Involvement of lncRNA-1700040D17Rik in Th17 cell differentiation and the pathogenesis of EAE

International Immunopharmacology 47 (2017) 141–149

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Involvement of lncRNA-1700040D17Rik in Th17 cell differentiation and the pathogenesis of EAE Wei Guo, Wen Lei, Dongmei Yu, Yaoyao Ge, Yucong Chen, Wenyao Xue, Qianwen Li, Shuo Li, Xiangdong Gao ⁎, Wenbing Yao ⁎ Jiangsu Key Laboratory of Druggability of Biopharmaceuticals, School of Life Science and Technology, China Pharmaceutical University, Nanjing 210009, China

a r t i c l e

i n f o

Article history: Received 28 January 2017 Received in revised form 22 February 2017 Accepted 10 March 2017 Available online xxxx Keywords: Experimental autoimmune encephalomyelitis Th17 lncRNA RORγt IL23

a b s t r a c t IL-23/STAT3 signaling pathway is a key process in Th17 cell differentiation, and Th17 cells are closely related to the development of autoimmune diseases. We previously designed and prepared rhIL23R-CHR protein to antagonize endogenous IL-23, showing effectiveness in the treatment of experimental autoimmune encephalomyelitis (EAE) in mice. To further elucidate the mechanism of action, mouse lncRNA microarray was used to screen expression profiles of lncRNAs, and a particular lncRNA, 1700040D17Rik was found to down-regulate in EAE model and its expression was significantly increased after the treatment by rhIL23R-CHR. The function of 1700040D17Rik was revealed to associate with the differentiation of Th17 cells through the regulation of the key transcription factor RORγt. Together, regulation of Th17 cells through lncRNA is responsible for the effects of rhIL23R-CHR to balance the immune responses, and 1700040D17Rik has the potential to serve as a therapeutic target or a biomarker for autoimmune diseases. © 2016 Elsevier B.V. All rights reserved.

1. Introduction Autoimmune diseases affect more than 5% population worldwide, which is characterized by immune cell-mediated attack on the body organs [1,2]. The concept of autoimmune disease was proposed more than 100 years ago and over 80 autoimmune diseases have been identified [2]. Recently, the mechanistic study has identified significant roles of T and B cells that recognize foreign antigens in autoimmune diseases, resulting in self-reactive antibodies or deregulated T cell distribution [3]. The subsequent secretion of inflammatory cytokines further activates the immune cells through different signal transduction pathways in an autocrine fashion. In addition, the reactivity of organism and the expression profiles of genes that for posing the specific clinical pathology are controlled by thousands of transcription factors, cofactors, and chromatin regulators, acting at different levels from DNA, RNA to protein [3]. Therefore, specific signal transduction pathways activated by certain stimuli and large number of kinases involved in the downstream processes have been proposed for therapeutic intervention [4]. Among autoimmune diseases, multiple sclerosis is a prototypic chronic inflammatory disease affecting the central nervous system, featuring multifocal areas of demyelination, axonal damage, activation of glial cells, and immune cells such as Th1 cells and Th17 cells, infiltration in lesion [5]. Previous investigation has shown that the pathogenesis of inflammatory diseases depends on activated T cells interacting with ⁎ Corresponding authors. E-mail addresses: [email protected] (X. Gao), [email protected] (W. Yao).

http://dx.doi.org/10.1016/j.intimp.2017.03.014 1567-5769/© 2016 Elsevier B.V. All rights reserved.

