miR-141 and miR-200a are involved in Th17 cell differentiation by negatively regulating RARB expression

doi:10.21203/rs.3.rs-110540/v1

Download PDF

Research

miR-141 and miR-200a are involved in Th17 cell differentiation by negatively regulating RARB expression

https://doi.org/10.21203/rs.3.rs-110540/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background: Among T helper (Th) lineages differentiated from naïve CD4⁺ T cells, interleukin (IL)-17-producing Th17 cells are highly correlated with the pathogenesis of autoimmune disorders. Although a series of microRNAs (miRs) that modulate autoimmunity progress have been reported, further studies on microRNAs particularly involved in Th17 lineage commitment are still warranted.

Results: In this study, to clarify the involvement of miR-141-3p and miR-200a-3p in Th17 cell differentiation, as well as to explore their potential target gene involved, purified human naïve CD4⁺ T cells were cultured under Th17 cell polarizing condition. Differentiation process was confirmed applying ELISA method to measure IL-17 secretion and RT-qPCR to assess Th17 cell-defining genes expression during differentiation period. miR-141 and miR-200a expression pattern as well as expression alteration of their predicted downstream genes, which were identified via consensus and integration in-silico approach, was then evaluated by RT-qPCR. Finally direct interaction between both microRNAs and their common predicted target sequence was approved by dual-luciferase reporter assay. Highly increased IL-17 secretion and Th17 lineage-specific genes expression confirmed Th17 cell differentiation.

Conclusions: Results demonstrated that miR-141 and miR-200a were Th17 cell-associated microRNAs and their expression levels upregulated significantly during Th17 cell induction. We also found that retinoic acid receptor beta (RARB) gene, whose product has been reported as a negative regulator of Th17 cell generation, was a direct target of both microRNAs and its downregulation could affect the transcriptional level of JAK/STAT pathway genes. Overall, our results introduced two novel Th17 lineage-associated microRNAs and provided a rationale to further clarify the RARB-dependent mechanism of miR-141 and miR-200a to promote Th17 cell differentiation and hence Th17-mediated autoimmunity.

General Cell Biology & Physiology

Autoimmune diseases

MicroRNAs

miR-141-3p

miR-200a-3p

Human Th17 differentiation

RARB

Autoimmune diseases are a class of more than 80 chronic, often debilitating and, in some cases, life-threatening illnesses. For reasons not yet understood, the incidence of autoimmunity is growing around the world and thousands of people worldwide are being diagnosed with autoimmune disorders each year. Some ascribe this surge to environmental factors such as exposure to bacterial or viral pathogens and others believe that it stems from genetic deficiencies. Whatever the cause, CD4⁺ T cell–mediated autoimmunity is one of the most important aspects of autoimmune disease pathogenesis.

After antigen stimulation, naïve CD4⁺ T cells differentiate into several subsets of effector helper cells with distinct functions [1]. T helper (Th) 17 cells have been identified as a proinflammatory Th cell lineage that plays important role in host defense and is involved in various autoimmune and inflammatory diseases mainly by secreting interleukin (IL)-17 [2]. IL-6 in conjunction with IL-1ß initiates Th17 cell differentiation, whilst IL-23 serves as a pivotal factor in the continuous differentiation as well as recruitment of Th17 cells to sites of inflammation upon binding to its receptor (IL23R) [3]. Th17 differentiation is directed by retinoic acid-related orphan receptor (ROR) C gene product called RORγt as Th17 lineage-defining transcription factor, and is controlled by delicate balanced activity of a set of positive and negative regulators [4].

Retinoic acid receptor (RAR) ß is a transcription factor that contributes negatively to Th17 differentiation process [5]. Retinoic acid (RA) which influences various immune cell fates and a range of immunological functions, needs to bind to heterodimers of nuclear receptors (RARα, RARß and RARγ) prior to its function [6, 7]. It has been shown that in vivo RA treatment suppresses Th17-mediated immunopathology in various autoimmune disease models, including experimental autoimmune encephalomyelitis (EAE), rheumatoid arthritis and type 1 diabetes [8–10]. Although the past few years have seen substantial progress in defining the inhibitory function of RARs particularly RARα in autoimmunity, RARB gene expression pattern, as well as the possible underlying mechanism of its expression alterations during Th17 cell differentiation are largely unknown and need to be clarified.

MicroRNAs (miRs) constitute a large family of small, approximately 22-nucleotide-long, noncoding RNAs that have emerged as key post transcriptional regulators of gene. By base pairing to complementary sequences on untranslated region (UTR) of target mRNAs, microRNAs mediate translational repression, mRNA degradation and destabilization [11]. In immune system, it has been found that microRNAs can play a role as critical regulators of T cell differentiation, so that deregulated expression of microRNAs leads to development of autoimmune disorders [12–14]. Therefore, microRNAs can potentially serve as good diagnostic markers or even therapeutic targets. Regarding the role of microRNAs in Th17 lineage induction, Cobb et al. reported that IL-17A production from Th17 cells was reduced in cells with conditional deletion of Dicer, a RNase III enzyme which is indispensable for the maturation of microRNAs [15]. Furthermore, miR-155, miR-181c, miR-21 and miR-326 have been shown to promote Th17 differentiation [16–19]. The miR-200 family consists of five members classified as two functional groups based on the similarity of their seed sequences. Two functional groups only differ one nucleotide in the seed region (AAUACUG for miR-200b/200c/429 and AACACUG for miR-141/200a) [20]. Microarray analyses of transfected cells and target prediction algorithms reveal a high degree of similarity in target sequences of miR-141-3p and miR-200a-3p, suggesting that these two microRNAs may target a large number of common genes to improve the efficiency of gene regulation [21]. Studies on miR-141 and miR-200a have shown that deregulation of these microRNAs have been correlated with the development of different autoimmune inflammatory diseases such as psoriasis, systemic lupus erythematosus (SLE) and inflammatory bowel disease (IBD) [22–24].

