-expressed with a given lncRNA significantly overlapped the target genes of a given TF, this TF presumably (or suggestively) interacted with the lncRNA, denoting that these mRNAs might be the trans-regulated target genes of that particular lncRNA.
To further explore the function of lncRNAs in MG, we subjected the results of the lncRNA and mRNA chip analyses to Pearson's correlation coefficient analysis, in which co-expression was considered at P N 0.98. LncRNA function was annotated using GO and KEGG pathway analyses. LncRNA ENSG00000267280.1 was associated with regulation of the inflammatory response, establishment of synaptic specificity at neuromuscular junction, positive regulation of cell migration, regulation of interleukin-1 beta production, and positive regulation of IkappaB kinase/NF-kappaB cascade (GO:0006954, GO:0007529, GO:0030335, GO:0032651, GO:0043123). LncRNA ENSG00000235138.1 was associated with cytokine activity, interleukin-5 receptor binding, inflammatory response, immune response, cytokine-mediated signaling pathway, positive regulation of B cell proliferation, and positive regulation of immunoglobulin secretion (GO:0005125, GO:0005137, GO:0006954, GO:0006955, GO:0019221, GO:0030890, GO:0051024). Selecting the reliability prediction terms
Fig. 3. (A). Top 30 GO terms for the difference in lncRNA co-expressed genes between MG patients and healthy control subjects. (B). KEGG pathways analysis. Top 30 pathways for the difference in lncRNA co-expressed genes between the MG patients and healthy control subjects.
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Fig. 4. LncRNAs' enrichment degree in different functions, such as biological process, cellular component and molecular function. The blue, green and red bars represent GO terms of biological process, cellular component and molecular function respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
(according to P-value and enrichment) produced a total of 30 enrichment GO terms (Fig. 3A). LncRNA KEGG pathways are listed in Fig. 3B, which included pathways for the intestinal immune network for IgA production, cytokine-cytokine receptor interaction, NF-kappaB signaling, NOD-like receptor signaling, cell adhesion molecules, TNF signaling and primary immunodeficiency. All these pathways are associated with immune and inflammatory responses. These results from the GO and KEGG pathway analyses confirmed the likelihood that lncRNAs are important players in such immune system functions as inflammation, immune response, cell differentiation, and proliferation (Fig. 4). 3.4. Cis analysis of the expression of lncRNAs and adjacent co-expression genes Evidence suggests that several lncRNAs regulate their own transcription in cis-regulatory fashion, as well as that of nearby genes, by recruiting remodeling factors to local chromatin. Prompted by this self-regulation evidence, we identified chromosomal co-expression genes 10 kbp upstream and downstream of the differentially expressed lncRNAs to determine potential lncRNA cis genes. Comparing MG patients to healthy controls, we found 31 cis genes (Table 2). 3.5. Trans mechanism of aberrant lncRNAs Currently, known trans regulation mechanisms involve factors that mediate chromatin regulation and transcription. We calculated the lncRNA co-expression genes of chromatin regulators and transcription factors (TF) in ENCODE to identify common genes involved in lncRNA regulation. Compared to healthy controls, in MG patients 45 trans genes were identified (Table 3). 4. Discussion In this study, we systematically screened the genome-wide expression pattern of lncRNAs as well as mRNAs in PBMCs obtained from MG patients (IV subtype, MGFA) and healthy controls. As a result, we identified 1561 upregulated and 1034 downregulated lncRNAs, and 921 upregulated and 806 downregulated mRNAs. Among them, 34 lncRNAs had a N6-fold difference, and 6 lncRNAs had a N 8-fold difference in expression level between the MG and control groups, suggesting the pivotal role of these lncRNAs in MG. Several candidate lncRNAs that
we identified were chosen for real-time PCR validation. Real-time PCR revealed the same direction of regulation in lncRNAs expression in MG and healthy controls. Therefore, our results from the real-time PCR analysis confirmed the microarray data to some extent. MG is traditionally regarded as an autoimmune antibody-mediated disorder of neuromuscular synaptic transmission whose complex pathogenesis is not well understood. Nevertheless, cytokines and chemokines may play a crucial role in the tissue damage and Table 2 “Cis” genes of aberrant lncRNAs. LncRNA
