miRNAs and NFKB1 and TRAF6 target genes: The initial functional study in CD14+ monocytes in rheumatoid arthritis patients

Abstract

We predicted miRNAs with regulatory impact on NFKB1 and TRAF6 gene expression and selected the miR-194-5p, miR-124-3p, miR-9-5p, and miR-340-5p and their target genes for expression analyses on CD14+ monocytes from rheumatoid arthritis (RA) patients and healthy controls. Additionally, we evaluated the influence of genes and miRNA expression on RA patients’ cytokine levels. No difference was observed in genes or miRNAs expression when compared to healthy controls and RA patients or clinical parameters. However, we found a significant difference between miR-194-5p and miR-9-5p levels (FC=-2.31; p=0.031; FC=-3.05;p=0.031, respectively) and non-prednisone users as compared to prednisone using patients. We conducted correlation analyses to identify the strength of the relationship between expression data and cytokine plasma levels. We observed a moderate positive correlation between miR-124-3p expression and IL-6 plasma levels (r=0.46; p=0.033). In addition, overexpression of miRNAs was concomitant to TRAF6 and NFKB1 genes as indicated by correlation analyses: TRAF6 and miR-194-5p (r=0.60;p<0.001) and miR-9-5p (r=0.63;p<0.001) and NFKB1 and miR-194-5p (r=0.72;p<0.001), miR-9-5p (r=0.72;p<0.001) and miR-340-5p (r=0.61;p<0.001). NFKB1 and TRAF6 genes and miRNAs monocyte expression do not appear to be related to RA but showed a significant difference in different groups of RA therapy. In addition, increased levels of miRNAs can be linked to concomitant overexpression of TRAF6 and NFKB1 in monocytes and act as its regulators.

Keywords:
miRNA; NFKB1; TRAF6

Introduction

Rheumatoid arthritis (RA) is a complex and autoimmune disease that affects joints and can promote irreversible disability in patients (Smolen et al., 2018). Genetic, epigenetic, and environmental factors can trigger RA. However, its etiology still needs to be fully understood. miRNAs can be related to RA trigger and its pathogenesis once it can influence gene regulation, thus leading to increased cytokines, chemokines, and autoantibodies and promoting tissue damage (de la Rica et al., 2013).

In addition, until now, the role of innate immunity cells is not clear in the etiology or pathogenesis of the disease. Besides, evidence is emerging to support the influence of monocytes on RA (Evans et al., 2009; Stuhlmüller et al., 2010; Tsukamoto et al., 2017; Smiljanovic et al., 2018). Literature data indicates that biomarkers on monocytes and other immune cells could help to predict therapeutic responses in RA or indicate disease activity in other autoimmune diseases, such as Systemic lupus erythematosus (SLE) (Stuhlmüller et al., 2010; Abd-Elhamid et al., 2017; Smiljanovic et al., 2018).

Transcriptome analysis of monocytes from RA patients indicated a dysregulation of inflammatory molecules when compared to osteoarthritis (OA) patients and healthy controls; among these, the NFκB pathway is the most important (Smiljanovic et al., 2018). The NFκB pathway is related to inflammation due to participation in the processes of activation, differentiation, and homeostasis of immune cells and secretion of pro-inflammatory cytokines (Sun et al., 2013; Liu et al., 2017). The NFκB family is a complex formed by two subunits (homodimer or heterodimer) composed of NFκB1, NFκB2, RelA, RelB, and c-Rel (Sun et al., 2013; Liu et al., 2017). The heterodimer composed of NFκB1/ RelA or NFκB1/c-Rel is predominant in different immune cells and plays a role in autoimmunity (Lawrence, 2009; Sun et al., 2013). In addition, a key molecule for NFκB activation is the tumor necrosis factor receptor (TNFR)-associated factor 6 (TRAF6) (Walsh et al., 2015). TRAF6 is a cytoplasmatic adaptor protein responsible for promoting signal transduction induced mainly by TNFR and IL1R, which leads to the activation of the NFκB pathway (Walsh et al., 2015). TRAF6 also mediates signals of various cellular receptors and acts in immunoregulatory functions, development, homeostasis, and activation of immune and non-immune cells (Wang et al., 2015a; Walsh et al., 2015; Zhu et al., 2017).

The expression increase of TRAF6 and NFκB1 in RA patients synovium promotes a higher concentration of inflammatory cells in the joint and, consequently, tissue damage in the synovium (Zhu et al., 2012; Świerkot et al., 2016; Liu et al., 2017; Puchner et al., 2018). However, there is still no consensus on the monocyte’s participation in this TRAF6 and NFKB1 gene deregulation and which factors are responsible for this (Smolen et al., 2018).

Some studies verified the interference of miRNAs on the regulation of the NFKB1 and TRAF6 genes in cancer (Huang et al., 2016; Wu et al., 2018; Zhu et al., 2020) and cardiovascular diseases (Liang et al., 2019). Specifically, in autoimmune diseases, only Yue et al. (2019) observed a negative regulation of NFKB1 mediated by miR-9-5p in BV2 cells in the study with multiple sclerosis. Thus, cell assays using monocytes can allow new insights into the pathogenesis of RA and be particularly promising for personalized medicine in the disease (Evans et al., 2009; Davignon et al., 2013; Puchner et al., 2018).

This case-control study aimed to verify potential miRNAs that target TRAF6 and NFKB1 genes and verify an association between TRAF6 and NFKB1 genes and miRNAs expression and RA etiopathogenesis and therapy. We also evaluated the expression of genes and miRNAs related to cytokine levels and the possible clinical significance of these findings in RA pathogenesis.