resident cells in tissues or migratory inflammatory cells [6]. Meanwhile, IL-23 has been widely recognized as an important cytokine to maintain the differentiation of Th17 cells. In addition, RORγt is a specific transcription factor for Th17 cells, whose induction is in a STAT3-dependent manner, together with inflammatory cytokine (IL-6)-initiated signaling cascade and TGF-β signaling pathway [7]. During the past decade, the field of immunology has witnessed remarkable advances in the biology of Th17 cells, and the importance of IL-23/Th17 axis in the pathogenesis of MS has become clearer and concreter. To target IL-23 mediated Th17 cell differentiation, we previously constructed, expressed and purified a soluble IL-23 receptor cytokine-binding homology region (rhIL23RCHR) to neutralize IL-23 and inhibit both differentiation and abnormal function of human/murine Th17 cells. This recombinant protein could effectively ameliorate EAE through reducing the production Th17-polarized pro-inflammatory cytokines and suppressing inflammation and demyelination in the focused parts [8,9]. For many years, most focus in the field has been on protein regulators. However, recent evidence has pointed out vital roles of long noncoding RNAs (lncRNA) in the regulation of development, activation and homeostasis of the immune system [10,11,12]. lncRNA is a type of non-coding RNAs mostly transcribed by RNA polymerase II. They usually are more than 200 nucleotides in length with little protein coding potential [13]. Differing from traditional RNAs as a messenger between genes and proteins, the discovery of a large number of lncRNAs has provide an important new perspective on the centrality of RNA in gene regulation, emerging as a cryptic and critical player in the genetic regulatory code [14]. Up to date, the roles of lncRNAs in the immune

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system has been extensively studied, including the activation and differentiation of various immune cells, including T cells, B cells, DC cells, and NK cells [15,16]. With regard to T cells, Ranzani et al. [17] have identified that linc-MAF-4 can regulate MAF transcription through the recruitment of chromatin modifiers in a Th2-correrelated manner. Meanwhile, lincR-Ccr2-5′AS has been indicated as an essential component of a regulatory circuit in gene expression specific to Th2 cells to play important roles in the migration of Th2 cells to lungs [18]. Furthermore, IFNG-AS1 (NeST, TMEVPG1) and GATA3-AS1 have been known to exhibit selective expression under Th1 or Th2-polarizing conditions [19]. More importantly, the recent discovery of lncRNA Rmrp has shed the light to the direct correlation between lncRNA and Th17 cells. Nuclear Rmrp could act as a key DDX5-associated RNA to promote assembly and regulate the function of RORγt transcriptional complexes at a subset of critical genes specifically in the Th17 effector program [20]. High tissue- and stage-specificity is another characteristic for lncRNAs. LncRNAs are frequently expressed only in specific developmental stage, hinting to their possible involvement in a particular process of diseases. Aware of diverse functions of lncRNAs, it is essential to determine specific lncRNAs involved in the inflammatory diseases. As previously indicated, the differentiation and activation of both innate and adaptive immune cells are highly dependent on a coordinated set of transcriptional and post-transcriptional events [21]. On the other hand, more and more evidence has demonstrated that lncRNAs can exert their functions through different ways such as severing as scaffold or decoy activator. As a result, lncRNAs have the potential to exhibit their functions through synergistic regulation of the transcription factors and their subsequent processes. In the present study, we aimed to establish the relationship between the antagonistic effects of rhIL23R-CHR and the involvement of lncRNAs in EAE model. More importantly, we identified a specific lncRNA to associate with the blockage of IL-23 signaling pathway by profiling lncRNAs between EAE and normal mice. After blocking IL-23/Th17 axis with rhIL-23R-CHR, the functional roles of a particular lncRNA 1700040D17Rik was further confirmed in Th17 cells. 2. Materials and methods 2.1. Animals Eight- to twelve-week-old female C57BL/6 mice (Comparative Medicine Centre of Yangzhou) were used in all animal experiments. Mice were kept in a conventional, pathogen-free facility at the Medical School of Southeast University. All animal procedures were in accordance with the Guidelines for the Care and Use of Laboratory Animals as adopted and promulgated by the United States National Institutes of Health, and were approved by the Jiangsu Provincial Experimental Animal Manage Committee under the Contract SCXK 2012(su)-0004. 2.2. Generation of rhIL23R-CHR Purified rhIL23R-CHR used in this study was prepared based on our previous report [9]. The recombinant protein showed a purity of 99% by SDS-PAGE. All proteins were treated with Endotoxin affinity Resin (Genscript, USA) to remove the endotoxin before followed assays, the purified proteins were shown to have negligible endotoxin (b 10 EU/mg) by an LAL Chromogenic endotoxin quantization assay (Genscript, USA).