Through a systematic literature mining of studies related to microRNA expression profiling in different immune system-related disorders, a list of microRNAs which had been reported to be deregulated in autoimmune diseases was prepared by our previous study [25]. Expression levels of some of listed microRNAs were then measured in CD4⁺ T lymphocytes of patients with autoimmunity [26, 27]. Although according to increase in the percentage of RORγt⁺ CD4⁺ T cells as well as upregulation of miR-141-3p and miR-200a-3p in CD4⁺ T cells of multiple sclerosis (MS) patients we suggested that miR-141 and miR-200a could be key microRNAs in Th17 cell induction, whether and how these microRNAs participate in Th17 differentiation have not been elucidated.

In present study, in order to extend our knowledge of the involvement of miR-141-3p and miR-200a-3p in Th17 cell differentiation, we aimed to screen for changes in both microRNAs expression level over human Th17 cell induction. Furthermore, based on our in-silico experiments, we planned to evaluate expression pattern and direct interaction of RARB, as potential target gene of miR-141 and miR-200a. Finally, we intended to detect expression changes of downstream genes of RARB to explore the plausible mechanism of miR-141 and miR-200a to promote Th17 cell polarization and maybe Th17-mediated inflammatory and autoimmune diseases.

1. miR-141-3p and miR-200a-3p target analysis

To clarify how miR-141-3p and miR-200a-3p may involve in Th17 cells differentiation, we applied three microRNA target prediction algorithms TargetScan, miRDB and microT-CDS with the aim of identification of the potential downstream target of both microRNAs (Supplementary Excel file sheet 1 and 2). In that context, by common-target screening with strict thresholds mentioned above in methods and using Venn diagrams, we found RARB as the only common target gene which is regulated by miR-141 and miR-200a (Fig. 1A and B). Applying miRTarBase and TarBase, we also realized that the interaction between these microRNAs and RARB gene had not been validated before through strong experimental tests including RT-qPCR, reporter assay and western blotting. The potential matching positions of miR-141-3p and miR-200a-3p within the 3′UTR of RARB are depicted in Fig. 1C. Interaction information of each microRNA-RARB interaction demonstrates that RARB is one of the highest ranking predicted genes with context + + score = 0.39, miRDB score = 97 and miTG score ~ 0.98 (Fig. 1D).

2. Molecular signaling pathway enrichment analysis of RARB

Molecular signaling pathway enrichment analysis was conducted for further evaluating our in-silico prediction approach. Statistically meaningful pathways associated with RARB were acknowledged using three online databases, Reactome, KEGG and BioPlanet (Table 1). Functional analysis of RARB indicated that this gene is mainly enriched in pathways correlated with cancer and development and interestingly in immune respond- and Th17 differentiation- related pathways (RXR/VDR, TGFß, calcium and retinoic acid signaling pathways).

Table 1

**Signaling pathways relevant to RARB gene obtained from various databases**. Selected genes appear to be play roles in many key signaling pathways concerned cancer as well as Th17 differentiation.
Database	Pathway	p value
Reactome	Signaling by retinoic acid Nuclear receptor transcription pathway Activation of HOX genes Developmental biology Generic transcription pathway	0.00210 0.00255 0.00445 0.03930 0.04060
KEGG	Non-small cell lung cancer Small cell lung cancer Gastric cancer Pathways in cancer TGFß signaling pathway Calcium signaling pathway	0.00330 0.00465 0.00745 0.02650 0.03988 0.04877
BioPlanet	RXR/VDR pathway Retinoic acid receptor-mediated signaling Nuclear receptors in lipid metabolism Vitamin A and carotenoid metabolism Non-small cell lung cancer Small cell lung cancer Pathways in cancer Generic transcription pathway	0.00138 0.00150 0.00170 0.00195 0.00270 0.00420 0.01625 0.01885

3. Interaction analysis of RARB

For a higher confidence in our predicted target gene, we also explored the interaction between RARB and genes closely associated with Th17 differentiation or autoimmunity via STRING database and visualized the results using cytoscape software (Supplementary Excel file sheet 3). Interestingly, as illustrated in Fig. 2, there are significant interactions between RARB and SIRT1 (from autoimmunity-related genes) and AKT1, EP300, RXRA, FOXO3, CREM, VDR, RARG and RARA (from Th17-related genes). The combined schematic KEGG signaling pathways involved in Th17 differentiation or immune response are indicated in supplementary Fig. 1 in which pathways and genes associated with RARB are indicated with red stars. Collectively, RARB was taken into account as putative target gene.

4. Naïve CD4⁺T cell purification and Th17 cell differentiation

Naïve CD4⁺ T cells were isolated from human peripheral blood sample and more than 65% purity was confirmed via flow cytometric analysis of surface proteins stained with CD45RA and CD4 antibodies (Fig. 3A and B). Purified naïve cells were then cultured under Th17 cells differentiation condition for six days. Within the first 48 h of differentiation, cells were attracted to the bound anti-CD3, causing creation of the Th17 lineage in the presence of appropriate cytokines and additional antibodies. Therefore positive indications of Th17 cells differentiation including visible clumping of cells around the coated antibody and increased signs of proliferation could be seen in all cultured wells (Fig. 3C). To confirm the efficiency of Th17 differentiation process, transcriptional levels of master markers of Th17 cells including IL-17, IL-23R and RORC genes were measured by RT-qPCR and the protein level of IL-17 was evaluated using ELISA method on day 0, 2, 4 and 6 of differentiation. All assessed gene expressions displayed constant increase over Th17 differentiation period. To be precise, based on real-time PCR analysis, the expression levels of IL-17, IL-23R and RORC mRNAs on day 6 reached 17.25 (P = 0.0102), 2.99 (P = 0.0052) and 6.85 (p = 0.0082) folds higher than on day 0 respectively (Fig. 3D-F). Moreover, there was a substantial increase in IL-17 secretion during Th17 cell polarization, reaching a peak of 905 pg/ml on day 6 (Fig. 3G).