Material and Methods

Study participants

The present study is an observational case-control study. Twenty RA patients diagnosed according to the 2010 classification criteria of the American College of Rheumatology/European League Against Rheumatism (ACR/EULAR) (Smolen et al., 2010) at the Policlinica Doutor Jamacy de Medeiros and Hospital das Clinicas of Federal University of Pernambuco, Recife, Pernambuco, Brazil were enrolled in our study. Demographic data and clinical features were collected in appropriate questionnaires during clinical care. Biochemical analyses (C reactive protein (CRP), erythrocyte sedimentation rate (ESR), and rheumatoid factor (RF)) were evaluated from the peripheral blood of each individual participating in the study. Patients included were women naive for treatment or treated only with glucocorticoid and/or disease-modifying antirheumatic drugs (DMARDs) synthetic (methotrexate, leflunomide, sulfasalazine, or hydroxychloroquine). The RA patients who received any biological agents were excluded from the study. The RA patients were divided into three subgroups: naïve (untreated patients); monotherapy (RA patients on monotherapy of synthetic DMARDs (methotrexate, leflunomide or hydroxychloroquine)) and combined treatment (RA patients on combined therapy with methotrexate plus hydroxychloroquine or methotrexate plus sulfasalazine)) to evaluate the relation of the TRAF6, NFKB1, and miRNAs with the treatment strategies. Besides this, we also stratified RA patients according to the use of glucocorticoids (prednisone users and non-prednisone users).

The healthy control group consisted of 18 individuals matched by gender, age, BMI (Body Mass Index), and geographic region. We also performed biochemical analysis (ESR) and excluded individuals with a significant level of inflammation (ESR >30 mm/h) and with autoimmune, chronic, or infectious diseases. This study was approved by the Ethics Committee of the Health Sciences Center of the Federal University of Pernambuco (CAAE 10035418.4.0000.5208). All participants signed a written informed consent according to the Declaration of Helsinki.

miRNA prediction analysis

The miRNAs were chosen using four prediction tools available online (TargetScan 7.1 (Agarwal et al., 2015); DIANA-MicroT (Paraskevopoulou et al., 2013); miRanda-mirSVR (Betel et al., 2010) and PicTar (Krek et al., 2005)). The miRNAs selected for the study were predicted at least in three tools and are more likely to have an impact on NFKB1 (Ensembl: ENSG00000109320) or TRAF6 (Ensembl: ENSG00000175104) gene expression.

Cell Isolation

The peripheral blood mononuclear cell (PBMC) isolation was performed from peripheral blood collected in vacutainer tubes containing heparin according to standard density gradient centrifugation with Ficoll-Paque Plus (GE Healthcare, USA). Subsequently, 1 x 10⁷ cells were used from each individual to purify CD14+ monocytes using positive sorting with Dynabeads CD14 (Invitrogen, USA). Posteriorly, cells were labeled with anti-CD14-FITC (BD Biosciences, USA) and anti-CD3-PE-Cy5.5 (BD Biosciences, USA) and incubated for 30 min at 4 ºC to perform the immunofluorescence analysis in the flow cytometry Accuri C6 Flow Cytometer (BD Biosciences, USA).

RNA isolation and determination of TRAF6 gene and miRNAs expression

The total RNA from monocytes was extracted using TRIzol^® reagent (Invitrogen, USA). The reverse transcription was performed by GoScript Reverse Transcription System (Promega, USA) according to the manufacturer’s protocol starting from 500 ng of RNA. We performed a quantitative reverse transcription PCR (qRT-PCR) using TaqMan probes as follows: NFKB1 (ID assay: Hs00765730), TRAF6 (ID assay: Hs00939742) GAPDH (ID assay: Hs03929097), ACTB (ID assay: Hs99999903), 18S (ID assay: Hs03003631). RPLP0 gene expression was also assessed using SYBR Green assay using 1X SYBR Green PCR Master Mix (Thermo Fisher Scientific, USA) and 10 µM of PCR primers previously validated (de Lima et al., 2019). NFKB1 and TRAF6 gene expression was normalized by GAPDH, ACTB, 18S, and RPLP0 reference genes.

The TaqMan MicroRNA Reverse Transcription Kit (Thermo Fisher Scientific, USA) was used to perform the cDNA of miRNAs analyzed using an input of 10ng of total RNA. Small RNAs analyses were performed with the probes miR-194-5p (ID assay: 000493), miR-124-3p (ID assay: 001182), miR-9-5p (ID assay: 000583), miR-340-5p (ID assay: 002258), RNU6B (ID assay: 001093) and RNU48 (ID Assay: 001006). miRNAs expression was normalized by RNU48 and RNU6B reference small RNAs.

All analyses were performed in triplicate using the ABI Prism 7500 Sequence Detection System (Thermo Fisher Scientific, USA). The relative gene expression and miRNA expression were conducted following the Vandesompele method (Vandesompele et al., 2002) and MIQE guidelines (Bustin et al., 2009).

Cytokine levels

Plasma samples were obtained from 25 individuals (16 RA patients and 9 healthy controls) during PBMC isolation after standard density gradient centrifugation and stored at -80 ºC until cytokine quantification. Cytokine levels (TNF-α, IL-6, IL-2, and IL-10) were measured using BD™ Cytometric Bead Array (CBA) Human Th1/Th2 Cytokine Kit II. All analyses were performed in an Accuri C6 Flow Cytometer (BD Biosciences, USA).

Statistical analyses

Data are expressed as mean ± standard deviation to quantitative variables or percentage and number to categorical variables. Relative expression levels were calculated using normalized data (Vandesompele et al., 2002). The normal distribution was tested according to the Shapiro-Wilk test, and parametric (ANOVA or T Student) or non-parametric (Kruskal-Wallis or Mann-Whitney U) tests were used as appropriate. Correlations between two continuous variables were measured using Pearson or Spearman’s correlation coefficient (r). P values < 0.05 were considered statistically significant. GraphPad Prism 6.0 software was employed for data analysis.

Results

Subjects of study

The demographic and clinical characteristics of RA patients and healthy controls are shown in Table 1. Both groups of patients and healthy controls were females with a mean age of 53.20±7.96 years and 53.83±4.54 years, respectively. RA group had a mean disease duration of 79.0 ±81.99 months and presented an active RA (DAS28-ESR: 5.41±1.20; CDAI: 28.99±12.97). Three patients were untreated, while nine patients received methotrexate, leflunomide, or hydroxychloroquine as monotherapy or in combination with prednisone, and eight patients were under treatment with combined synthetic DMARDs (methotrexate plus hydroxychloroquine or methotrexate plus sulfasalazine).

