LncRNA‐miRNA network analysis across the Th17 cell line reveals biomarker potency of lncRNA NEAT1 and KCNQ1OT1 in multiple sclerosis

Abstract Differentiation of CD4+ T cells into Th17 cells is an important factor in the onset and progression of multiple sclerosis (MS) and Th17/Treg imbalance. Little is known about the role of lncRNAs in the differentiation of CD4+ cells from Th17 cells. This study aimed to analyse the lncRNA‐miRNAs network involved in MS disease and its role in the differentiation of Th17 cells. The lncRNAs in Th17 differentiation were obtained from GSE66261 using the GEO datasets. Differential expression of lncRNAs in Th17 primary cells compared to Th17 effector cells was investigated by RNA‐seq analysis. Next, the most highlighted lncRNAs in autoimmune diseases were downloaded from the lncRNAs disease database, and the most critical miRNA was extracted by literature search. Then, the lncRNA‐miRNA interaction was achieved by the Starbase database, and the ceRNA network was designed by Cytoscape. Finally, using the CytoHubba application, two hub lncRNAs with the most interactions with miRNAs were identified by the MCODE plug‐in. The expression level of genes was measured by qPCR, and the plasma level of cytokines was analysed by ELISA kits. The results showed an increase in the expression of NEAT1, KCNQ1OT1 and RORC and a decrease in the expression of FOXP3. In plasma, an upregulation of IL17 and a downregulation of TGFB inflammatory cytokines were detected. The dysregulated expression of these genes could be attributed to relapsing‐remitting MS (RR‐MS) patients and help us understand MS pathogenesis better.


| INTRODUC TI ON
Th17 cells followed by RORC promotion. Therefore, regulation of FOXP3 and RORC expression would also affect the Th17/Treg balance. 4 However, the biomolecule that could affect the expression of FOXP3 and RORC and thus disrupt the Th17/Treg balance at MS is still unknown. Long non-coding RNA (lncRNA) is a class of noncoding RNA that modulates biological activities such as altering histone and chromatin structure, altering splicing profiles, suppressing microRNA (miRNA) binding sites and regulating gene expression. 5 The identification of aberrant expression of lncRNAs in the CNS and immune system, and its involvement in T cells and B cells differentiation and activation, make it one of the golden topics in the study of biomarker evaluation in neurological and autoimmune diseases, such as MS. 6,7 One of the most important lncRNAs is lincRNA-Cox2, which regulates macrophages, and some lncRNAs expressed by T cells coincide with NRON, GAS5 and LincR-Ccr2-5'AS. 8,9 GAS5 are an essential suppressor of T-cell proliferation and is associated with cell cycle suppression in response to stress or other environmental conditions; GAS5 also regulates glucocorticoid receptor expression. 10 The upregulation of BACE1-AS in Alzheimer's disease triggers an increase in BACE1 protein levels by binding and stabilizing BACE1 mRNA. 11 Despite ample evidence for the involvement of lncRNAs in disease pathogenicity, especially in human autoimmunity, its role in the Th17 /Treg imbalance at MS remains controversial and should be investigated, which is essential for the development of targeted therapies. CD 4+ T helper cells (Th), which are inflammatory and autoreactive that play an important role in the pathogenesis of MS. 12 Recent studies have shown that lncRNAs are involved in regulating the function of CD 4+ cells such as Th17/Treg. However, it is not yet known whether abnormal lncRNAs indirectly control FOXP3 and RORC levels during the development of MS, resulting in Th17/ Treg imbalance. 13 This study aimed to evaluate the association of lncRNAs with Th17/Treg imbalance in MS. Therefore, using a combination of bioinformatics and literature review methods, we gathered a list of genes differentially expressed in MS compared to controls.
The lncRNAs that are likely to be dysregulated in this pathway were determined. Next, confirmation assays such as real-time PCR (RT-PCR) and ELISA were completed to confirm our claim.

| Screening of dysregulated lncRNAs in Th17 differentiation
The lncRNA expression profile in T helper 17-cell differentiation associated with RNA-seq (GSE66261) was taken from the GEO database (https://www.ncbi.nlm.nih.gov/geo/), which serves as a public repository for curated gene expression datasets and original series and platform records. 14 We then used the lncRNA disease database (http://www.cuilab.cn/LncRN Adisease) to select the lncRNAs that were validated in autoimmune diseases. 15 All microRNAs involved in Th17 differentiation in various autoimmune diseases were found using a literature review on Th17 differentiation and microRNA in MS, rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), inflammatory bowel disease (IBD) and diabetes. 16 Subsequently, the Starbase database (http://starb ase.sysu.edu.cn/) was used to determine the interaction between lncRNA-miRNAs involved in Th17 cell differentiation and autoimmune diseases. 17 The lncRNA-miRNA network was designed using Cytoscape. Finally, using the CytoHubba application, two lncRNA hubs that have to react most strongly with miRNAs in differentiation were identified by the MCODE method. 18  and PBMC was extracted. PBMC was extracted using the Ficoll-Hypaque density gradient centrifugation protocol. The blood was diluted 1:1 with PBS and then carefully placed in Falcon tubes containing 2 mL of Ficoll-Hypaque solution. It was then centrifuged at 956 g for 20 minutes. After centrifugation, the solution contained three different phases, the supernatant was frozen at −80°C, the PBMC obtained was washed again with PBS at 680 g for 10 minutes, and then the RNA was extracted from the cell pellet.