5. Upregulation of miR-141 and miR-200a during human Th17 cell differentiation

In order to explore the participation of miR-141-3p and miR-200a-3p in Th17 cell induction, we evaluated changes in both microRNAs expression over Th17 differentiation. Quantitative analysis of the microRNA expression applying RT-qPCR revealed that miR-141 and miR-200a were significantly overexpressed during differentiation of human naïve CD4⁺ T cells into Th17 lineage. Both microRNAs showed consistent expression alteration pattern in that their expression levels were constantly upregulated until reaching their highest level on day 6 compared to day 0 by mean factors of 8.23 (P = 0.0077) and 9.11 (P = 0.0053) for miR-141 and miR-200a respectively (Fig. 4A and B).

6. Confirmation of RARB as a direct target gene of miR-141 and miR-200a

To evaluate our hypothesis that RARB is a downstream target of miR-141-3p and miR-200a-3p, we used reporter assays in HEK 293T cells with luciferase report vectors that contained the putative wild type or mutant microRNA binding sites within the RARB-3´UTR (Fig. 5A). Both microRNAs inhibited the luciferase activity of the reporter containing the wild type RARB-3´UTR whereas luciferase activity did not change in cells transfected with control vacant pBud-EGFP vectors in which microRNA precursor had not been ligated. However, neither of pBud-EGFP vectors influenced the luciferase activity of the reporter with a mutant 3´UTR that was unable to bind to microRNAs (Fig. 5B and C). Because of the ability of microRNAs to silence genes by mRNA destabilization, we next researched the impact of both microRNAs on the levels of RARB mRNA. For this purpose, we measured RARB expression on day 0, 2, 4 and 6 of Th17 cells induction using RT-qPCR. Interestingly, the RARB showed an opposite changing trend compared to that of microRNAs. Our statistical analysis confirmed markedly lower expression of RARB on days 2, 4 and 6 of differentiation in the way that its transcriptional level dropped around 70, 90 and 95 percent in comparison with day 0 respectively (Fig. 5D).

7. Downregulation of downstream genes of RARB

To further understanding of possible pathway affected by miR-141 and miR-200a in Th17 differentiation, we investigated the expression level of STAT6 and GATA3 as downstream genes of RARB. These genes were chosen based on Zhu et al. study which stated binding of RARß to IL4Ra promoter contributes negatively to Th17 differentiation [5]. By KEGG pathway analysis, we found STAT6 and GATA3 as downstream genes of IL4Ra in JAK/STAT signaling pathway (data not shown). Interestingly, expression level of STAT6 and GATA3 displayed similar pattern to RARB in which their transcriptional level approximately halved until day 6 of differentiation. (P = 0.0040 for STAT6 and P = 0.0053 for GATA3) (Fig. 6A and B).

Inappropriate mobilization of Th17 cells against a person’s own tissues occurs in almost all autoimmune disorders. Th17 lineage is a subpopulation of T helper cells which is differentiated from naïve CD4⁺ T cells and secretes proinflammatory cytokines [28]. These cells have high abilities to stimulate acute and chronic inflammation and several studies confirmed their pivotal role in pathogenesis of human autoimmune diseases [29–31]. Since it is revealed that microRNAs are pivotal controllers of autoimmunity, over recent years, increasing attention has focused on finding and nominating different microRNAs involved in Th17 differentiation with the aim of introducing novel biomarkers or even therapeutic targets to the world of autoimmune disorders.

As only mere studies have assessed human Th17 differentiation, here, naïve CD4⁺ T cells isolated from human peripheral blood were cultured under Th17 polarizing condition in order to directly test the association of miR-141 and miR-200a with human Th17 lineage commitment. IL-17 is one of the most specific cytokines of Th17 cells expressing RORγt as their master transcription factor [2, 4]. In humans, microarray analysis of MS plaques demonstrated increased level of IL-17 mRNA compared with the healthy controls [32]. Like MS patients, enhanced expression of IL-17 has been detected in inflamed mucosa from patients with IBD and in synovial fluid from patients with rheumatoid arthritis [33, 34]. It has been also approved that IL-23 took a prominent role in Th17 cell function, as IL-23-deficient mice was reported to lack IL-17-producing T cells and to be resistant to induction of EAE [28]. For evidence mentioned, noticeable increase in IL-17 protein secretion along with elevated mRNA level of RORC, IL23R and IL-17A were considered as strong confirmation of human Th17 differentiation in our study.

In current study, we focused on miR-141 and miR-200a, since these microRNAs have been proved to contribute to the development of some autoimmune disorders [22–24, 26, 27]. We discovered that human Th17 cell differentiation involves miR-141-3p and miR-200a-3p so that the mRNA level of both microRNAs was constantly upregulated during six-day differentiation of Th17 cells from human naïve CD4⁺ T cells. Several studies evaluated miR-141 and miR-200a expression levels in PBMCs, B cells, serum and/or other tissues of autoimmune patients to explored the correlation between these microRNAs with autoimmunity. In 2011, Olaru et al. demonstrated the significant increase of both microRNAs in IBD-dysplasia [35]. Also a study on brain tissue of EAE mice displayed miR-141 overexpression suggesting the potential role of this microRNA in MS [36]. In 2018, two separate studies by our colleagues showed upregulation of miR-141 and miR-200a in MPP+-treated differentiated PC12 cells as a model of Parkinson's disease [37, 38]. In our previous study it has been also revealed that both microRNAs expression level and frequency of Th17 cells were higher in MS patients compared to healthy groups [27]. Despite the lines of evidence described above, direct involvement of miR-141 and miR-200a in human Th17 differentiation as well as the plausible mechanism behind their contribution has not been established. We report in this study, to our knowledge for the first time, the involvement of miR-141and mir-200a in human Th17 cell differentiation as our RT-qPCR analysis clearly shows dramatic increase in both microRNAs expression over Th17 cell induction until they reached peak level on day 6 of differentiation. There are a few number of studies reporting reduction of miR-141 and/or miR-200a in B lymphocytes, PBMCs, serum and urine of autoimmune patients [23, 39, 40]. Considering the fact that the participation and expression alteration of both microRNAs were directly investigated during Th17 differentiation process in this study, we can vindicate our results.