injected s.c. into each mouse at five different sites in the flank. EAE induction was performed using MOG35–55 immunization as indicated above plus 200 ng pertussis toxin in 200 μL PBS for each mouse via i.v. (tail vein), and equivalent PBS instead of MOG35–55was used as negative control. EAE models were administrated by i.v. with hIL23R-CHR 12 days after immunization just before first onset, then re-injected every two days. The pathological status of EAE was evaluated every day until being sacrificed and scored with a standard grading system as follows: grade 0, normal; grade 1, limp tail; grade 2, limp tail and hind limb weakness; grade 3, hind limb paralysis; grade 4, forelimbs paralysis; and grade 5, moribund or death. 2.4. RNA isolation and preparation of RNA microarray libraries Total RNA was extracted from mouse spleen using Trizol reagent (Intrivogen) according to the manufacturer's instructions. 2.5. RNA labeling and array hybridization Sample labeling and array hybridization were performed according to Agilent One-Color Microarray-Based Gene Expression Analysis protocol (Agilent Technology) with minor modifications. Briefly, mRNA was purified from total RNA after removal of rRNA (mRNA-ONLY™ Eukaryotic mRNA Isolation Kit, Epicentre). Then, each sample was amplified and transcribed into fluorescent cRNA along with the entire length of the transcripts without 3′ bias utilizing a random priming method (Arraystar Flash RNA Labeling Kit, Arraystar). The labeled cRNAs were purified by RNeasy Mini Kit (Qiagen). The concentration and specific activity of the labeled cRNAs (pmol Cy3/μgcRNA) were measured by NanoDrop ND-1000. 1 μg of each labeled cRNA was fragmented by adding 5 μL 10× Blocking Agent and 1 μL of 25× Fragmentation Buffer, then heated the mixture at 60 °C for 30 min, finally 25 μL 2× GE Hybridization buffer was added to dilute the labeled cRNA. 50 μL of hybridization solution was dispensed into the gasket slide and assembled to the LncRNA expression microarray slide. The slides were incubated for 17 h at 65 °C in an Agilent Hybridization Oven. The hybridized arrays were washed, fixed and scanned using Agilent DNA Microarray Scanner (part number G2505C). Arraystar Mouse LncRNA Microarray V3.0 was used for profiling the mouse LncRNAs and protein-coding transcripts. A total of 35,923 LncRNAs and 24,881 coding transcripts were detected by the third-generation LncRNA microarray. 2.6. Analysis of lncRNA microarray data Agilent Feature Extraction software (version 11.0.1.1) was used to analyze acquired array images. Quantile normalization and subsequent data processing were performed using GeneSpring GX v12.1 software package (Agilent Technologies). After quantile normalization of the raw data, lncRNAs and mRNAs that at least 3 out of 6 samples have flags in Present or Marginal (“All Targets Value”) were chosen for further data analysis. Differentially expressed lncRNAs and mRNAs with statistical significance between two groups were identified through pvalue/FDR filtering. Differentially expressed lncRNAs and mRNAs between two samples were identified through Fold Change filtering. Hierarchical Clustering and combined analysis were performed using homemade scripts. 2.7. Validation of differentially expressed genes by quantitative real-time PCR

2.3. EAE model induction and treatment by rhIL23R-CHR Myelin oligodendrocyte glycoprotein (MOG) peptide (MEVGWYRSPFSRVVHLYRNGK, MOG35–55) (GL Biochem, China) was dissolved in PBS at 2 mg/mL and emulsified in an equal volume of CFA consisting of IFA (Sigma-Aldrich) plus 4 mg/mL heat-inactivated Mycobacterium tuberculosis (strain H37 RA, Difco). 200 μL emulsions were

The expression of differentially expressed genes from RNA Sequencing was validated by qPCR using custom designed Primer Express (Table S3). From the list of top differentially expressed genes from different comparisons, 10 up- and 10 down-regulated genes were selected for their expression validation by Q-PCR in a subset of samples used for RNA Sequencing. Twenty lncRNA genes were validated. All cDNA were