Table 1 -
Demographic data and clinical parameters features of patients with rheumatoid arthritis and healthy controls.

Variable	RA patients (N=20)	Healthy controls (N=18)
Age; mean ± SD years	53.20 ± 7.96	53.83 ± 4.54
BMI; mean ± SD	27.37 ± 5.91	26.28 ± 3.90
ESR, mean ± SD mm/h	29.40 ± 11.19	18.47 ± 7.42
CRP, mean ± SD mg/L	1.60 ± 2.09
Age at RA onset; mean ± SD years	46.60 ± 9.48
Disease duration, mean ± SD months	79.08 ± 81.99
DAS28, mean ± SD	5.41 ± 1.20
CDAI; mean ± SD	28.39 ± 12.97
HAQ; mean ± SD	0.86 ± 0.73
TJC; mean ± SD	11.05 ± 9.26
SJC; mean ± SD	4.65 ± 4.73
Rheumatoid factor positivea, n (%)	7 (53.85)
Joint space narrowing presenceb, n (%)	8 (80.00)
Treatment
Without treatment n (%)	3 (15.00)
Corticosteroids, n (%)	11 (55.00)
Methotrexate, n (%)	12 (60.00)
Hydroxychloroquine, n (%)	9 (45.00)
Leflunomide, n (%)	2 (10.00)
Sulfasalazine, n (%)	1 (5.00)
Hypertension	11 (55.00)

• ^a
  ^- Data available for 13 patients; ^b- Data available for 10 patients; SD: Standard deviation; BMI: Body Mass Index; ESR: Erythrocyte sedimentation rate; CRP: C-reactive protein; DAS28: RA disease activity score 28 joint; CDAI: Clinical Disease Activity Index; HAQ: Health
• Assessment Questionnaire; TJC: Tender joint count; SJC: Swollen joint count.

miR-194-5p and miR-124-3p, miR-9-5p and miR-340-5p directly target the 3′UTR of TRAF6 and NFKB1 genes, respectively, according to in silico approach

We identified 87 miRNAs predicted by DIANA-MicroT, 13 miRNAs predicted by TargetScan, 21 miRNAs predicted by miRanda-mirSVR, and 2 miRNAs predicted by PicTar for a potential binding site of TRAF6. We also considered different scores (DIANA-MicroT: miTG score; TargetScan: context score and Pct score; and miRanda-SVR: miSVR score) to indicate a possible impact on TRAF6 expression and selected the miR-194-5p and miR-124-3p to conduct the assays. Likewise, we found miRNAs with potential binding sites in NFKB1 predicted by TargetScan (n=16), DIANA-MicroT (n=41), miRanda-mirSVR (n=17) and PicTar (n=1). The miR-9-5p and miR-340-5p were selected for analysis.

TRAF6, NFKB1, and miRNAs expression are not related to development and clinical parameters but are influenced by the RA treatment strategy

We evaluated the TRAF6 and NFKB1 genes and miRNAs expression in monocytes from RA patients and healthy controls. Relative expression data did not show a significant difference when comparing RA patients with healthy controls to both genes TRAF6 (FC (Fold Change) = -1.227; p=0.404) and NFKB1 (FC=-1.013; p=0.798). Likewise, miRNA expression data also did not show a significant difference when comparing RA patients with healthy controls for miR-194-5p (FC=-1.042; p=0.796), miR-124-3p (FC=-1.126; p=0.910), miR-9-5p (FC=-1.200; p=0.378) and miR-340-5p (FC=1.482; p=0.448). Data are shown in Figure 1.

Figure 1 -
Relative expression levels of TRAF6, NFKB1, and miRNAs in monocytes of RA patients and healthy controls. The non-parametric Mann-Whitney test was used to test for statistical differences. Data are presented as mean (central line) and standard deviation (top and bottom of the line).

In addition, we evaluated the correlations between genes and miRNAs expression with clinical characteristics of RA patients. We did not observe correlations between TRAF6, NFKB1, and miRNAs expression and disease duration, activity disease parameters (DAS28 and CDAI), or biochemical features (ESR and CRP). Furthermore, no significant correlation was found in the inflamed joint counts TJC (tender joints count), SJC (swollen joint count), or disability index, measured by HAQ. Correlation data are shown in Table 2.

Table 2 -
Correlation between TRAF6, NFKB1 and miRNAs expression in CD14+ monocytes and clinical characteristics of RA patients.

Variables	NFKB1 gene		TRAF6 gene		miR-194-5p		miR-124-3p		miR-9-5p		miR-340-5p
	r	p-value	r	p-value	r	p-value	r	p-value	R	p-value	r	p-value
Disease duration	0.18	0.450 b	-0.02	0.944 b	0.12	0.601 b	-0.10	0.684 b	0.28	0.262 b	0.04	0.888 b
DAS28	-0.23	0.321 a	-0.14	0.567 b	-0.29	0.208 b	-0.20	0.448 a	-0.14	0.578 b	-0.29	0.254 b
CDAI	-0.26	0.276 a	-0.24	0.299 b	-0.34	0.139 b	-0.20	0.453 a	-0.18	0.485 b	-0.37	0.141 b
HAQ	-0.15	0.516 b	-0.30	0.195 b	-0.13	0.571 b	-0.37	0.145 b	-0.11	0.671 b	-0.32	0.204 b
ESR	-0.18	0.458 a	0.10	0.859 b	-0.01	0.992 b	0.28	0.270 a	-0.28	0.253 b	-0.11	0.664 b
CRP	-0.19	0.517 b	0.05	0.859 b	-0.21	0.470 b	0.31	0.359 b	-0.29	0.357 b	0.11	0.739 b
TJC	-0.19	0.432 b	-0.06	0.791 b	-0.11	0.638 b	-0.09	0.720 b	0.04	0.874 b	-0.18	0.485 b
SJC	0.33	0.166 b	0.15	0.530 b	0.19	0.436 b	-0.29	0.281 a	0.11	0.671 b	0.06	0.822 b

• r: correlation coefficient; DAS28: Disease Activity Score 28-joint; CDAI: Clinical Disease Activity Index; HAQ: Health Assessment Questionnaire; ESR: erythrocyte sedimentation rate; CRP: C-reactive protein; TJC: Tender joint count; SJC: Swollen joint count;
• ^a
  - Correlations was tested using Pearson’s correlation test; ^b- Correlations was tested using Spearman’s correlation test.