Generally, microRNAs are thought to function by targeting 3´UTRs of genes, which leads to mRNA degradation or inhibition of mRNA translation [11].We documented in the present study that RARB, a negative regulator of Th17 generation, is a potential common target of both microRNAs. The immune system is profoundly influenced by a range of effects mediated by RA, not only for the development of lymphoid tissues but also for the functional fate of different immune cells [7, 41]. By promoting regulatory T cell (Treg) differentiation and suppressing Th17 cell generation, RA and its receptors including RARα, RARß and RARγ play significant roles in maintaining the balance between inflammatory and tolerogenic responses [42]. In 2007, Mucida et al. reported that generation of Treg cells, which are in charge of suppressing deleterious activities of Th cells, was inhibited by RAR antagonists [42]. Conversely, in another study, diet supplementation of RA led to increased differentiation of Tregs and amelioration of inflammation in a mouse model of Crohn’s disease [43]. Th17 differentiation drastically reduced in mouse models of EAE and arthritis upon RA or RA agonist treatment [8, 44]. RA counteracts the Th17 differentiation mainly via its suppressive effect on IL-6R expression. This results in less IL-23R expression, which further negatively influences maturation and stabilization of Th17 cells [45]. As a retinoic acid receptor, RARß doubtlessly involves in many cellular processes, including immune responses. Remarkable impact of RARß on the inhibition of Th17 differentiation was identified in Zhu et al. study [5]. They reported that Il-4 plus RA-treated inflammatory dendritic cells markedly reduced the secretion of proinflammatory cytokines and strongly suppressed Th17 differentiation. Mechanistically, RARß mediate these effects through close interaction with IL4Ra that is also a negative controller of Th17 differentiation and inducing aldehyde dehydrogenase family 1, subfamily A2 (Aldh1a2) which is a rate-limiting enzyme for RA synthesis. In fact, only a few studies provided preliminary clues that RARß is a participant in the Th17 cell inhibition. However, no further studies have been performed to reveal the RARB expression profile and its potential regulating factors during Th17 polarization. Our bioinformatics analysis applying prediction algorithms suggested that RARB might be a downstream target of miR-141-3p and miR-200a-3p. To indicate direct interaction between our microRNAs and RARB-3´UTR, we performed a luciferase assay. In support of dual luciferase assay results, quantitative analysis of RARB mRNA level revealed its significant decrease during Th17 differentiation process. Therefore, our study examined the previously untouched area of the participation of miR-141 and miR-200a in Th17 differentiation by targeting RARB gene.

While characterization of the mechanisms behind these findings is out of the scope of this study, we examined STAT6 and GATA3 expression level during six-day differentiation process in order to explore possible pathway affected by miR-141 and miR-200a to promote Th17 differentiation. Zhu et al. study specified RARß binding to the Il4Ra promoter which based on our in-silico research could cause high expression of STAT6 and GATA3 genes in JAK/STAT pathway. This leads ultimately to the suppression of RORC expression and therefore Th17 differentiation. Interestingly, our RT-qPCR results illustrated downregulation of STAT6 and GATA3 over Th17 induction. Considering the fact that STAT6 and GATA3 are master regulators of Th2 cells, lessened expression level of RARB and then STAT6 and GATA3 may help explain the probable mechanism of our microRNAs to take part in Th17 induction.

In conclusion, by differentiation of human naïve T cells into Th17 cells and using RT-qPCR, we indicated that expression level of miR-141 and mir-200a increased over Th17 differentiation process and therefore we were able to nominate two novel microRNAs involved in Th17 generation. In addition, our target prediction and luciferase reporter assay results revealed that these microRNAs interfere directly with a negative regulator of Th17 differentiation, RARß. Finally, based on declined expression level of STAT6 and GATA3, we proposed that miR-141 and miR-200a could involve in Th17 differentiation by affecting JAK/STAT signaling pathway. Collectively, the present work is a preliminary study on the identification of two deregulated microRNAs and understanding of their underlying molecular mechanisms involved in Th17 cell differentiation, which could be indispensable for developing novel strategies aimed at diagnosing or even treating Th17-mediated autoimmune diseases. However, in-vitro and in-vivo studies are still required for further confirmation.

MicroRNA target analysis

Three microRNA target prediction databases including TargetScan (release 7.2), miRDB (release 6.0) and microT-CDS (release 5.0) were used to identify potential miR-141-3p and miR-200a-3p target sites in genomic sequences [46–48]. Moreover, 115 genes strongly correlated with Th17 cell differentiation identified through literature mining in our earlier study [49]. The common predicted targets for both microRNAs from Th17-related gene list, TargetScan with a total context + + score ≤ − 0.3, miRDB with a score ≥ 90 and microT-CDS with a miTG score ≥ 0.9 were retrieved using VENNY 2.1 tool [50]. These strict thresholds were chosen to achieve high-confidence target genes and reduce the incidence of false positives. Experimentally validation of predicted microRNA-mRNA interactions was also checked from miRTarBase (release 7.0) and TarBase (release 8.0) [51, 52]

Molecular Signaling Pathway Enrichment Analysis

Reactome, KEGG and BioPlanet were employed to obtain a comprehensive view of statistically significant signaling pathways and molecular networks related via predicted target gene (p value < 0.05) [53–55].

Interaction Network Construction

For further evaluating our candidate target gene, we also constructed a gene-based network which included two sets of 55 genes highly associated with autoimmune diseases (gda score > 0.2) and 115 genes strongly involved in Th17 cell differentiation obtained from DisGeNET database (release 7.0) and our earlier study respectively [49, 56]. Interactions of candidate target gene with the genes of both groups were assessed by STRING-db and visualized by Cytoscape software (release 3.8.0) [57, 58].