mRNA
Cis-regulation
Correlation
P value
p33678 p14386 p20717 p29417 p27434 p9911 p2099 p25807 p33842 p7144 p8336 p13834 p12967 p33413 p1630 p11734 p24849 p24849 p8846 p717 p26352 p17665 p8846 p13711 p8546 p26080 p30215 p778 p11275 p29405 p13399 p11144 p1657
BCL2A1 CLDN20 SELV LOC101928858 BCL11A AFF3 ITPRIP RNF214 ZNF322 ZNF594 ZNF573 LOC101927041 LOC101929911 RNASE4 DDIT4 SATB1-AS1 DKFZp667F0711 DKFZp667F0711 FCAR CHRM3-AS2 PSMG4 BTBD19 FCAR GTF2H2C_2 ZNF177 KIR3DX1 KIR3DX1 LOC284600 SRGAP3 CKMT2 SLC22A4 LOC102724900 LOC101929574
Sense Intergenic(10k) Intergenic(10k) Sense Antisense Bidirectional Antisense Intergenic(10k) Sense Intergenic(10k) Intergenic(10k) Sense Sense Sense Antisense Sense Sense Sense Intergenic(10k) Sense Sense Intergenic(10k) Intergenic(10k) Intergenic(10k) Intronic Sense Sense Sense Sense Antisense Antisense Sense Sense
0.988423097 0.958197566 0.969579552 0.976956168 0.983434175 0.977217313 0.984670267 0.988416613 0.958771416 0.950495739 0.963420815 0.975324743 0.974222322 0.975533386 0.995041452 0.965470243 0.985573234 0.984301318 0.98760845 0.991860409 0.956595064 0.965967646 0.989528739 0.974393426 0.950633518 0.973742323 0.962405174 0.981015774 0.992119054 0.968365167 0.977215819 0.967901851 0.987971836
1.11E-08 3.44E-06 8.37E-07 2.42E-07 5.54E-08 2.3E-07 3.92E-08 1.12E-08 3.24E-06 7.28E-06 1.9E-06 3.29E-07 4E-07 3.17E-07 2.47E-10 1.47E-06 2.98E-08 4.36E-08 1.51E-08 2.29E-09 4.07E-06 1.38E-06 7.09E-09 3.88E-07 7.19E-06 4.34E-07 2.15E-06 1.02E-07 1.98E-09 9.96E-07 2.3E-07 1.06E-06 1.32E-08
LncRNAs, long noncoding RNAs.
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Table 3 “Trans” mechanism of the aberrant lncRNAs. LncRNA
mRNA
Trans-regulation
Correlation
P value
p25234 p14884 p25234 p38187_v4 p37987_v4 p33704 p23505 p11081 p12139 p5400 p37036_v4 p1247 p8336 p15306 p2187 p4554 p19691 p10261 p99 p10261 p25234 p30177 p29036 p39396_v4 p1122 p10995 p21292 p106 p42164_v4 p25474 p8641 p39396_v4 p1076 p15933 p33415 p33414 p28307 p16958 p17624 p16958 p28526 p33413 p3949 p37690_v4 p39396_v4 p4074 p29405 p39396_v4 p39877_v4 p12560 p2878 p3643 p23670 p1247 p25940 p5543 p12682 p16288 p10845 p17770 p354 p28908 p33415 p33414 p41410_v4
C8orf44 C8orf44 TCAIM GINS2 FRA10AC1 FRA10AC1 BCKDHB EPHA10 USHBP1 USHBP1 ZFP14 ZFP14 ZFP14 ANKK1 ANKK1 CNR2 KLHDC4 MARVELD3 CLDN11 CLDN11 ZNF394 ZNF418 SCN2B TRAF5 TRAF5 PAQR8 NRIP3 MPV17L STK32C ZNF785 ZNF785 ZNF780B GEMIN2 GEMIN2 RNASE2 RNASE2 ZNF543 IAPP IAPP SEMA5A SEMA5A RNASE4 METTL21A METTL21A ZNF780B ZNF813 VSIG1 VSIG1 VSIG1 VSIG1 ZNF286B C21orf62 C21orf62 C21orf62 FAM178A ZNF234 ZNF345 ZNF345 DTWD2 RRP15 SSR1 ZNF550 RNASE2 RNASE2 ZNF417
miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration miRNA sequestration
0.979184076 0.967051673 0.950237997 0.955350047 0.969318114 −0.952529884 0.959270142 0.952529554 0.954738903 0.9513335 −0.957830902 0.966718485 0.961383732 0.950318927 −0.955676099 0.968166389 0.961728717 0.958409961 0.960502988 0.971992201 −0.96320495 0.953296975 0.957576275 0.953407368 0.95770186 0.951919235 −0.955885577 0.955756501 0.980183668 0.956998089 0.97744378 0.963352026 0.964345084 0.964600151 0.993152061 0.985149413 0.971432714 0.965658447 0.960150568 0.960495387 0.9646042 0.975533386 0.950844908 0.954049799 0.974040167 0.952496808 0.954029368 0.950775573 0.960626242 0.953291531 0.962158254 0.951695009 0.953493403 0.953057247 −0.96289371 −0.954129547 0.969430182 0.980898596 −0.954788502 0.953157303 −0.963927896 0.957974677 0.996203737 0.996220954 −0.950812641
1.54E-07 1.19E-06 7.45E-06 4.61E-06 8.69E-07 6.05E-06 3.07E-06 6.05E-06 4.9E-06 6.75E-06 3.58E-06 1.25E-06 2.42E-06 7.4E-06 4.46E-06 1.02E-06 2.32E-06 3.36E-06 2.67E-06 5.79E-07 1.95E-06 5.63E-06 3.67E-06 5.57E-06 3.63E-06 6.4E-06 4.37E-06 4.43E-06 1.23E-07 3.9E-06 2.2E-07 1.92E-06 1.7E-06 1.64E-06 1.05E-09 3.4E-08 6.32E-07 1.44E-06 2.78E-06 2.68E-06 1.64E-06 3.17E-07 7.06E-06 5.23E-06 4.13E-07 6.07E-06 5.24E-06 7.1E-06 2.64E-06 5.63E-06 2.21E-06 6.53E-06 5.52E-06 5.75E-06 2.03E-06 5.19E-06 8.55E-07 1.05E-07 4.87E-06 5.7E-06 1.79E-06 3.52E-06 7.45E-11 7.3E-11 7.08E-06
LncRNAs, long noncoding RNAs.