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prepared with a mixture of random and oligo(dT) primers following the manufacturer's instructions. Real time PCR was performed with Super Master Mix (Applied biochemistry). For PCR amplification, following thermal cycle was used: 5 min at 95 °C; 40 × (95 °C for 40 s and 60 °C for 30 s). Expression of each lncRNA was estimated using the 2-ΔΔCT method. β-actin was used an reference gene. 2.8. Purification of immune cell subsets from the spleens mixed lymphocytes Spleens extracted from C57BL/6 mice were teased through sterilized 70 μm cell strainers (BD, USA) to obtain single-cell suspensions in IMDM containing 10% FBS medium (Invitrogen, USA). Red blood cells were lysed with RBC lysis buffer (eBioscience, USA). Lymphocytes and monocytes were separated based on forward and side scatter profiles and CD4+ T cells were purified by using anti-CD4 magnetic beads (Miltenyi biotech, Germany). For validating the purity of CD4+ T cells, mixed lymphocyte were stained with APC adjusted anti-mouse CD4 antibody (BD Biosciences).

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lymphocytes were seeded in 24 well plates at 5 × 105/well and cultured with beads coated with anti-mCD3 (BD Biosciences) along with antimCD28 antibody (eBioscience, USA) for specific activation for 36 h. Then, the cells were centrifuged at 1000 rpm for 10 min to exchange new medium in 100 μL and infected by adding 400 μL of supernatants and 8 μg/mL polybrene (Sigma, USA). After 24 h of the transduction, fresh medium was added, and cells were cultured for another 2 days under 10 ng/mL mIL-6 (R&D, USA), 10 ng/mL mIL-23 (R&D,USA) and 1 ng/mL mTGF-β(R&D,USA). After the first infection, the infection and polarization process were repeated twice to improve the infection efficiency. 2.12. Statistical analyses The two-tailed Student's t-test was used for two-group analyses. One-way ANOVA followed by post hoc Bonferroni test was performed for multiple group comparisons. Results are expressed as mean ± SD, and p value b 0.05 was considered statistically significant.

2.9. Differentiation of Th17 cells and identification

3. Results

For mouse Th17 cells differentiation, naïve CD4+ T cells were purified by using anti-CD4 magnetic beads (Miltenyi biotech, Germany). Naïve CD4+ T cells were seeded in 24 well plates at 5 × 105/well and stimulated with plate-bound anti-mCD3 (Miltenyi biotech, Germany) and soluble anti-mCD28 (1 μg/mL, eBioscience, USA) under Th17 polarizing conditions: (10 ng/mL mIL-6, R&D, USA, 1 ng/mL mTGF-β, R&D, USA and 10 ng/mL mIL-23, R&D, USA) for 72 h. For identification of differential level of Th17 cells, the supernatant was used to measure the cytokines with commercially available mouse IL-17A ELISA kits (Dakewe, China). And total RNA was extracted from cells pellets using Trizol reagent (Invitrogen) according to the manufacturer's instructions and the first-strand cDNA was synthesized using TransScript FirstStrand cDNA Synthesis SuperMix (Transgen Biotech, China). Then the gene expression of Th17 differentiation factors (RORγt and IL-17A) were determined by quantitative RT-PCR (qRT-PCR). The qRT-PCR was performed on an ABI Step one Plus Instrument (Applied biosystem, USA) using SYBRgreen Mix (Applied biosystem, USA) under standard thermocycler conditions.

3.1. Expression profiles of lncRNAs and mRNAs in EAE and normal mice

2.10. Construct overexpression

of

lentivirus

vector

pLVX-1700040D17Rik

for

Based on the Ensemble database, 1700040D17Rik gene was obtained by RT-PCR from mouse spleen cDNA library in EAE model using the Primers F1 and F2 (Table S4). After purification with Tian-quick midi purification kit (Tiangen, China), the contracted 1700040D17Rik gene was digested with EcoRI and XbaI (Takara, China) and then inserted into lentiviral expression vector pLVX-IRES-ZsGreen1 (Clontech, USA). E.coli DH5α (Novagen, Germany) was transformed with recombinant plasmid and the following selection was conducted on Luria-Bertani (LB) broth supplemented with 0.1 mg/mL ampicillin (Sigma, USA). After verification of correct sequences by restriction digestion and DNA sequencing (Genscript, USA), the plasmids for cell transfection including pLVX-1700040D17Rik, psPAX2 and PMD2G, were prepared using EndoFree Maxi Plasmid Kit (Tiangen, China) and then transfected into HEK293T cell line (ATCC, USA) at the logarithmic growth phage.

