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Longitudinal assessment of immuno-metabolic parameters in multiple sclerosis patients during treatment with glatiramer acetate

Metabolism (Available online 8 May 2015)

Abstract

Objective

We investigated the effect of glatiramer acetate (GA) on the modulation of immune cell subpopulations and serum levels of multiple immune/metabolic markers in patients with relapsing–remitting multiple sclerosis (RRMS) to understand whether the treatment with GA could induce a specific change in the immunometabolic asset of patients with RRMS.

Material and methods

We performed an extensive peripheral blood immunophenotyping and measured serum levels of several parameters involved in the pathogenesis of RRMS and also relevant in the pathogenesis of metabolic syndrome and obesity such as leptin, soluble leptin-receptor (sLep-R), myeloperoxidase (MPO), soluble CD40 ligand (sCD40-L), soluble tumor necrosis factor-receptor (sTNF-R), monocyte chemoattractant protein 1 (MCP-1), soluble Inter-Cellular Adhesion Molecule-1 (sICAM-1) and osteoprotegerin (OPG), in 20 naïve-to-treatment RRMS patients and 20 healthy controls. We repeated these analyses over time at 6 and 12 months after starting GA treatment.

Results

Our analysis showed that naïve-to-treatment RRMS patients had a lower number of CD16+CD56+ NK cells, CD19+ B cells, CD4+ T cells co-expressing the MHC class II activation marker HLA-DR (CD4+DR+) and naïve CD4+CD45RA+ T cells in basal conditions. GA treatment induced a specific and significant decrease of circulating CD19+ B cells. Naïve-to-treatment RRMS patients also showed a significantly higher number of CD4+ T cells with a memory phenotype (CD4+CD45RO+) whose peripheral frequency was not affected by GA treatment. These changes over time associated with a higher serum concentration of leptin and lower levels of MPO. GA treatment also reduced significantly the circulating levels of sCD40-L and sTNF-R overtime.

Conclusions

Our data suggest that the clinical outcome of GA treatment is associated with changes in immune cell subpopulations and modulation of specific immunometabolic markers. These data add substantial evidence of the immune modulating effect of GA during RRMS and could be of relevance in understanding the pathogenesis of disease and its follow-up.

Abbreviations: GA - glatiramer acetate, RRMS - relapsing–remitting multiple sclerosis, sLep-R - soluble leptin-receptor, MPO - myeloperoxidase, sCD40-L - soluble CD40 ligand, sTNF-R - soluble tumor necrosis factor-receptor, MCP-1 - monocyte chemoattractant protein 1, sICAM-1 - soluble inter-cellular adhesion molecule 1, OPG - osteoprotegerin, CNS - central nervous system, IFN - interferon, APL - altered peptide ligand, MBP - myelin basic protein, TGF - transforming growth factor, Treg - regulatory T, EAE - autoimmune encephalomyelitis, CSF - cerebrospinal fluid.

Keywords: Metabolism, Multiple sclerosis, Glatiramer acetate.

1. Introduction

Multiple sclerosis (MS) is an immune-mediated demyelinating disease of central nervous system (CNS) characterized by progressive neurodegeneration caused by an autoimmune response against self-antigens in individuals that are genetically susceptible [1] . Patients often display an initial clinically isolated syndrome, followed by a series of subacute clinical events that spontaneously decrease. In this case it refers to disease defined as relapsing remitting MS (RRMS) [2] .

Glatiramer acetate (GA) represents, together with the family of beta-interferons (IFN)-β1α and 1β the first line agents for treatment of RRMS [3] . The mechanism of action of GA has been extensively investigated and it seems to be particularly related to its capacity to induce immune-deviation of the anti-myelin autoimmune response at different levels. It seems to act as an altered peptide ligand (APL) and inhibit activation of myelin basic protein (MBP)-specific T cells. Moreover, it has been described that treatment of RRMS patients with GA results in an induction of protective/regulatory cytokines such as interleukin (IL)-10, IL-4, and transforming growth factor-β (TGF-β) [4] . In addition, several studies have been reported that GA treatment increases the expression of the forkhead box P3 (FoxP3), the master gene of regulatory T (Treg) cells [4] , a cellular subset involved in the control of immune responses and in the prevention of autoimmune diseases.