Finally, we evaluated differences in the miRNA’s expression and RA therapy. RA patients were stratified according to the use of glucocorticoids (prednisone), and we observed that non-prednisone users showed significantly lower miR-194-5p expression when compared to prednisone users (FC=-2.31; p=0.031). Similarly, non-prednisone users showed lower miR-9-5p levels when compared to prednisone users (FC=-3.05; p=0.031) (Table 3).

Table 3 -
Normalized quantitative expression of TRAF6 and NFKB1 genes and miRNAs expression in CD14+ monocytes from RA patients in different treatment strategies.

Variables	NFKB1 gene	TRAF6 gene	miR-194-5p	miR-124-3p	miR-9-5p	miR-340-5p
Treatment strategies
Naïve (N = 3)	1.107 ± 0.198	1.423 ± 0.679	0.277 ± 0.120	0.66 (0.56-0.76)	0.14 (0.12 - 0.28)	0.130 ± 0.111
DMARDs as monotherapy (N = 9)	1.531 ± 0.484	1.632 ± 0.703	0.592 ± 0.351	0.47 (0.22 - 0.85))	0.40 (0.23 - 0.88)	0.270 ± 0.222
DMARDs combined (N = 8)	1.511 ± 0.869	1.578 ± 0.862	0.845 ± 0.704	0.50 (0.17 - 0.89)	0.31 (0.25 - 0.45)	0.782 ± 0.792
P-value	0.606 a	0.961 a	0.269 a	0.756 b	0.267 b	0.102 a
Use of glucocorticoid
Prednisone user (N= 11)	1.656 ± 0.682	1.744 ± 0.793	0.84 (0.39 - 1.01)	0.36 (0.17 - 0.82)	0.48 (0.28 - 0.88)	0.631 ± 0.679
No Prednisone user (N = 9)	1.218 ± 0.509	1.377 ± 0.634	0.33 (0.19-0.36)	0.63 (0.38 - 0.87)	0.23 (0.14 - 0.40)	0.237 ± 0.338
P-value	0.128a	0.277 a	0.031 b	0.228 b	0.031 b	0.0941 a

• Normal distribution data was shown as mean ± standard deviation, while non-normal distribution data was shown as median (25th-75th). Naïve: RA patients untreated; DMARDs as monotherapy: RA patients treated with methotrexate, leflunomide, or hydroxychloroquine as monotherapy; DMARDs combined: RA patients treated with combined therapy (methotrexate plus hydroxychloroquine or methotrexate plus sulfasalazine). Bold means significant
• ^a
  Statistic was tested using parametric tests (Unpaired t-test or ANOVA); ^b Statistic was tested using non-parametric tests (Mann-Whitney or Kruskal-Wallis tests).

We also stratified RA patients according to their use of synthetic DMARDs: untreated (naive), monotherapy of synthetic DMARDs (methotrexate, leflunomide, or hydroxychloroquine), and RA patients with combined DMARDs (methotrexate plus hydroxychloroquine or methotrexate plus sulfasalazine). However, we did not observe significant differences among these groups.

miR-124-3p expression in monocytes is positively correlated to IL-6 plasma levels

We performed correlation analyses to verify the influence of TRAF6 and NFKB1 genes and miRNAs expression in the TNF-α, IL-6, IL-2, and IL-10 plasma cytokines levels. Correlation data are shown in Table 4. We observed a moderate positive correlation between miR-124-3p expression and IL-6 plasma levels (r=0.46 p=0.033). Besides this, the results indicate a tendency of correlation between TRAF6 gene expression and IL-10 plasma levels (r = 0.40; p = 0.051). In addition, no significant correlation was observed between genes and miRNA expression and other plasma cytokine levels.

Table 4 -
Correlation between TRAF6, NFKB1 and miRNAs expression in CD14+ monocytes and TNF-α, IL-6, IL-2 and IL-10 plasma cytokines levels from RA patients and healthy controls.

Variables	NFKB1 gene		TRAF6 gene		miR-194-5p		miR-124-3p		miR-9-5p		miR-340-5p
	r	p-value	r	p-value	r	p-value	r	p-value	R	p-value	r	p-value
TNF-α	0.12	0.556	0.14	0.515	-0.05	0.821	-0.05	0.824	0.04	0.859	-0.09	0.713
IL-6	0.19	0.373	0.25	0.247	0.20	0.367	0.46	0.033	0.01	0.984	-0.01	0.981
IL-2	0.31	0.134	0.23	0.276	0.33	0.125	-0.01	0.986	0.29	0.187	0.23	0.373
IL-10	0.34	0.093	0.40	0.051	0.17	0.441	0.08	0.719	-0.03	0.899	-0.2	0.951

• r: correlation coefficient; BMI: Body Mass Index; DAS28: Disease Activity Score 28-joint; CDAI: Clinical Disease Activity Index; HAQ: Health Assessment Questionnaire; ESR: erythrocyte sedimentation rate; CRP: C-reactive protein; TJC: Tender joint count; SJC: Swollen joint count;
• Correlations was tested using Spearman’s correlation test. Bold means significant.

TRAF6 and NFKB1 gene expression are strongly correlated in monocytes

NFKB1 and TRAF6 are closely related in many pathways, and we decided to assess whether the overexpression of NFKB1 is concomitant to the overexpression of TRAF6 in monocytes. Analysis using all individuals of the study (RA patients plus healthy controls) showed a strong positive correlation between NFKB1 and TRAF6 genes (r=0.820; p<0.001) (Figure 2a). In the same way, in the subgroup analyses, a strong positive correlation was observed in the RA patients’ group (r=0.836, p<0.001) and healthy control group (r=0.831, p<0.001), suggesting that both are related in the inflammatory and non-inflammatory context.