To explore the potential roles of lncRNAs in the progress of multiple sclerosis, CD4+ T cells were isolated from spleens of EAE and normal mice and used as the samples to obtain the expression profiles of lncRNAs and mRNAs with the mouse LncRNA Array v3.0 (8 × 60 K, Arraystar). A total of 27,398 lncRNAs were found by the microarray analysis. The hierarchical clustering analysis and the scatter plots showed the expression profile difference of lncRNAs between EAE and normal mice (Fig. 1A–C). Among 2090 differentially expressed lncRNAs, there were 1328 significantly up-regulated lncRNAs and 762 significantly down-regulated lncRNAs (fold change N2, p-value b 0.05, FDR b 0.05). According to relative location near the coding gene, the 2090 differentially expressed lncRNAs were classified into six classes: bidirectional lncRNAs, exon sense-overlapping lncRNAs, intron sense lncRNAs, intronic antisense lncRNAs, natural antisense lncRNAs and intergenic lncRNAs. Fig. 1D and Table S1 showed the percentage and absolute amount of differentially expressed lncRNAs in these subgroups. Meanwhile, according to chromosome location, the 2090 differentially expressed lncRNAs were further classified into 21 classes as shown in Fig. 1E. In addition to lncRNAs expression profiles, mRNAs expression profiles also were examined (Fig. 1A–C). 18,481 coding transcripts were found by the microarray analysis. The expression of 866 mRNAs were significant increased and 533 decreased (fold change N 2, p-value b 0.05, FDR b 0.05). For the differentially expressed mRNAs, Gene Ontology (GO) and pathway analysis were performed. GO enrichment of differentially expressed mRNAs could provide insight of the function of corresponding lncRNAs. With respect to the biological process, the most highly enriched GO targeting up-regulated transcripts were found to include the response to homeostasis of cells, immunological process, cellular regulation and single-organism process (Fig. 2A, B). KEGG pathway analysis was conducted to identify which gene networks are affected by aberrantly expressed lncRNAs, and the top 10 significant pathways were indicated in Fig. 2C and D, which are closely related to hematopoietic cell lineage, chemokine signaling pathway and MPAK signaling pathway.

2.11. Lentivirus production and transduction 293T cells seeded in 10 cm plate were transfected with 10 μg pLVX1700040D17Rik and pLVX-control using Lifectamine 2000 (Life Technologies), 2100 and 2500G (plus) (abm, Canada). At 72 h after the transfection, supernatants were collected and concentrated by ultrafiltration (Millipore, USA), then used for CD4+ T lymphocytes infection in the presence of 8 μg/mL polybrene (Sigma, USA). Naïve CD4+ T

3.2. Prediction of lncRNA function by constructing the lncRNA-mRNA co-expression network To further associate the differentially expressed lncRNAs with their potential biological functions, 20 up-regulated and 20 down-regulated inflammation related mRNAs were given from the combination of GO analysis and pathway analysis and then carried out with Coding and