CD4⁺ naïve T cell purification

Human peripheral blood mononuclear T cells (hPBMCs) were separated from 20 mL freshly collected, heparinized peripheral blood via Ficoll– Hypaque density gradient centrifugation (Sigma-Aldrich, USA). Purification of naïve CD4⁺ T cells was performed using magnetic cell sorting system (MACS) according to Naïve CD4⁺ T Cell Isolation Kit II (Miltenyi Biotec, Germany) instruction. Briefly, non-naïve T cells were magnetically labeled with a cocktail of biotin-conjugated antibodies and anti-biotin microbeads. Isolation of pure naïve CD4⁺ T cells was achieved by depletion of magnetically labeled non-target cells.

Flow Cytometry

Purification analysis of isolated naïve CD4⁺ T cells was carried out using flow cytometry. After single cell preparation, FITC-conjugated antiCD4 and PE-conjugated anti-CD45RA antibodies (all antibodies were purchased from eBioscience, USA) were added to cell suspension and resulting mixture incubated for 30 min at 4 °C. Flow cytometry analysis was performed by Canto II (BD FACS Calibur) and analyzed using BD CellQuest Pro software.

Th17 Cell Differentiation

For Th17 cell differentiation, the CellXVivo Human Th17 Cell Differentiation Kit (R&D Systems, USA) was used. Purified naïve CD4⁺ T cells were cultured at a density of 2 × 10⁵ cell per well in 12-well plates with beads coated with anti-CD3 and containing X-VIVO™ 15, Serum-free hematopoietic cell medium (Lonza, Switzerland), under Th17 cell polarizing condition prepared according to the kit manufacturer’s instruction. Cells were maintained in a humidified incubator containing 5% CO2 at 37 °C for six days and differentiation media were refreshed every other day.

Enzyme linked immunosorbent assay (ELISA)

Th17 cells differentiation was confirmed using Quantikine ELISA Human IL-17 Immunoassay kit (R&D, USA) according to the manufacturer’s protocols. IL-17A content of supernatant culture media was measured 5 hours, 2 days, 4 days, and 6 days after induction of Th17 differentiation. Results were read by microplate reader (ELX 800) at 450 nm in duplicate.

RNA extraction, cDNA synthesis and real-time quantitative PCR (RT-qPCR)

Cells were lysed by TRIzol (Invitrogen, USA) for RNA isolation on day 0, 2, 4 and 6 of Th17 differentiation process. The RNA quality and quantity was determined via a NanoDrop 2000/2000c spectrophotometer (Thermo Fisher Scientific, USA) and the 260/280 nm absorbance ratio was evaluated. In addition, to eliminate any potential genomic DNA contamination, the total RNA samples were treated with DNase I (Fermentas, USA). For gene expression analysis, one microgram of total RNA from each sample was reverse-transcribed using cDNA Synthesis Kit (TaKaRa, Japan) and examined employing SYBR Green Master Mix (TaKaRa, Japan) and specific primer sets synthesized by Macrogen Company, South Korea (Table 2). To test miR-141 and miR-200a expression, 100 ng total RNA from each sample was performed using a miR-CURY LNA Universal RT microRNA PCR kit (Exiqon, Denmark). Relative expression of mRNAs and microRNAs was evaluated by the 2^−ΔΔCt method on ABI PRISM 7500 instrument (Applied Biosystems, USA) and normalized to the expression of glyceraldehyde 3-phosphate dehydrogenase (GAPDH) and U6 small nuclear RNA as internal references respectively.

Table 2

RT-qPCR primer pairs.
Gene Name	Forward/Reverse	Primer Sequence	Amplicon Size (bp)
RARB	Forward Reverse	5´TACAAACCCTGCTTCGTC 3´ 5´ACACAGTTCTTATCTCGGTGA 3´	141
IL17A	Forward Reverse	5` CATTGGTGTCACTGCTACTGC 3` 5` AAGGTGAGGTGGATCGGTTG 3`	201
RORC	Forward Reverse	5` TGCCAGAATGACCAGATTGTGCTT 3` 5` GAACAGCTCCATGCCACCGTA 3`	132
IL23R	Forward Reverse	5` CACATGGAATTCTGGGCTAACAG 3` 5` AGCAAAGACGATCATTCCCAAT 3`	111
GAPDH	Forward Reverse	5` CCACTCCTCCACCTTTGACG 3` 5` CCACCACCCTGTTGCTGTAG 3`	107

Plasmid Construction And Dual-luciferase Reporter Assay

To specify direct interaction between microRNAs and 3´UTR of predicted target gene, sequenced purified PCR products of microRNA precursor were digested with XbaI and HindIII and ligated into the pBud-EGFP vector (Promega, USA). Synthesized wild type (wt) and mutant (mut) target gene–3´UTR were also cloned into the psiCHECK2 vector (Promega, USA). Both vectors were then co-transfected into HEK293T cells using Lipofectamine 2000 reagent (Invitrogen, USA) following the instructions. Cells were harvested at 48 h post-transfection, assayed for the firefly and Renilla luciferase activity using the Dual-Luciferase Reporter Assay System (Promega, USA). Each experiment was repeated in triplicate and changes in Renilla luciferase activity were calculated relative to firefly luciferase enzyme activity (internal control).

Statistical analysis

The results are expressed as the means ± standard error of the mean (SEM) of three independent experiments and analyzed by and GraphPad Prism 8 software. Independent sample t tests were used to evaluate the differences between groups. A P value of 0.05 or less was considered significant.

Ethics approval and consent to participate

The protocol of study to use human samples was confirmed by both the Bioethics Committee of University of Isfahan and ROYAN institute review board under the bioethical code number: IR.ACECR.ROYAN.REC.1396.111.

Consent for publication

Not applicable.

Availability of data and materials

The data and materials that support the findings of this study are available from the corresponding author upon reasonable request.

Competing interests

The authors declares that there is no competing interests regarding the publication of this article.