Figure 2 -
Scatter plot demonstrating correlations of mRNAs and miRNAs gene expression (fold change) in monocytes from RA patients and controls using Spearman’s correlation analyses. a) Correlation between TRAF6 and NFKB1 gene expression; b) Correlation between TRAF6 gene expression and miR-194-5p expression; c) Correlation between TRAF6 gene expression and miR-9-5p expression; d) Correlation between NFKB1 gene expression and miR-194-5p expression; e) Correlation between NFKB1 gene expression and miR-9-5p expression; f) Correlation between NFKB1 gene expression and miR-340-5p expression.

miR-194-5p, miR-9-5p and miR-340-5p may act as regulators of TRAF6 and NFKB1 genes in monocytes

We conducted correlation analyses to explore the potential role of miR-194-5p, miR-124-3p, miR-9-5p, and miR-340-5p in TRAF6 and NFKB1 gene expression.

A significant correlation was observed between TRAF6 gene expression and miR-194-5p, indicating a moderate positive correlation (r=0.603; p<0.001) (Figure 2b), which was maintained in the subgroups analysis (RA patients’ group: r=0.721; p<0.001; control group: r=0.521; p=0.041). A positive correlation was also observed between miR-9-5p and TRAF6 gene (r=0.636; p<0.001) (Figure 2c) and in the subgroup analyses (RA patients’ group: r=0.552, p=0.018; control group: r=0.723, p=0.004).

On the other hand, we did not find a correlation between TRAF6 gene expression and miR-124-3p levels in the total individuals (r=-0.105; p=0.562) or the subgroups analyzed (RA patients’ group: r=0.154, p=0.553; control group: r=-0.159, p=0.556). Likewise, we did not observe a correlation between miR-340-5p and TRAF6 gene expression in the analysis, including total individuals (r=0.455; p=0.149) or the subgroups analyzed (RA patients group: r=0.417, p=0.098 and control group: r=0.573, p=0.071).

Concerning NFKB1, the analysis using total subjects showed a strong positive correlation between NFKB1 and miR-194-5p (r=0.718; p<0.001) (Figure 2d). Similarly, the subgroups analyzed showed the same findings (RA patients’ group: r=0.849; p<0.001; control group: r=0.642; p=0.006). We also observed a positive and strong correlation between NFKB1 gene expression and miR-9-5p (r=0.719; p<0.001) (Figure 2e) and the analysis of the subgroups RA patients (r=0.719; p<0.001) or controls (r=0.750; p=0.002). Likewise, a positive correlation also was found between the NFKB1 gene and miR-340-5p (r=0.606; p<0.001) (Figure 2f) and in the subgroup analyses (RA patients’ group: r=0.534, p=0.029; control group: r=0.727, p=0.014). On the other hand, the NFKB1 gene expression and miR-124-3p were not correlated in the group of total individuals (r=-0.257; p=0.142) or the subgroups (RA patients’ group: r=-0.194, p=0.454; control group: r=-0.306, p=0.231).

Interestingly, our results also indicate a strong correlation between all the analyzed miRNAs, suggesting that their expression is similar in monocytes (miR-194-5p and miR-9-5p: r=0.819 p<0.001; miR-194-5p and miR-340-5p: r=0.661 p<0.001; miR-9-5p and miR-340: r=0.765 p<0.001).

Discussion

In this study, we assessed four main topics related to TRAF6 and NFKB1 gene expression and its relationship with inflammation and RA. First, our study evaluated miRNAs with a possible effect on TRAF6 and NFKB1 expression through an in silico approach. Second, we conducted mRNA and miRNA expression analyses of RA patients and healthy controls in monocytes to assess an association with etiopathogenesis in RA. Third, we verified the influence of genes and miRNA expression on plasma cytokine levels. Fourth, we verified the correlation between mRNAs and miRNA expression.

Our results in silico showed that miR-9-5p and miR-340-5p seemed to be promising as regulatory factors of NFKB1, while miR-194-5p and miR-124-3p can potentially regulate TRAF6 gene expression. In the Table S1 we summarize the main results of published studies on the expression of TRAF6 and NFKB1 and miR-194-5p, miR-124-3p, miR-9-5p, and miR-340-5p and its implication for Rheumatoid arthritis.

Concerning TRAF6 and NFKB1 and miRNA expression in monocytes and RA development or clinical features, we did not observe differences in genes and miRNA expression between RA patients and healthy controls or about clinical features of RA patients (clinical activity indices, serological parameters, or treatment). Similar to our findings, no statistical differences were observed by Zhu et al. (2012) when evaluating the relationship between TRAF6 expression in synovial tissue and clinical features of RA patients, although studies indicated overexpression of TRAF6 in synovial of RA and RA-fibroblast-like synoviocytes (RA-FLSs) (Zhu et al., 2012; Wang et al., 2015b). The expression of the NFKB1 gene seems to be relevant to RA severity, as observed by Sarmiento Salinas et al. (2018) since the authors reported an upregulation of NFKB1 in active RA compared to inactive RA patients. Regarding miRNA expression, we also did not observe statistical differences in the development or clinical parameters of the RA patients. Low levels of miR-9-5p, miR-124-3p, and miR-340-5p in plasma or serum of RA patients as compared with controls were observed by Wang et al. (2015b); Goldbergová et al. (2018); Zhang et al. (2020). Likewise, it was observed by De la Rosa et al. (2020) in neutrophils from the peripheral blood of RA patients as compared to healthy controls. However, no relation was observed in RA severity or biochemical markers in either of these studies. On the other hand, Fernández-Ruiz et al. (2018) observed that miR-194-5p was overexpressed in whole blood of RA flare-up patients as compared to sustained remission RA.