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Fig. 1. Discovery of EAE-associated lncRNAs by microarray hybridization. CD4+ T cells were isolated from spleens of EAE (n = 3) and normal mice (n = 3) and tested with the mouse LncRNA Array v3.0. (A) Hierarchical clustering results of lncRNAs and mRNAs expression profiles between EAE models and normal groups. “Green” indicates low relative expression while “red” indicates high relative expression. (B) Scatter plots show the variation in lncRNAs and mRNAs expression between EAE models and normal groups. The values of X and Y axes are an average of normalized values in each group. (C) Volcano plots show significantly differentially expressed lncRNAs and mRNAs between EAE and normal groups. The values of X axe is log2 (fold change) and Y axe is -log10 (p-value). (D) Percentage of differentially expressed lncRNAs in the subgroups. (E) Chromosome distribution of differentially expressed lncRNAs. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Non-coding (CNC) analysis. Consequently, CNC analysis showed that total 958 lncRNAs were associated with the inflammation related mRNAs (Fig. 2E). Based on the criteria that the absolute Pearson correlation coefficient greater than 0.9978 and the FDR value less than 0.05, we finally obtained 150 lncRNAs that are associated with immune inflammatory processes, including 133 up-regulated and 17 down-regulated lncRNAs. Then 20 representative lncRNAs were found from the total 150 lncRNAs screened. 3.3. Identification of distinctive lncRNA by real-time quantitative PCR To confirm the microarray results, real-time quantitative PCR was carried out to examine 20 representative lncRNAs. These lncRNAs were validated in EAE model, normal and rhIL23R-CHR treatment

groups in mice. rhIL23R-CHR could effectively relieve the severity of illness and improve the clinical assessment of EAE (Fig. 3A, B). As we previously reported [8], rhIL23R-CHR could interact with IL-23 and suppress the development of Th17 cells in EAE mice. Therefore, the EAE model with rhIL23R-CHR treated mice were examined for the difference on lncRNA expression to identify specific lncRNAs possibly involved in the pathological process of multiple sclerosis, especially those related to the Th17/IL-23 axis. According to qRT-PCR data, six lncRNAs, AK014730, ENSMUST00000174173, 1700040D17Rik, uc008ixy.1, AK036595 and ENSMUST00000164587, showed significant difference on their expression levels in EAE model compared to normal mice, and rhIL23R-CHR treated mice exhibited a similar trend as normal mice (Fig. 3C). These lncRNA transcripts were subsequently selected for further studies as shown in Table S2.

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Fig. 2. Bioinformatic analysis of expressed lncRNAs and mRNAs. (A, B) Go analysis of differentially expressed mRNAs (biological process). A stands for up-regulated mRNAs, and B stands for down-regulated mRNAs. (C, D) Pathway analysis of differentially expressed mRNAs. C stands for up-regulated mRNAs, and D stands for down-regulated mRNAs. (E) Co-expression analysis of mRNAs (up: n = 20, down: n = 20; fold change N2, p-value b 0.05, FDR b 0.05) and lncRNAs. “Red” plots indicate up-regulated mRNAs related to inflammation; “Green” plots indicate down-regulated mRNAs related to inflammation; “Blue” plots indicate co-expressed lncRNAs. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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Fig. 4. Effects of 1700040D17Rik on the differentiation of Th17 cells. (A) Transfection efficacy under the optimal lentivirus packaging conditions. (B–C) Gene transduction efficacy of lentiviral vector in Th17 cells. Q-PCR for mRNA expression of ZsGreen (B) and 1700040D17Rik (C) in Th17 cells. (D) Q-PCR for mRNA expression of RORγt. (E) Cytokine production detected by ELISA. Control stands for normal Th17 differentiation; pLVX-1700040D17Rik stands for Th17 cells infected by lentivirus containing 1700040D17Rik. All tests were performed in triplicate and presented as the mean ± SD. *p b 0.05, **p b 0.01, ***p b 0.001.

3.4. Identification of a functional lncRNA affecting the differentiation of Th17 cells Among the above candidate lncRNAs, we were particularly interested in 1700040D17Rik because it was intriguingly down-regulated in EAE model whereas rhIL23R-CHR treatment could significantly increase the expression of this lncRNA (Fig. 3C). Bioinformatic analysis of 1700040D17Rik (Fig. S1A–C) indicated that 1700040D17Rik is a mouse lncRNA located near the RORγt gene and may influence RORγt

in a cis- fashion. Therefore, we selected 1700040D17Rik for further characterization of its role in the differentiation of Th17 cells and the pathogenesis of EAE. To clarify whether 1700040D17Rik is involved in Th17 cell differentiation, we used a lentiviral vector constructed by inserting 1700040D17Rik sequence, to overexpress 1700040D17Rik in Th17 cells. The optimal packaging conditions have been explored by our previous work. Transfection efficacy could be detected by fluorescence microscope and consequent infection efficiency could be quantified by FACS (Fig. S2). Fig. 4A showed the transfection efficacy