Funding

This project was supported by the National Institute for Medical Research Development

(NIMAD’s project no. 942792).

Authors' contributions

I. B. and M. B. designed the experiments, drafted sections of the manuscript, and performed in silico study, cell culture and real-time PCR analyses. M. P. performed ﬂow cytometry. A. J. and K. G. were responsible for the supervision of project, wrote the manuscript and approved the ﬁnal version of manuscript. All authors read and approved the ﬁnal version of manuscript.

Acknowledgments

We would like to show our gratitude to our colleagues especially Autoimmune Research Team at Royan Institute for Biotechnology for their assistance and helpful comments.

Abbas AK, Murphy KM, Sher A: Functional diversity of helper T lymphocytes. Nature 1996, 383(6603):787-793.
Harrington LE, Hatton RD, Mangan PR, Turner H, Murphy TL, Murphy KM, Weaver CT: Interleukin 17–producing CD4+ effector T cells develop via a lineage distinct from the T helper type 1 and 2 lineages. Nature immunology 2005, 6(11):1123-1132.
Veldhoen M, Hocking RJ, Atkins CJ, Locksley RM, Stockinger B: TGFβ in the context of an inflammatory cytokine milieu supports de novo differentiation of IL-17-producing T cells. Immunity 2006, 24(2):179-189.
Ivanov II, McKenzie BS, Zhou L, Tadokoro CE, Lepelley A, Lafaille JJ, Cua DJ, Littman DR: The orphan nuclear receptor RORγt directs the differentiation program of proinflammatory IL-17+ T helper cells. Cell 2006, 126(6):1121-1133.
Zhu B, Buttrick T, Bassil R, Zhu C, Olah M, Wu C, Xiao S, Orent W, Elyaman W, Khoury SJ: IL-4 and retinoic acid synergistically induce regulatory dendritic cells expressing Aldh1a2. The Journal of Immunology 2013, 191(6):3139-3151.
Ghyselinck NB, Dupe V, Dierich A, Messaddeq N, Garnier J, Rochette-Egly C, Chambon P, Mark M: Role of the retinoic acid receptor beta (RARbeta) during mouse development. The International journal of developmental biology 1997, 41(3):425.
Hall JA, Cannons JL, Grainger JR, Dos Santos LM, Hand TW, Naik S, Wohlfert EA, Chou DB, Oldenhove G, Robinson M: Essential role for retinoic acid in the promotion of CD4+ T cell effector responses via retinoic acid receptor alpha. Immunity 2011, 34(3):435-447.
Kwok S-K, Park M-K, Cho M-L, Oh H-J, Park E-M, Lee D-G, Lee J, Kim H-Y, Park S-H: Retinoic acid attenuates rheumatoid inflammation in mice. The Journal of Immunology 2012, 189(2):1062-1071.
Massacesi L, Castigli E, Vergelli M, Olivotto J, Abbamondi A, Sarlo F, Amaducci L: Immunosuppressive activity of 13-cis-retinoic acid and prevention of experimental autoimmune encephalomyelitis in rats. The Journal of clinical investigation 1991, 88(4):1331-1337.
Van Y-H, Lee W-H, Ortiz S, Lee M-H, Qin H-J, Liu C-P: All-trans retinoic acid inhibits type 1 diabetes by T regulatory (Treg)-dependent suppression of interferon-γ–producing T-cells without affecting Th17 cells. Diabetes 2009, 58(1):146-155.
Wu L, Belasco JG: Let me count the ways: mechanisms of gene regulation by miRNAs and siRNAs. Molecular cell 2008, 29(1):1-7.
Li Q-J, Chau J, Ebert PJ, Sylvester G, Min H, Liu G, Braich R, Manoharan M, Soutschek J, Skare P: miR-181a is an intrinsic modulator of T cell sensitivity and selection. Cell 2007, 129(1):147-161.
O'Connell RM, Kahn D, Gibson WS, Round JL, Scholz RL, Chaudhuri AA, Kahn ME, Rao DS, Baltimore D: MicroRNA-155 promotes autoimmune inflammation by enhancing inflammatory T cell development. Immunity 2010, 33(4):607-619.
Rodriguez A, Vigorito E, Clare S, Warren MV, Couttet P, Soond DR, Van Dongen S, Grocock RJ, Das PP, Miska EA: Requirement of bic/microRNA-155 for normal immune function. Science 2007, 316(5824):608-611.
Cobb BS, Nesterova TB, Thompson E, Hertweck A, O'Connor E, Godwin J, Wilson CB, Brockdorff N, Fisher AG, Smale ST: T cell lineage choice and differentiation in the absence of the RNase III enzyme Dicer. The Journal of experimental medicine 2005, 201(9):1367-1373.
Du C, Liu C, Kang J, Zhao G, Ye Z, Huang S, Li Z, Wu Z, Pei G: MicroRNA miR-326 regulates T H-17 differentiation and is associated with the pathogenesis of multiple sclerosis. Nature immunology 2009, 10(12):1252.
Murugaiyan G, da Cunha AP, Ajay AK, Joller N, Garo LP, Kumaradevan S, Yosef N, Vaidya VS, Weiner HL: MicroRNA-21 promotes Th17 differentiation and mediates experimental autoimmune encephalomyelitis. The Journal of clinical investigation 2015, 125(3):1069-1080.
Yao R, Ma Y-L, Liang W, Li H-H, Ma Z-J, Yu X, Liao Y-H: MicroRNA-155 modulates Treg and Th17 cells differentiation and Th17 cell function by targeting SOCS1. PloS one 2012, 7(10):e46082.
Zhang Z, Xue Z, Liu Y, Liu H, Guo X, Li Y, Yang H, Zhang L, Da Y, Yao Z: MicroRNA-181c promotes Th17 cell differentiation and mediates experimental autoimmune encephalomyelitis. Brain, behavior, and immunity 2018, 70:305-314.
Humphries B, Yang C: The microRNA-200 family: small molecules with novel roles in cancer development, progression and therapy. Oncotarget 2015, 6(9):6472.
Park S-M, Gaur AB, Lengyel E, Peter ME: The miR-200 family determines the epithelial phenotype of cancer cells by targeting the E-cadherin repressors ZEB1 and ZEB2. Genes & development 2008, 22(7):894-907.
Pekow JR, Kwon JH: MicroRNAs in inflammatory bowel disease. Inflammatory bowel diseases 2012, 18(1):187-193.
Wang G, Tam L, Li E, Kwan B, Chow K, Luk C, Li P, Szeto C: Serum and urinary free microRNA level in patients with systemic lupus erythematosus. Lupus 2011, 20(5):493-500.
Zibert JR, Løvendorf MB, Litman T, Olsen J, Kaczkowski B, Skov L: MicroRNAs and potential target interactions in psoriasis. Journal of dermatological science 2010, 58(3):177-185.
Honardoost MA, Naghavian R, Ahmadinejad F, Hosseini A, Ghaedi K: Integrative computational mRNA–miRNA interaction analyses of the autoimmune-deregulated miRNAs and well-known Th17 differentiation regulators: An attempt to discover new potential miRNAs involved in Th17 differentiation. Gene 2015, 572(2):153-162.
Ahmadian-Elmi M, Pour AB, Naghavian R, Ghaedi K, Tanhaei S, Izadi T, Nasr-Esfahani MH: miR-27a and miR-214 exert opposite regulatory roles in Th17 differentiation via mediating different signaling pathways in peripheral blood CD4+ T lymphocytes of patients with relapsing–remitting multiple sclerosis. Immunogenetics 2016, 68(1):43-54.
Naghavian R, Ghaedi K, Kiani-Esfahani A, Ganjalikhani-Hakemi M, Etemadifar M, Nasr-Esfahani MH: miR-141 and miR-200a, revelation of new possible players in modulation of Th17/Treg differentiation and pathogenesis of multiple sclerosis. PloS one 2015, 10(5):e0124555.
Langrish CL, Chen Y, Blumenschein WM, Mattson J, Basham B, Sedgwick JD, McClanahan T, Kastelein RA, Cua DJ: IL-23 drives a pathogenic T cell population that induces autoimmune inflammation. Journal of Experimental Medicine 2005, 201(2):233-240.