perpetuation of this disease (Berrih-Aknin et al., 2014; Berrih-Aknin and Le Panse, 2014; Le Panse and Berrih-Aknin, 2013). Here, we drew conclusions about lncRNA functions based on co-expressed gene relationships identified in GO and pathway analyses. In a comparison between MG patients and healthy control subjects, the most enriched GO terms in the predicted target genes of the lncRNAs were inflammatory
response, sequence-specific DNA binding transcription factor activity, nucleic acid binding transcription factor activity, response to lipid, leukocyte proliferation, regulation of inflammatory response, regulation of leukocyte proliferation and regulation of mononuclear cell proliferation. Many were associated with the immune pathology of MG. Therefore, these results provide significant new information as a foundation for further studies on MG. Using a bioinformatics approach, we predicted that the differentially expressed lncRNAs were likely to execute their functions by regulating gene expression in both a cis and trans fashion. Cis-regulation is identified as occurring when the transcription of an lncRNA affects the expression of its neighbor genes. By screening the co-expressed genes located near the differentially expressed lncRNAs, we discovered some possible cis-regulatory target genes. For instance, BCL2A1 might be the target of lncRNA ST20, which is upregulated by 2.64 fold in PBMCs of MG patients. BCL2A1 has been reported to control mitochondrial apoptosis, and its members exert critical cell type and differentiation stage-specific functions, acting as barriers against autoimmunity or transformation (Czabotar et al., 2014; Tait and Green, 2013). Bcl2 family members play important role in the maintenance of mature B-cell homeostasis (Sochalska et al., 2016). Therefore, we hypothesized that lncRNAs ST20 might be involved in the pathophysiology of MG by regulating BCL2A1 gene expression, in a cis fashion. LncRNAs can also act on their target genes through long range trans-regulation in conjunction with other TFs. For example, the lncRNA AL163636.6 can affect the transportation of RNASE4, thereby influencing the expression of RNASE4 target genes. In our study, to identify which TFs exert co-regulatory effects on differentially expressed lncRNAs, we overlapped the coding genes co-expressed with lncRNAs and the genes targeted by TFs. In summary, this study described the expression profile of lncRNAs in MG patients using a RNA microarray method. Bioinformatics approaches were used to predict the target genes and potential functions of the differentially expressed lncRNAs. These findings suggest that the differentially expressed lncRNAs may important in the process of MG and warrant further exploration to establish the specific molecular mechanisms and biological functions of these lncRNAs in the pathogenesis of MG. Funding sources This work was financially supported by the National Natural Science Foundation of China (81571600, 81322018, 81273287, and 81100887 to J. W. H.); the Youth Top-notch Talent Support Program; and the National Key Clinical Specialty Construction Project of China. Conflict of interest None. Acknowledgements We thank our patients for participating in this study. We also thank S. W. Zhang for technical support. References Berrih-Aknin, S., Le Panse, R., 2014. Myasthenia gravis: a comprehensive review of immune dysregulation and etiological mechanisms. J. Autoimmun. 52, 90–100. Berrih-Aknin, S., Frenkian-Cuvelier, M., Eymard, B., 2014. Diagnostic and clinical classification of autoimmune myasthenia gravis. J. Autoimmun. 48–49, 143–148. Cui, H., Xie, N., Tan, Z., Banerjee, S., Thannickal, V.J., Abraham, E., Liu, G., 2014. The human long noncoding RNA lnc-IL7R regulates the inflammatory response. Eur. J. Immunol. 44 (7), 2085–2095. Czabotar, P.E., Lessene, G., Strasser, A., Adams, J.M., 2014. Control of apoptosis by the BCL2 protein family: implications for physiology and therapy. Nat. Rev. Mol. Cell Biol. 15 (1), 49–63. Duarte, J.H., 2015. Connective tissue diseases: large intergenic noncoding RNA linked to disease activity and organ damage in SLE. Nat. Rev. Rheumatol. 11 (7), 384.
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