Previous analyses of Treg cells frequency in MS patients have provided conflicting results indicating no differences between MS patients and healthy controls [5] and [6], or a decreased level in MS patients [7] and [8]. Others have reported, that they find a disturbance in the development or function, or both, of Treg subpopulations [5], [6], and [9]. Recent evidence indicates that metabolism controls T cell activation and loss of immune tolerance; in this context circulating factors at the interface between immunity and metabolism such as leptin, adiponectin, ghrelin and neuropeptide Y can alter susceptibility to experimental autoimmune encephalomyelitis (EAE) a mouse model of MS [10], [11], and [12]. For example, the secretion of the adipocyte-derived leptin is generally increased in serum and cerebrospinal fluid (CSF) of naïve-to-treatment RRMS patients and correlates not only with the levels of peripheral blood Treg cells but also with disease severity and susceptibility [13] . In this context it has been shown that leptin controls both innate and adaptive immunity and, in particular, acts as a proinflammatory cytokine by inhibiting the proliferation of Treg cells and sustaining effector T cells activation and functions [14] ; this evidence suggests its potential role in the pathogenesis of several autoimmune and inflammatory diseases. In agreement with these data, it is well accepted that the growing worldwide obesity epidemic has several damaging effect on public health, with regard not only to cardiovascular disease, diabetes mellitus type 2, sleep apnea, and osteoarthritis but also autoimmune diseases such as RRMS [15] . Indeed, a series of recent reports indicates that the excessive body weight is associated with increased risk of RRMS suggesting a very strong link between metabolic factors and immune system responses [16], [17], and [18]. In particular, it is now well established that epidemic childhood obesity is likely to lead to increased incidence of RRMS, especially in adolescent girl [19] since it has been reported that there is a twofold increased risk of developing MS among subjects with a BMI ≥ 30 kg/m2 at age 18–20 years, compared with normal weight subjects. In addition, it has been shown that the risk for MS is related to interactions between human leukocyte antigen (HLA) genotype and body mass index (BMI) status [17] . Indeed many genes have been identified for predisposition to MS, such as HLA-DRB1*15 allele conferring a threefold higher risk, while protective effect, with a twofold lower risk, was driven mainly by the HLA-A*02 [20] . It has been highlighted that there are a significant correlation between HLA-DRB1*15 and obesity, regardless of HLA-A*02 and at the same time a significant interaction between the absence of HLA-A*02 and obesity, regardless HLA-DRB1*15. Subjects with a BMI < 27 kg/m2 and the two risk genotypes (carriage of DRB1*15 and absence of A*02) displayed an OR = 5.1–5.7 whereas the same genotype for subjects with BMI ≥ 27 kg/m2 rendered an OR = 13.8–16.2 indicating the presence of interactions between BMI status and HLA genotype with regard to MS risk. This could be explained by assuming that the low-grade inflammatory state, correlated with obesity, synergizes with the adaptive, HLA molecule-restricted arm of the immune system, causing MS. Although the strong link among obesity, low-grade inflammatory state, and HLA-susceptibility could at least in part explain the increased number of cases of autoimmune diseases in the last few years, the factors that correlate the increased body weight with the increased risk of MS development are still unknown. In this context adipose tissue is now considered as an endocrine organ involved in the regulation of both physiologic and pathologic processes including inflammation. Indeed it has been shown that it secretes a series of metabolites called adipokines, including cytokines and chemokines. The release of adipokines such as tumor necrosis factor (TNF)-α, IL-6, and monocyte chemoattractant protein 1 (MCP-1) leads to a chronic inflammatory state that could play a central role in the increased risk of autoimmune disease [21] . Understanding the impact and the role of these parameters that link obesity, metabolism and immune tolerance mechanisms could lead to the identification of new markers for the monitoring of disease progression and response to treatment.

In this study we analyzed over time the immune/metabolic assessment in terms of peripheral blood immune cells number and the levels of several relevant metabolic parameters involved in the mechanisms leading to metabolic syndrome and obesity in RRMS patients, before and after GA treatment, to dissect whether there are specific metabolic changes associated with loss of immunological tolerance during RRMS, and whether GA treatment could have an impact on the modulation of immunometabolic profile of RRMS patients.