Interestingly, our observational study showed differences in miR-194-5p and miR-9-5p expression between non-prednisone users as compared to prednisone users, since patients using prednisone showed a 2.31-fold increase in levels of this miRNA, while miR-9-5p showed a 3.05-fold increase in its levels in prednisone users. The relation of miR-194-5p and treatment with prednisone was not tested previously. However, a study conducted by Fernández-Ruiz et al. (2018) failed to detect a relation between treatment with tofacitinib and miR-194-5p in RA patients. Moreover, to our knowledge, the miR-9-5p levels were not tested for any RA treatment strategies until now. Thus, we suggest that the expression of miR-194-5p and miR-9-5p in monocytes can be important to understand the effect of therapy in RA patients. Based on our findings, we suggest that prospective studies should be carried out to clarify whether there is a cause-and-effect relationship between therapy and miRNA expression.

Our findings also showed that miR-124-3p levels presented a significant positive correlation with IL-6 levels, while TRAF6 expression showed a borderline correlation with IL-10 levels (p = 0.051). Similarly, miR-124a-3p overexpression promoted an upregulation of TNF-α, IL-6, and IL-1β production in the human cardiac myocyte (HCM) cell line (Liang et al., 2019). On the other hand, in the murine macrophage RAW264.7 cell line, the miR-124-3p levels were associated with decreased TNFα, IL-6, and IL-1β (Ma et al., 2014), while in the serum of RA patients, no significant correlation was found between miR-124-3p and IL-6, TNFα or IL-8 cytokine levels (Goldbergová et al., 2018). The miR-194-5p and miR-340-5p levels were related to a downregulation of TNF-α, IL-6, and IL-1β cytokine levels in mice nucleus pulposus cells and RA-fibroblast-like synoviocytes induced with lipopolysaccharides (Kong et al., 2018; Zhang et al., 2020) while miR-9-5p plasma levels of RA patients showed no correlation with IL-6, IL-1β and TNF-α levels (Wang et al., 2015b). Considering the discrepancies of different studies in different cells, we suggested that the influence of these genes and miRNAs expression on cytokine production depends on cellular type and it should be studied in each specific cellular context.

In addition, we verified the correlation between mRNAs and miRNAs expression. Our results showed a very strong positive correlation between TRAF6 and NFKB1 gene expression in monocytes. Both genes and their proteins act together to activate the NFκB pathway (Walsh et al., 2015; Liu et al., 2017) and probably show a co-expression to promote inflammation. It is important to note that the close relationship between both molecules can be explored in personalized medicine as a new approach to diseases that present dysregulation of TRAF6 or NFκB1.

Considering that TRAF6 and NFKB1 genes were positively correlated in monocytes, we decided to assess the correlation of all miRNAs (miR-194-5p, miR-124-3p, miR-9-5p, and miR-340-5p) and both genes. We observed a strong positive correlation between TRAF6 with miR-194-5p and miR-9-5p and also NFKB1 with miR-194-5p, miR-9-5p and miR-340-5p. In contrast with our findings, studies showed that miR-194-5p plays a role in TRAF6 suppression in mouse nucleus pulposus cells (Kong et al., 2018) and in THP-1 cells (Tian et al., 2015). In relation to NFKB1, Bazzoni et al. (2009) found similar results since the NFKB1 active by TLR4 enhanced miR-9-5p levels in human monocytes. On the other hand, Gu et al. (2016) also found the suppression of NFKB1 mediated by miR-9-5p in human primary chondrocytes of osteoarthritis patients. Regarding miR-340-5p, Li et al. (2016) found an NFKB1 downregulation mediated by miR-340 in ovarian cancer cells. Our study and literature data agree that these miRNAs can act as regulators of the TRAF6 and NFKB1 genes, although this regulation appears to occur in different ways in different cell types. miRNAs commonly promote downregulation of target genes, but the opposite effect on specific cellular contexts has been seen (Vasudevan et al., 2007; Vasudevan, 2012; Ni and Leng, 2016; O’Brien et al., 2018). These studies suggested that miRNA can affect the mRNA translate activation, promoting mRNA activation to translation but not causing its degradation, which can lead to an increase in the number of mRNA molecules in a specific cellular type (Vasudevan, 2012; Ni and Leng, 2016). According to this, we suggested that these miRNAs (miR-194-5p, miR-9-5p, and miR-340-5p) bind in the 3’UTR region of their target genes (TRAF6 and NFKB1) and block its translation through a mechanism in which the cell stores mRNAs ready for translation if needed.

Although, in this observational study, a functional analysis between miRNAs and mRNAs was not performed, we provide insights that may encourage future studies to test this hypothesis. Besides this, some potential limitations exist, such as a reduced sample size to perform the analyses and a different number of participants in the cytokine levels measurement, which may affect our ability to recognize the real associations between data. Therefore, we encourage future studies, including studies with larger samples and prospective and functional studies that can test the hypotheses raised in our study.

In conclusion, our study showed that expression of the TRAF6 and NFKB1 genes and miRNAs in monocytes do not play a role in the development and pathogenesis of RA, although miR-194-5p and miR-9-5p levels showed differences in patients with different RA treatment strategy. In addition, we observed a significant correlation between genes and miRNAs analyzed and hypothesized the role of these miRNAs as regulators of TRAF6 and NFKB1 in monocytes. We suggest further studies to confirm whether there is a causality relationship between these findings.

Acknowledgements

We would like to thank the Instituto Aggeu Magalhães (Fiocruz Pernambuco) for use of its facilities. This work was supported by the Coordenação de Aperfeiçoamento de Pessoal de Nivel Superior (CAPES) and the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq).