| Total RNA extraction and cDNA synthesis
Extraction of total RNA was performed using the triazole method according to the protocol of the RNX-PLUS kit (Sinaclon).
Quantification of extracted RNAs was evaluated using Nanodrop (Thermo Scientific™), and RNA quality was assessed by electrophoresis on 2% agarose gels. The cDNA synthesis kit (TAKARA) was used to convert RNA cDNA, using an equal volume of RNA for all samples.

| Primer design and real-time PCR
Primer design is the most important factor in qPCR; a variety of software has been used for primer design such as primer3, primer BLAST, gene runner and oligo analyser. Among the common genes as internal control, the β-actin gene was selected for this study because this gene has relatively stable expression in peripheral blood mononucleosis cells and is also suitable for normalizing the expres-  Table 1.

| Plasma extraction
Whole blood samples (2 mL) were collected in (blood collection tubes) ethylenediaminetetraacetic acid (EDTA) anticoagulation tubes. The tubes were immediately centrifuged (1000g/min for 10 minutes) at 4°C, and plasma was collected. Subsequently, all separated plasma samples were stored at −80°C in RNase-free microcentrifuge tubes.

| Enzyme-linked immunosorbent assay
The presence of inflammatory cytokines such as TGFβ and interleukin-17 (IL-17) in the plasma samples was determined by enzymelinked immunosorbent assay (ELISA), using the Karmania ELISA kit according to the manufacturer's instructions (Karmania Pars Gene). Briefly, 100 µL of serum was loaded on 96 well plates precoated with IFNγ and IL-4 capture antibodies. After washing, HRPstreptavidin labelled detection antibodies were added. Finally, the TMB/peroxide (ELISA substrate) was added to the reaction, and the OD was recorded at 450 nm.

| Statistical analysis
For the analysis of the real-time PCR data, preliminary tests were performed using the CtΔΔ method. To select the type of statistical test for statistical analysis, the Shapiro-Wilk test was first used to examine the normal distribution of the data. p values of ≤0.05 were considered as normal distribution and above that as abnormal distribution. The Mann-Whitney U test was used to compare gene expression in the patient and control groups for abnormal data and Student's t-test for normal data. Depending on the distribution of the data, data were defined as mean and standard deviation (SD), median and interquartile range (IQR), or number (per cent). The relationship between lncRNAs and genes expression was examined using Spearman's rank correlation to determine the degree of correlation between the variables. The efficiency of lncRNAs and genes in MS diagnosis was assessed using receiver operating characteristic curves (ROC) the area under the ROC curve (AUC) and the 95% confidence interval (CI). For the above analyses, we used SPSS 17.0 and GraphPad Prism 5.0. Statistical significance was described as a p value of less than 0.05.

| Patient characteristics and their relation to genes expression in RR-MS patients
The demographic data, sex, age, smoking, infections and family autoimmune history of 25 RR-MS patients and 25 healthy controls are summarized in Table 2. Statistical analyses showed no significant differences between age and smoking in the sample group, but there was a significant association between the expression of NEAT1

| Identification of Th17-specific lncRNAs and miRNAs differentially expressed
We All previously discussed differentially expressed biomolecules were used to construct the dysregulated network.

| Screening of DE-lncRNAs in primary and effector Th17 cells
To investigate the expression profile of lncRNAs in primary and effector Th17 cells, we retrieved the GSE66261 dataset from the NCBI Gene Expression Omnibus (GEO). Differential expression analysis between primary and effector Th17 cells was performed using DESeq2 (18). Genes with p adjusted <0.05 were considered significantly differentially expressed genes (DEGs). With a total of 51109 differentially expressed lncRNAs, we identified 48 DE lncRNAs between primary and effector Th17 cells and miRNA-lncRNA constructs in autoimmune diseases (Figure 2). A heat map was also created to visualize the expression patterns of lncRNAs in different samples ( Figure 3).

| Analysis of DElnRNA-DEmiRNA network demonstrates top-five deregulated lncRNAs
The lncRNA-miRNAs interaction was predicted by the Starbase database. Finally, the DElncRNA-DEmiRNA network was created ( Figure 4). The DElncRNA-DEmiRNA network, which consists of 91 nodes, and 320 edges, was reconstructed and visualized using Cytoscape. The DEmiRNA and DElnRNA with high interaction levels were identified as hub nodes in the DElncRNA-DEmiRNA regulatory network. The five highly interacting lncRNA included KCNQ1OT1, NEAT1, MALAT1, TUG1 and PVT1 (Table 3).