Aranami T, Yamamura T: Th17 Cells and autoimmune encephalomyelitis (EAE/MS). Allergology International 2008, 57(2):115-120.
Seiderer J, Elben I, Diegelmann J, Glas J, Stallhofer J, Tillack C, Pfennig S, Jürgens M, Schmechel S, Konrad A: Role of the novel Th17 cytokine IL-17F in inflammatory bowel disease (IBD): upregulated colonic IL-17F expression in active Crohn's disease and analysis of the IL17F p. His161Arg polymorphism in IBD. Inflammatory bowel diseases 2008, 14(4):437-445.
Wong CK, Lit LCW, Tam LS, Li EKM, Wong PTY, Lam CWK: Hyperproduction of IL-23 and IL-17 in patients with systemic lupus erythematosus: implications for Th17-mediated inflammation in auto-immunity. Clinical immunology 2008, 127(3):385-393.
Lock C, Hermans G, Pedotti R, Brendolan A, Schadt E, Garren H, Langer-Gould A, Strober S, Cannella B, Allard J: Gene-microarray analysis of multiple sclerosis lesions yields new targets validated in autoimmune encephalomyelitis. Nature medicine 2002, 8(5):500-508.
Fujino S, Andoh A, Bamba S, Ogawa A, Hata K, Araki Y, Bamba T, Fujiyama Y: Increased expression of interleukin 17 in inflammatory bowel disease. Gut 2003, 52(1):65-70.
Kotake S, Udagawa N, Takahashi N, Matsuzaki K, Itoh K, Ishiyama S, Saito S, Inoue K, Kamatani N, Gillespie MT: IL-17 in synovial fluids from patients with rheumatoid arthritis is a potent stimulator of osteoclastogenesis. The Journal of clinical investigation 1999, 103(9):1345-1352.
Olaru AV, Selaru FM, Mori Y, Vazquez C, David S, Paun B, Cheng Y, Jin Z, Yang J, Agarwal R: Dynamic changes in the expression of MicroRNA-31 during inflammatory bowel disease-associated neoplastic transformation. Inflammatory bowel diseases 2011, 17(1):221-231.
Liu X, He F, Pang R, Zhao D, Qiu W, Shan K, Zhang J, Lu Y, Li Y, Wang Y: IL-17-induced MiR-873 attributes to the pathogenesis of experimental autoimmune encephalomyelitis by targeting A20 ubiquitin editing enzyme. Journal of Biological Chemistry 2014:jbc. M114. 577429.
Delavar MR, Baghi M, Safaeinejad Z, Kiani-Esfahani A, Ghaedi K, Nasr-Esfahani MH: Differential expression of miR-34a, miR-141, and miR-9 in MPP+-treated differentiated PC12 cells as a model of Parkinson's disease. Gene 2018, 662:54-65.
Talepoor Ardakani M, Rostamian Delavar M, Baghi M, Nasr‐Esfahani MH, Kiani‐Esfahani A, Ghaedi K: Upregulation of miR‐200a and miR‐204 in MPP+‐treated differentiated PC12 cells as a model of Parkinson’s disease. Molecular genetics & genomic medicine 2019, 7(3):e548.
Iborra M, Bernuzzi F, Invernizzi P, Danese S: MicroRNAs in autoimmunity and inflammatory bowel disease: crucial regulators in immune response. Autoimmunity reviews 2012, 11(5):305-314.
Sievers C, Meira M, Hoffmann F, Fontoura P, Kappos L, Lindberg RL: Altered microRNA expression in B lymphocytes in multiple sclerosis: towards a better understanding of treatment effects. Clinical immunology 2012, 144(1):70-79.
van de Pavert SA, Ferreira M, Domingues RG, Ribeiro H, Molenaar R, Moreira-Santos L, Almeida FF, Ibiza S, Barbosa I, Goverse G: Maternal retinoids control type 3 innate lymphoid cells and set the offspring immunity. Nature 2014, 508(7494):123-127.
Mucida D, Park Y, Kim G, Turovskaya O, Scott I, Kronenberg M, Cheroutre H: Reciprocal TH17 and regulatory T cell differentiation mediated by retinoic acid. Science 2007, 317(5835):256-260.
Kang SG, Wang C, Matsumoto S, Kim CH: High and low vitamin A therapies induce distinct FoxP3+ T-cell subsets and effectively control intestinal inflammation. Gastroenterology 2009, 137(4):1391-1402. e1396.
Klemann C, Raveney BJ, Klemann AK, Ozawa T, von Hörsten S, Shudo K, Oki S, Yamamura T: Synthetic retinoid AM80 inhibits Th17 cells and ameliorates experimental autoimmune encephalomyelitis. The American journal of pathology 2009, 174(6):2234-2245.
Xiao S, Jin H, Korn T, Liu SM, Oukka M, Lim B, Kuchroo VK: Retinoic acid increases Foxp3+ regulatory T cells and inhibits development of Th17 cells by enhancing TGF-β-driven Smad3 signaling and inhibiting IL-6 and IL-23 receptor expression. The Journal of Immunology 2008, 181(4):2277-2284.
Agarwal V, Bell GW, Nam J-W, Bartel DP: Predicting effective microRNA target sites in mammalian mRNAs. elife 2015, 4:e05005.
Paraskevopoulou MD, Georgakilas G, Kostoulas N, Vlachos IS, Vergoulis T, Reczko M, Filippidis C, Dalamagas T, Hatzigeorgiou AG: DIANA-microT web server v5. 0: service integration into miRNA functional analysis workflows. Nucleic acids research 2013, 41(W1):W169-W173.
Wong N, Wang X: miRDB: an online resource for microRNA target prediction and functional annotations. Nucleic acids research 2015, 43(D1):D146-D152.
Teimuri S, Hosseini A, Rezaenasab A, Ghaedi K, Ghoveud E, Etemadifar M, Nasr-Esfahani MH, Megraw TL: Integrative analysis of lncRNAs in Th17 cell lineage to discover new potential biomarkers and therapeutic targets in autoimmune diseases. Molecular Therapy-Nucleic Acids 2018, 12:393-404.
Oliveros JC: VENNY. An interactive tool for comparing lists with Venn Diagrams. http://bioinfogp cnb csic es/tools/venny/index html 2007.
Chou C-H, Shrestha S, Yang C-D, Chang N-W, Lin Y-L, Liao K-W, Huang W-C, Sun T-H, Tu S-J, Lee W-H: miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions. Nucleic acids research 2018, 46(D1):D296-D302.
Karagkouni D, Paraskevopoulou MD, Chatzopoulos S, Vlachos IS, Tastsoglou S, Kanellos I, Papadimitriou D, Kavakiotis I, Maniou S, Skoufos G: DIANA-TarBase v8: a decade-long collection of experimentally supported miRNA–gene interactions. Nucleic acids research 2018, 46(D1):D239-D245.
Huang R, Grishagin I, Wang Y, Zhao T, Greene J, Obenauer JC, Ngan D, Nguyen D-T, Guha R, Jadhav A: The NCATS BioPlanet–an integrated platform for exploring the universe of cellular signaling pathways for toxicology, systems biology, and chemical genomics. Frontiers in pharmacology 2019, 10:445.
Jassal B, Matthews L, Viteri G, Gong C, Lorente P, Fabregat A, Sidiropoulos K, Cook J, Gillespie M, Haw R: The reactome pathway knowledgebase. Nucleic acids research 2020, 48(D1):D498-D503.
Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K: KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic acids research 2017, 45(D1):D353-D361.
Piñero J, Ramírez-Anguita JM, Saüch-Pitarch J, Ronzano F, Centeno E, Sanz F, Furlong LI: The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic acids research 2020, 48(D1):D845-D855.
Kohl M, Wiese S, Warscheid B: Cytoscape: software for visualization and analysis of biological networks. In: Data mining in proteomics. Springer; 2011: 291-303.
Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, Santos A, Doncheva NT, Roth A, Bork P: The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible. Nucleic acids research 2016:gkw937.