References

• Abd-Elhamid YA, Eltanawy RM, Fawzy RM, Fouad NA and Atlm AM (2017) Expression of CD64 on surface of circulating monocytes in systemic lupus erythematosus patients: Relation to disease activity and lupus nephritis. Egypt J Immunol 24:67-78.
• Agarwal V, Bell GW, Nam J-W and Bartel DP (2015) Predicting effective microRNA target sites in mammalian mRNAs. eLife 4:e05005.
• Bazzoni F, Rossato M, Fabbri M, Gaudiosi D, Mirolo M, Mori L, Tamassia N, Mantovani A, Cassatella MA and Locati M (2009) Induction and regulatory function of miR-9 in human monocytes and neutrophils exposed to pro-inflammatory signals. Proc Natl Acad Sci EUA 106:5282-5287.
• Betel D, Koppal A, Agius P, Sander C and Leslie C (2010) Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites. Genome Biol 11:R90.
• Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M, Mueller R, Nolan T, Pfaffl MW, Shipley GL et al (2009) The MIQE guidelines: Minimum information for publication of quantitative real-time PCR experiments. Clin Chem 55:611-622.
• Davignon J-L, Hayder M, Baron M, Boyer JF, Constantin A, Apprarailly F, Poupot R and Cantagrel A (2013) Targeting monocytes/macrophages in the treatment of rheumatoid arthritis. Rheumatology 52:590-598.
• de la Rica L, Urquiza JM, Gómez-Cabrero D, Islam ABMMK, López-Bigas N, Tegnér J, Toes REM and Ballestar E (2013) Identification of novel markers in rheumatoid arthritis through integrated analysis of DNA methylation and microRNA expression. J Autoimmun 41:6-16.
• De la Rosa IA, Perez-Sanchez C, Ruiz-Limon P, Patiño-Trives A, Torres-Granados C, Jimenez-Gomez Y, Abalos-Aguilera MDC, Cecchi I, Ortega R, Caracuel MA et al (2020) Impaired microRNA processing in neutrophils from rheumatoid arthritis patients confers their pathogenic profile. Modulation by biological therapies. Hematológica 105:2250-2261.
• de Lima CAD, de Lima SC, Barbosa AD, Sandrin-Garcia P, Pita WB, Silva JA and Crovella S (2019) Postmenopausal osteoporosis reference genes for qPCR expression assays. Sci Rep 9:16533.
• Evans HG, Gullick NJ, Kelly S, Pitzalis C, Lord GM, Kirkham BW and Taams LS (2009) In vivo activated monocytes from the site of inflammation in humans specifically promote Th17 responses. Proc Natl Acad Sci U S A 106:6232-6237.
• Fernández-Ruiz JC, Ramos-Remus C, Sánchez-Corona J, Castillo-Ortiz JD, Castañeda-Sánchez JJ, Bastian Y, Romo-García MF, Ochoa-González F, Monsivais-Urenda AE, González-Amaro R et al (2018) Analysis of miRNA expression in patients with rheumatoid arthritis during remission and relapse after a 5-year trial of tofacitinib treatment. Int Immunopharmacol 63:35-42.
• Goldbergová MP, Lipková J, Fedorko J, Nemec P, Gatterová J, Válková L, Sevcikova J and Vasku A (2018) Relationship of epigenetic variability of miR-124 to extracellular matrix remodelling and age-related MMP-3 expression in rheumatoid arthritis. Gen Physiol Biophys 37:703-710.
• Gu R, Liu N, Luo S, Huang W, Zha Z and Yang J (2016) MicroRNA-9 regulates the development of knee osteoarthritis through the NF-kappaB1 pathway in chondrocytes. Medicine (Baltimore) 95:e4315.
• Huang T, Kang W, Zhang B, Wu F, Dong Y, Tong JHM, Yang W, Zhou Y, Zhang L, Cheng ASL et al (2016) miR-508-3p concordantly silences NFKB1 and RELA to inactivate canonical NF-κB signaling in gastric carcinogenesis. Mol Cancer 15:9.
• Kong L, Sun M, Jiang Z, Li L and Lu B (2018) MicroRNA-194 inhibits lipopolysaccharide-induced inflammatory response in nucleus pulposus cells of the intervertebral disc by targeting TNF Receptor-Associated Factor 6 (TRAF6). Med Sci Monit 24:3056-3067.
• Krek A, Grün D, Poy MN, Wolf R, Rosenberg L, Epstein EJ, MacMenamin P, Piedade I, Gunsalus K, Stoffel M et al (2005) Combinatorial microRNA target predictions. Nat Genet 37:495-500.
• Lawrence T (2009) The nuclear factor NF- B pathway in inflammation. Cold Spring Harb Perspect Biol 1:a001651.
• Li P, Sun Y and Liu Q (2016) MicroRNA-340 induces apoptosis and inhibits metastasis of ovarian cancer cells by inactivation of NF-kB1. Cell Physiol Biochem 38:1915-1927.
• Liang Y-P, Liu Q, Xu G-H, Zhang J, Chen Y, Hua F-Z, Deng C-Q and Hu Y-H (2019) The lncRNA ROR/miR-124-3p/TRAF6 axis regulated the ischaemia reperfusion injury-induced inflammatory response in human cardiac myocytes. J Bioenerg Biomembr 51:381-392.
• Liu T, Zhang L, Joo D and Sun S-C (2017) NF-κB signaling in inflammation. Signal Transduct Target Ther 2:17023.
• Ma C, Li Y, Li M, Deng G, Wu X, Zeng J, Hao X, Wang X, Liu J, Cho WCS et al (2014) microRNA-124 negatively regulates TLR signaling in alveolar macrophages in response to mycobacterial infection. Mol Immunol 62:150-158.
• Ni W-J and Leng X-M (2016) miRNA-dependent activation of mRNA translation. MicroRNA 5:83-86.
• O’Brien J, Hayder H, Zayed Y and Peng C (2018) Overview of microRNA biogenesis, mechanisms of actions, and circulation. Front Endocrinol 9:402.
• Paraskevopoulou MD, Georgakilas G, Kostoulas N, Vlachos IS, Vergoulis T, Reczko M, Filippidis C, Dalamagas T and Hatzigeorgiou AG (2013) DIANA-microT web server v5.0: Service integration into miRNA functional analysis workflows. Nucleic Acids Res 41:W169-W173.