| Upregulation of lncRNA NEAT1 and KCNQ1OT1
is strongly correlated with RR-MS patients

| Cytokines assay demonstrates altered expression of TGF-β and IL-17 cytokines as critical indicators of Th17/Treg dysregulation in RR-MS
TGFβ and interleukin-17 (IL-17) were measured in a plasma sample from MS patients and healthy controls using the ELISA method.
The result showed that IL-17 had an upregulation (p = 0.03) and downregulation of TGFβ (p = 0.03) in RR-MS compared to controls. These observations represent an imbalance in Th17/Treg cells. (Figure 7).

| ROC analysis for lncRNAs and genes as clarified putative biomarker characterization in RR-MS patients
The diagnostic value of NEAT1 for RR-MS was assessed using a ROC  (Figure 8).

| DISCUSS ION
MS is a neurodegenerative and autoimmune disorder that results in damage to the central nervous system. The disease progresses rapidly without drug treatment. However, no conclusive cause for the development of MS has yet been found, and no early biomarkers for the disease have been discovered. 19 Recently, it was discovered that lncRNAs control immune responses and inflammation in MS, suggesting that they may be the missing piece in the puzzle of MS etiopathology. 20 Therefore, many efforts have been made in recent years to discover the MS molecular mechanism and function of ncR-NAs underlying disease progression. 21 Extensive data analysis has clarified many coding and non-coding loci associated with MS demyelination, neuroinflammatory lesions and exocytopenia. Increased  was observed in patients with RR-MS compared to healthy controls, which is consistent with other studies. 28,29 This leads to a loss of homeostasis between immune cells and the occurrence of inflammation in the nervous system. 27 Our study also shows that there is a significant association between reduced FOXP3 gene expression and a positive family history of autoimmune disease (p = 0.03).
In 2016, a study of different variants of FOXP3 found a significant difference in the distribution of the rs3761548 and rs2232365 alleles in MS patients compared to controls, suggesting that this polymorphism leads to suppression of FOXP3. 30 The study found that 24% of MS patients who had recently been exposed to a viral disease is also involved in the regulation of innate immune responses and non-specific defence mechanisms in MS, and this lncRNA acts as a positive regulator of the inflammatory response. 42 We used primers targeting the 5′ region of NEAT1 in this analysis, allowing us to detect both NEAT1 isoforms. According to the research by Zhang et al.

NEAT1 expression increases in response to viral infections such as
HIV-1, influenza virus and herpes simplex virus, which upregulate antiviral genes such as interleukin 8. In these subjects, IL-8 levels were significantly higher than in those who were not diagnosed with a neurological or autoimmune disease. Consequently, once the inflammatory process has begun, NEAT1 could accelerate the pathogenesis of MS by influencing macrophages and attracting them to the site of inflammation. 43 The upregulation of NEAT1 was positively correlated with Th1-associated TNF-a and Th17-associated IL-17 to increase susceptibility to MS. 44 Shui et al. 45 reported increased expression of NEAT1 in RA and showed that suppression of NEAT1reduced Th17 differentiation. Upregulation of expression was also observed in our study, which is consistent with other studies. 41 Dastmalchi et al 46  TNF-a were suppressed. 47 The expression level of NEAT1 in female patients was found to be higher than in affected male patients, suggesting that NEAT1 acts differently in the female immune system than in the male. The increase in NEAT1 expression suggests a coincidence with the onset of RR-MS and greater disease severity in women. 46 In our study, a significant association was found between NEAT1 expression in families with a positive history of the disease, such that NEAT1 expression was increased in individuals with a family background of autoimmune disease. Furthermore, based on the results, this study found that NEAT1 could be effective in the possible diagnosis of the disease with 72% specificity and sensitivity.
Increased NEAT1 expression likely leads to suppression of Treg cells through H3K4me3 methylation in FOXP3. 48 According to a report on inflammatory bowel disease, increased NEAT1 expression is in- There is a significant difference in KCNQ1OT1 expression in serum samples from asthmatic children with airway remodelling compared with those without airway remodelling. 54   Therefore, in the future, these data can be used to predict and develop effective drugs that inhibit the differentiation of pathogenic Th17 subtypes in autoimmune diseases, thereby alleviating or modifying these diseases. Finally, this study is the first to simultaneously investigate the expression profile of this gene set on MS as a molecular mechanism involved in the imbalance between Th17 and Treg.

| CON CLUS IONS
F I G U R E 8 ROC curve analysis assessing indicated that these genes have relatively suitable sensitivity and specificity in MS diagnosis. The AUC for lncNEAT1 was 0.72.9 (A), KCNQ1OT1 was 1 (B), RORC was 0.75 (C) and FOXP3 was 0.70 (D) It suggests that KCNQ1OT1 and NEAT1 play an essential role in the development of the intercellular imbalance between Th17 and Treg and that these lncRNAs can be used for pharmacological, therapeutic and diagnostic purposes.

ACK N OWLED G EM ENTS
We thank all of the patients and their families for taking part in this research and the Abu Raihan MS Center Hospital and Hormozgan molecular medicine research centre.

CO N FLI C T O F I NTE R E S T
The authors declare that they have no conflict of interest.

DATA AVA I L A B I L I T Y S TAT E M E N T
The data that support the findings of this study are available from the corresponding author upon reasonable request.