The effect of glatiramer acetate, IFNβ-1a, fingolimod, and dimethyl fumarate on the expression of T-bet, IFN-γ, and MEG3 in PBMC of RRMS patients

Objective Multiple sclerosis (MS) is a progressing neurodegenerative disease marked by chronic central nervous system inflammation and degeneration.This study investigates gene expression profiles of T-box transcription factor TBX21 (T-bet), interferon-gamma (IFN-γ), and long non-coding RNA MEG3 in peripheral blood mononuclear cells (PBMCs) from treatment-naïve Relapsing-Remitting Multiple Sclerosis patients (RRMS), healthy controls, and RRMS patients on different Disease Modifying Therapies (DMTs). The aim is to understand the role of T-bet, IFN-γ, and MEG3 in MS pathogenesis and their potential as diagnostic and therapeutic targets. Results Elevated T-bet expression is observed in treatment-naïve RRMS patients compared to healthy individuals. RRMS patients treated with Interferon beta-1alpha (IFNβ-1a) and fingolimod exhibit downregulated T-bet and MEG3 expression levels, respectively, with more pronounced effects in females. Healthy individuals show a moderate positive correlation between T-bet and MEG3 and between IFN-γ and T-bet. In RRMS patients treated with Glatiramer Acetate (GA), a strong positive correlation is observed between MEG3 and IFN-γ. Remarkably, RRMS patients treated with Dimethyl Fumarate (DMF) exhibit a significant positive correlation between T-bet and MEG3. These findings underscore the diagnostic potential of T-bet in RRMS, warranting further exploration of MEG3, T-bet, and IFN-γ interplay in RRMS patients.


Introduction
MS is an autoimmune disease characterized by chronic central nervous system inflammation [1].The pathogenesis of MS is not fully understood, but evidence suggests that inflammation mediated by T-helper1 (Th1) and T-helper17 (Th17) cells plays a central role.Understanding T-cell biology and identifying diagnostic biomarkers related to MS can improve patient management [2][3][4][5][6].

Study subject
The research involved 52 patients and 20 healthy individuals selected at the Tabriz Neurology Clinic in Iran.Patients, aged 20 to 60, met McDonald's criteria (2017) [25], and had received at least two months of treatment without prior RRMS therapy.They were divided into five groups based on their treatments (GA-treated patients, fingolimod-treated patients, DMF-treated patients, IFNβ-1a-treated patients, Treatment-naïve patients).All patients were in remission during sampling.Exclusion criteria covered primary or secondary progressive MS, recent corticosteroid use, other inflammatory/ autoimmune diseases, and CNS degenerative conditions.
Informed written consent was obtained from all participants.The case and control groups were of the same ethnicity, and samples were collected simultaneously to minimize errors.The study's ethics code is IR.TBZMED.REC.1399.074.For age, gender, clinicopathological information, and treatment specifics, refer to Tables 1 and 2.

PBMC isolation
Peripheral blood samples (10 ml) were collected from each participant in heparinized vials.Then, density gradient centrifugation was used to separate mononuclear cells with lymphodex Ficoll (Inno-Train, Germany).

RNA extraction and cDNA synthesis
TRIzol (GeneAll Biotechnology CO., LTD, Korea) used to extract total RNA according to the manufacturer's instructions.After obtaining the quantitative and qualitative analysis of RNA with the Spectrophotometer (DeNovix Inc., USA), complementary DNA (cDNA) was synthesized using 2X RT-PCR Pre-Mix Taq cDNA synthesis kit (BioFact,Korea).

Quantitative real-time PCR
We quantified the expression of T-bet, MEG3, and IFN-γ by real-time PCR (Step One Plus, Thermo Fisher Scientific, USA) using 2x Master Mix SYBR Green (Amplicon, Denmark) and specific primers for each gene.The reaction volume was 10 µl, containing 5 µl of SYBR Green Master Mix (2×), 0.5 µl of each primer pair, 3.5 µl of ddH2O, and 1 µl of cDNA (100 ng/µl).The PCR program consisted of an initial denaturation step at 94 °C  for 13 min, followed by 45 cycles of denaturation at 94 °C for 10 s, annealing at 60 °C for T-bet and GAPDH, 58 °C for IFN-γ, or 57 °C for MEG3 for 30 s, and elongation at 72 °C for 20 s.The melting curves of all reactions were checked to verify the specificity of amplification.The expression levels of the target genes were normalized to GAPDH as the internal control.The primer sequences used are shown in (Table 3).

Statistical analysis
After

Differential gene expression in healthy controls, treated patients, and untreated RRMS patients GA-treated patients
T-bet expression is significantly higher in treatmentnaïve RRMS patients compared to controls (P = 0.0005).Furthermore, T-bet is upregulated in GA-treated RRMS patients (P = 0.03).However, no significant changes were observed in the expression levels of IFN-γ and MEG3 among the groups.(Fig. 1).

DMF-Treated patients
The expression of T-bet is significantly upregulated in both treatment-naïve patients and DMF-treated patients when compared to healthy controls (P = 0.01 and P = 0.02, respectively).However, no statistically significant changes were observed in the expression levels of IFN-γ and MEG3 among the three groups (Fig. 3).

IFNβ-1a -Treated patients
T-bet expression was notably higher in treatment-naïve RRMS individuals when compared to both controls and patients treated with IFNβ-1a (P = 0.035 and P = 0.037, respectively).However, there were no significant differences observed in the expression levels of IFN-γ and MEG3 among these groups.(Fig. 4).