• Puchner A, Saferding V, Bonelli M, Mikami Y, Hofmann M, Brunner JS, Caldera M, Gonçalves-Alves E, Binder NB, Fischer A et al (2018) Non-classical monocytes as mediators of tissue destruction in arthritis. Ann Rheum Dis 77:1490-1497.
• Sarmiento Salinas FL, Santillán Benítez JG, Hernández Navarro MD and Mendieta Zerón H (2018) NF-κB1/IKKε gene expression and clinical activity in patients with rheumatoid arthritis. Lab Med 49:11-17.
• Smiljanovic B, Radzikowska A, Kuca-Warnawin E, Kurowska W, Grun JR, Stuhlmuller B, Bonin M, Schulte-Wrede U, Sorensen T, Kyogoku C et al (2018) Monocyte alterations in rheumatoid arthritis are dominated by preterm release from bone marrow and prominent triggering in the joint. Ann Rheum Dis 77:300-308.
• Smolen JS, Landewe R, Breedveld FC, Dougados M, Emery P, Gaujoux-Viala C, Gorter S, Knevel R, Nam J, Schoels M et al (2010) EULAR recommendations for the management of rheumatoid arthritis with synthetic and biological disease-modifying antirheumatic drugs. Ann Rheum Dis 69:964-975.
• Smolen JS, Aletaha D, Barton A, Burmester GR, Emery P, Firestein GS, Kavanaugh A, McInnes IB, Solomon DH, Strand V et al (2018) Rheumatoid arthritis. Nat Rev Dis Primer 4:18001.
• Stuhlmüller B, Häupl T, Hernandez MM, Grutzkau A, Kuban R-J, Tandon N, Voss JW, Salfeld J, Kinne RW and Burmester GR (2010) CD11c as a transcriptional biomarker to predict response to anti-TNF monotherapy with adalimumab in patients with rheumatoid arthritis. Clin Pharmacol Ther 87:311-321.
• Sun S-C, Chang J-H and Jin J (2013) Regulation of nuclear factor-κB in autoimmunity. Trends Immunol 34:282-289.
• Świerkot J, Nowak B, Czarny A, Zaczynska E, Sokolik R, Madej M, Korman L, Sebastian A, Wojtala P, Lubinski L, et al (2016) The activity of JAK/STAT and NF-κB in patients with rheumatoid arthritis. Adv Clin Exp Med 25:709-717.
• Tian H, Liu C, Zou X, Wu W, Zhang C and Youan D (2015) MiRNA-194 regulates palmitic acid-induced toll-like receptor 4 inflammatory responses in THP-1 cells. Nutrients 7:3483-3496.
• Tsukamoto M, Seta N, Yoshimoto K, Suzuki K, Yamaoka K and Takeuchi T (2017) CD14brightCD16+ intermediate monocytes are induced by interleukin-10 and positively correlate with disease activity in rheumatoid arthritis. Arthritis Res Ther 19:28.
• Vandesompele J, Preter KD, Pattyn F, Poppe B, Roy NV, Paepe AD and Speleman F (2002) Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol 3:RESEARCH0034.
• Vasudevan S, Tong Y and Steitz JA (2007) Switching from repression to activation: MicroRNAs can up-regulate translation. Science 318:1931-1934.
• Vasudevan S (2012) Posttranscriptional upregulation by MicroRNAs: Posttranscriptional upregulation by microRNAs. Wiley Interdiscip Rev RNA 3:311-330.
• Walsh MC, Lee J and Choi Y (2015) Tumor necrosis factor receptor-associated factor 6 (TRAF6) regulation of development, function, and homeostasis of the immune system. Immunol Rev 266:72-92.
• Wang H, Chen W, Wang L, Li F, Zhang C and Xu L (2015a) Tumor necrosis factor receptor-associated factor 6 promotes migration of rheumatoid arthritis fibroblast-like synoviocytes. Mol Med Rep 11:2761-2766.
• Wang W, Zhang Y, Zhu B, Duan T, Xu Q, Wang R, Lu L and Jiao Z (2015b) Plasma microRNA expression profiles in Chinese patients with rheumatoid arthritis. Oncotarget 6:42557-42568.
• Wu J, Ding J, Yang J, Guo X and Zheng Y (2018) MicroRNA roles in the nuclear factor Kappa B signaling pathway in cancer. Front Immunol 9:546.
• Yue P, Jing L, Zhao X, Zhu H and Teng J (2019) Down-regulation of taurine-up-regulated gene 1 attenuates inflammation by sponging miR-9-5p via targeting NF-κB1/p50 in multiple sclerosis. Life Sci 233:116731.
• Zhang S, Meng T, Tang C, Li S, Cai X, Wang D and Chen M (2020) MicroRNA-340-5p suppressed rheumatoid arthritis synovial fibroblast proliferation and induces apoptotic cell number by targeting signal transducers and activators of transcription 3. Autoimmunity 53:314-322.
• Zhu G, Lin C, Cheng Z, Wang Q, Hoffman RM, Singh SR, Huang Y, Zheng W, Yang S and Ye J (2020) TRAF6-mediated inflammatory cytokines secretion in LPS-induced colorectal cancer cells is regulated by miR-140. Cancer Genomics Proteomics 17:23-33.
• Zhu L-J, Dai L, Zheng D-H, Mo Y-Q, Ou-Yang X, Wei X-N, Shen J and Zhang B-Y (2012) Upregulation of tumor necrosis factor receptor-associated factor 6 correlated with synovitis severity in rheumatoid arthritis. Arthritis Res Ther 14:R133.
• Zhu L-J, Yang T-C, Wu Q, Yuan L-P, Chen Z-W, Luo M-H, Zeng H-O, He D-L and Mo C-J (2017) Tumor necrosis factor receptor-associated factor (TRAF) 6 inhibition mitigates the pro-inflammatory roles and proliferation of rheumatoid arthritis fibroblast-like synoviocytes. Cytokine 93:26-33.

Supplementary material

The following online material is available for this article:

Table S1 - Results of the main published studies on expression of TRAF6 and NFKB1 genes and miR-194-5p, miR-124-3p, miR-9-5p and miR-340-5p its implication for Rheumatoid arthritis

Edited by

Associate Editor:

Luis Mariano Polo

Publication Dates

• Publication in this collection
  26 July 2024
• Date of issue
  2024

History

• Received
  06 Oct 2023
• Accepted
  10 May 2024