Sup.jpg.jpg
Supplementary figure. The integrated diagram of KEGG pathways in which RARB involved. Red stars marks pathways and genes associated with RARB. RARB is correlated with genes and pathways strongly involved in Th17 differentiation or immune responses.
Sup.jpg.jpg
Supplementary figure. The integrated diagram of KEGG pathways in which RARB involved. Red stars marks pathways and genes associated with RARB. RARB is correlated with genes and pathways strongly involved in Th17 differentiation or immune responses.
Sup.jpg.jpg
Supplementary figure. The integrated diagram of KEGG pathways in which RARB involved. Red stars marks pathways and genes associated with RARB. RARB is correlated with genes and pathways strongly involved in Th17 differentiation or immune responses.
SupplementaryExcelfile.xlsx
Supplementary table. Prediction of miR-141 and miR-200a genes in 3 different databases as well as Th17 cell and autoimmunity genes
SupplementaryExcelfile.xlsx
Supplementary table. Prediction of miR-141 and miR-200a genes in 3 different databases as well as Th17 cell and autoimmunity genes
SupplementaryExcelfile.xlsx
Supplementary table. Prediction of miR-141 and miR-200a genes in 3 different databases as well as Th17 cell and autoimmunity genes

Download PDF

Version 1

posted

You are reading this latest preprint version

miR-141 and miR-200a are involved in Th17 cell differentiation by negatively regulating RARB expression

Status:

Version 1

Abstract

Figures

Background

Results

Discussion

Conclusion

Materials And Methods

Declarations

References

Supplementary Files

Status:

Version 1