Fig. 3. Screening and validation of Th17-associated lncRNAs (A) Photographs of three group mice (normal: n = 5, EAE: n = 5, rhIL23R-CHR treatment: n = 5); a stands for normal mice; b stands for mice in EAE model; c stands for mice treated with rhIL23R-CHR; d the process of establishing the EAE model and treated by rhIL23R-CHR. (B) Mean clinical score of mice treated with rhIL23R-CHR (n = 5), vehicle (n = 5) and normal groups (n = 5). Graphs show mean ± SD. (C) Screening and validation of lncRNA expression in the group of normal (n = 3), EAE model (n = 3), and rhIL23R-CHR treatment (n = 3). The tests were performed in triplicate and presented as the mean ± SD. *p b 0.05, **p b 0.01, ***p b 0.001.

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under the optimal lentivirus packaging conditions. Subsequently, 1700040D17Rik was transfected into Th17 cells with lentivirus in the process of Th17 cells differentiation. Fig. 4B and Fig. 4C showed that lentivirus was capable of transfecting 1700040D17Rik with high efficiency. The result of q-PCR revealed that while 1700040D17Rik was overexpressed, the expression of transcription factor RORγt was significantly reduced (Fig. 4D). Additionally, the cytokine production of Th17 cells transfected with the lentivirus was determined to decrease in various degrees by ELISA (Fig. 4E). The collective data indicated that overexpression of 1700040D17Rik could affect the process of Th17 cell differentiation, suggesting that its biological functions could be associated with the differentiation of Th17cells and the regulation of T-cell mediated immune response. 4. Discussion The next generation of deep sequencing technologies has provided a powerful tool for the discovery of non-coding RNAs. The discovery of lncRNAs, a newly identified class of non-coding RNAs, has shown enormous momentum to the entire field of regulatory RNA. Recently, many lncRNAs with diverse and versatile functions have been discovered, gaining widespread attention in biomedical research [22]. Multiple sclerosis is an classic autoimmune, demyelinating neurodegenerative disorder, mostly affecting adults [23]. Thus far, several miRNAs associated with immune responses have been linked to this deadly disease, including miR-155, miR-29, miR-17-92, miR-132 and miR-326 [24]. However, the association between lncRNAs and multiple sclerosis is less understood. In the present study, we employed EAE model, the commonly used and well-characterized mouse model for multiple sclerosis, to examine the expression profiles of lncRNAs and mRNAs using microarray approach. Compared to normal mice, a total of 1328 up- and 762 downregulated lncRNAs were identified to be significantly expressed in EAE model, suggesting that lncRNAs might be relevant to the process and pathology of multiple sclerosis. At the same time, according to GO and pathway analyses, the differentially expressed mRNAs were revealed to link with immunological responses, regulation of biological process and hematopoietic cell lineage. lncRNAs, a class of emerging RNA molecules, can serve as important modulators in the development of autoimmunity and autoimmune diseases. Although Th17 cells are known to be potent inducers of tissue inflammation and have been implicated in the pathogenesis of many autoimmune diseases, little is known on the expression and function of lncRNAs in Th17cells. Hence, the present study has provided the expression pattern of lncRNAs from microarray screening in EAE to discover new functional lncRNAs. These lncRNAs and other coding genes might be associated with autoimmune diseases, and some of the lncRNAs could be closely linked to the signaling pathways in Th17 cells. Thus, according to GO and pathway analyses, we further investigated the expression patterns of lncRNAs and mRNAs, and then focused on the identification of inflammatory regulators related to Th17 cell differentiation, such as IL-23 and RORγt, in order to close the gap between mRNA expression and biological function of specific lncRNA with regard to their respective roles in Th17 cell differentiation and development. To ascertain our findings, we constructed a functional lncRNAmRNA regulatory network for multiple sclerosis based on the identified mRNAs and lncRNAs. It has been suggested that lncRNAs could affect transcription of proximal or distal genes via cis- and trans-acting mechanisms [25]. According to CNC network, the co-expressional relationship between differentially expressed lncRNAs and mRNAs was thoroughly investigated to examine the relationship between Th17 cell differentiation and specific lncRNAs. Finally, 150 lncRNAs were found to differentially express. Among them, 20 lncRNAs were preliminarily validated by qPCR to be down-regulated in spleen tissues of both normal mice and rhIL23R-CHR treatment. Ultimately, 6 lncRNAs showed remarkable correlation with autoimmunity and the signaling pathways of Th17 cells. Particularly, one down-regulated lncRNA,