Evaluation of T-bet, IFN-γ, and MEG3 expression in females with RRMS: a comparison between treated and nontreated patients
The impact of GA treatment on T-bet, IFN-γ, and MEG3 expression in female patients was found to be statistically non-significant (Fig. 5A).In contrast, IFNβ-1a treatment led to a significant reduction in the expression levels of T-bet, IFN-γ, and MEG3 (P-value = 0.004, P-value = 0.024, and P-value = 0.048, respectively) (Fig. 5B).Additionally, DMF treatment resulted in a significant downregulation of T-bet expression (P-value = 0.008) (Fig. 5C).while fingolimod treatment led to a significant downregulation of IFN-γ and MEG3 expression in female RRMS patients (P-value = 0.002 and P-value = 0.011, respectively) (Fig. 5D).

Discussion
The intricate web of gene expression patterns unveiled by this study within the context of RRMS offers a compelling insight into the multifaceted nature of the disease and its intricate responsiveness to diverse therapeutic interventions.A critical analysis of the results reveals both consistencies and discrepancies when compared to existing research, shedding light on the complex interplay between gene regulation and the pathophysiological processes underlying RRMS.
The identification of T-bet as a potential diagnostic marker for treatment-naïve RRMS patients aligns with previous studies that emphasize the role of T-bet in autoimmune responses [26,27].This finding is particularly noteworthy as it provides a promising avenue for the early identification of patients who may benefit from specific therapeutic strategies.
The observed positive correlations between T-bet and other genes (MEG3, IFN-γ) in healthy controls prompt an exploration into the regulatory interplay between these genes.This alignment is consistent with the known role of T-bet in orchestrating immune responses [28,29].However, the notable absence of similar correlations in treatment-naïve RRMS patients raises intriguing questions about the distinct immune dynamics at play in this subgroup.
A meticulous comparison of the gene correlations in DMT-treated patients underscores the intricate nature of their responses.While the study establishes correlations between T-bet and MEG3, and IFN-γ in these groups, the lack of such correlations in treatment-naïve patients poses a stark contrast.This discrepancy might be attributed to variations in immunological milieu observed between patients commencing Disease-Modifying Therapy (DMT) and those who remain treatment-naïve, possibly implicating distinct mechanisms of immune modulation [30].
Further analysis of gender-specific treatment responses unveils nuances in the effects of GA and IFNβ-1a.The lack of significant effects of GA on gene expression in female patients echoes its established immune-modulating mechanisms, which are less gender-dependent.Conversely, the substantial reduction of T-bet, IFN-γ, and MEG3 expression by IFNβ-1a aligns with its known anti-inflammatory properties and its capacity to modulate immune responses by reducing the expression levels of proinflammatory cytokines such as IFN-γ Comparisons with existing literature reveal a mix of consistencies and disparities suggesting that the effects of these medications on gene expression and gender-specific responses are complex and may vary in different studies or patient populations [24,31,32].The elevated expression of T-bet in RRMS patients, particularly those undergoing Fingolimod treatment, is in line with prior studies highlighting T-bet's role in inflammation.However, the unexpected downregulation of MEG3 in Fingolimodtreated patients contradicts some studies that underscore MEG3's potential as an immunomodulatory factor, High  MEG3 expression has been linked to immune hallmarks like Inflammatory Response and IFN-γ Response [20,33].These divergent results may stem from variations in study design, patient characteristics, or treatment durations.
The limitations of the study, including the inability to isolate Th1 cells, the relatively small sample size for male-focused analysis, and the modest representation of treatment-naïve RRMS patients, are transparently acknowledged.These limitations underscore the importance of considering the study's findings within the context of these constraints and highlight avenues for future research to address these gaps.this study offers a comprehensive exploration of gene expression dynamics in RRMS patients and their responses to diverse therapeutic approaches.Through a rigorous comparison of the study's outcomes with existing research, we reveal both alignments and divergences, underscoring the complex nature of RRMS pathophysiology.This study stands as a significant contribution to the field, providing valuable insights that contribute to our understanding of disease progression and treatment outcomes.Further investigations into the mechanistic underpinnings of gene interactions and their implications will be pivotal in refining therapeutic strategies and deepening our comprehension of RRMS complexities.

Conclusion
The study sheds light on the interplay of gene expression patterns in RRMS, enhancing our understanding of immune dysregulation.It provides insights into T-bet, MEG3, and IFN-γ dynamics across diverse treatments, with T-bet being a potential diagnostic biomarker for RRMS.This research lays a foundation for refined therapeutic strategies and deepens our comprehension of RRMS intricacies for improved patient outcomes.

Table 1
demographic and clinical features of study subjects

Table 2
Treatment and gender classification conducting real-time PCR and obtaining Ct values for samples, gene expression levels in RRMS patients compared to healthy controls were determined by calculating 2^(-∆Ct) in Excel.Statistical analysis was performed using GraphPad Prism 9.0.0.Variables were analyzed using the Shapiro-Wilk normality test.Data were presented as mean ± standard errors of the mean (SEM) or median, IQR, and range.Group comparisons utilized the Kruskal-Wallis nonparametric test.Correlation analysis employed Spearman and Pearson methods based on normality test results.Diagnostic significance was assessed using ROC curves for gene expressions in treatment-naïve patients and healthy controls.A p-value < 0.05 (95% CI) indicated statistical significance.

Table 3
The sequences of used primer

Table 4
Correlation between the genes expressed in different groups

Table 5
ROC curve statistical evaluation for biomarker efficacy in MS patients