named 1700040D17Rik, was found to be significantly suppressed in EAE model whereas its expressional level was greatly increased after rhIL23R-CHR treatment. Subsequent bioinformatic analysis indicated that 1700040D17Rik is a mouse intergenic lncRNA containing one intron and two exons without a polyA tail. According to its location at chromosome 3:94,409,395–94,412,919, this lncRNA may influence nearby RORγt gene in a cis- fashion. As indicated in previous studies, lncRNAs play a typical function of regulation. Down-regulation of lncRNA expression has been regarded as an instinctive feature of various diseases, such as cancer, autoimmune diseases and metabolic diseases [26,27]. To elucidate the roles of our newly discovered lncRNA-1700040D17Rik, we investigated its involvement in the differentiation of Th17 cells. Through the construction of a lentivirus vector to overexpress lncRNA-1700040D17Rik, we packaged the lentivirus and examined the transfection efficiency using HEK293T cells to confirm the correct expression of this lncRNA by sequencing. To optimize the transfection efficiency, we further compared different plasmids ratio and transfection reagents, resulting in the best efficiency of 85% in HEK293T cells. In the case of transfection to Th17 cells, multiple transfection was required to achieve the overexpression of lncRNA1700040D17Rik indicated by green florescence. After successful overexpression of lncRNA-1700040D17Rik in Th17 cells, significant inhibition of Th17cell differentiation was observed accompanying with the decrease of transcription factor-RORγt. In conclusion, the expression profiles of lncRNAs in EAE model were markedly different from normal mice, and such differences could be reversed by the treatment of rhIL23R-CHR. Comparison and quantification of the expression of lncRNAs and their mRNAs, a unique lncRNA1700040D17Rik was discovered to be associated with Th17 cell differentiation and consequent EAE development. Taking together, our findings suggested that lncRNA-1700040D17Rik might involve in the pathogenesis of multiple sclerosis through regulating the differentiation of Th17 cells. This newly discovered lncRNA could potentially serve as a therapeutic target or biomarker for the patients with multiple sclerosis. Competing interests The authors declare that they have no competing interests. Acknowledgments This work was supported by National Natural Science Foundation of China (81573444 and 81430082), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) and College Students Innovation Project for the R&D of Novel Drugs (J1310032). Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.intimp.2017.03.014. References [1] B. Hrdlickova, V. Kumar, K. Kanduri, D.V. Zhernakova, S. Tripathi, J. Karjalainen, R.J. Lund, Y. Li, U. Ullah, R. Modderman, et al., Expression profiles of long non-coding RNAs located in autoimmune disease-associated regions reveal immune cell-type specificity, Genome Med. 6 (2014) 88. [2] N.R. Rose, Prediction and prevention of autoimmune disease in the 21st century: a review and preview, Am. J. Epidemiol. 183 (2016) 403–406. [3] T.I. Lee, R.A. Young, Transcriptional regulation and its misregulation in disease, Cell 152 (2013) 1237–1251. [4] S. Carpenter, K.A. Fitzgerald, Transcription of inflammatory genes: long noncoding RNA and beyond, J. Interf. Cytokine Res. 35 (2015) 79–88. [5] L. Legroux, N. Arbour, Multiple sclerosis and T lymphocytes: an entangled story, J. NeuroImmune Pharmacol. 10 (2015) 528–546. [6] C.B. Schmidt-Weber, M. Akdis, C.A. Akdis, TH17 cells in the big picture of immunology, J. Allergy Clin. Immunol. 120 (2007) 247–254.

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