2. Methods

2.1. Study Design, Patients and Healthy Controls

This is a longitudinal, observational study. We included in the study 20 patients with RR-MS defined according to the criteria of McDonald et al. [22] and 20 age-, gender- and BMI-matched healthy controls (Patients’ and healthy controls’ basic demographic and clinical features are shown in Table 1 ). All RRMS patients were naïve-to-treatment and had an expanded disability status scale (EDSS) score of 1.1 ± 0.9. RRMS patients’ blood samples were collected at diagnosis, before starting GA treatment, and during GA treatment at 6 and 12 months. The institutional ethics committee of Università di Napoli “Federico II” approved the study and all individuals gave written informed consent.

Table 1 Clinical and anthropometric characteristics of RRMS patients and healthy controls.

  RRMS patients Healthy controls p
No. of subjects 20 20
Sex (male/female) 9/11 10/10
Age, yr 38.1 ± 8.2 39.2 ± 14.4 ns
Height, m 1.71 ± 0.08 1.68 ± 0.07 ns
Mass, kg 75.6 ± 7.6 74.4 ± 10.7 ns
BMI, kg/m2 26.4 ± 2.3 25.4 ± 3.2 ns
EDSS score 1.1 ± 0.9

Abbreviations: EDSS = Expanded Disability Status Scale; RRMS = Relapsing remitting multiple sclerosis; BMI = Body mass index. (Data are expressed as mean value ± SD).

2.2. Immunophenotypic Analysis

Immunophenotypic analysis of peripheral blood of RRMS patients and healthy controls was performed with an EPICS XL flow cytometer (Beckman Coulter) using the Beckman Coulter software program XL system II. Triple combinations of different anti-human mAbs, e.g., FITC- and phycoerythrin (PE)-anti-CD3, PE- and PC-5-anti-CD4, PC5-anti-CD8, PE-anti-CD16, PC5-anti-CD19, PE-anti-CD25, FITC-anti-CD45, and PE-anti-CD56 (all from Coulter Immunotech, Marseille, France), were used for immunofluorescence staining and to identify different cell populations.

2.3. Fluorescent Bead-Based Immunoassay

All serum samples from RRMS patients and controls were obtained via centrifugation and stored at − 80 °C before the analysis. Human obesity 9plex kit (Bender MedSystems, Burlingame, CA, USA) was used to perform the bead based Analyte Detection Assay for quantitative detection of soluble CD40 ligand (sCD40L), soluble ICAM-1 (sICAM-1), monocyte chemoattractant protein-1 (MCP-1), myeloperoxidase (MPO), osteoprotegerin (OPG) and sTNF-R by Flow Cytometry. Preparation of the samples was performed according to manufacturer's instructions. A 96-well plate format was used, including two eight-point standard curves (inclusive blank). Concentrations of analytes were proportional to fluorescent intensity measured on a BD FACSCanto (BD Biosciences). Data were acquired using a BD FACSCanto (BDBiosciences) and analyzed using FlowCytomixPro 2.2 Software (Bender MedSystems, Burlingame, CA, USA).

2.4. Leptin and SLep-R Measurement

Circulating leptin and soluble leptin receptor (sLepR) were determined in duplicate serum samples using human Leptin and human Leptin sR Immunoassays, respectively (R&D System, Minneapolis, MN, USA).

2.5. Statistical Analysis

Comparison between RRMS patients and healthy controls was evaluated using an unpaired Student’s T test while comparison between time point 0 (patients at diagnosis), 1 (after 6 months of GA treatment) and 2 (after 12 months of GA treatment) was evaluated using a paired Student’s T test. Statistical analyses were performed using the program GraphPad InStat3 (Abacus Concepts, Cary, NC).

Spearman correlation coefficient was computed to investigate the statistical correlations between the different variables. Due to the large number of correlations involved and in order to control the family-wise error rate at level α, the adaptive Bonferroni procedure [23] and [24] was used. The Spearman correlation coefficient was computed to investigate the biological correlations. p < 0.05 was considered statistically significant.

3. Results

3.1. Immunophenotypic Analysis of Healthy Controls and RRMS Subjects at Diagnosis and After GA Treatment

To evaluate possible differences in the immunological profile between patients with RRMS and control subjects, we analyzed the absolute number and the percentage of several immune cell subpopulations in the peripheral blood of 20 naïve-to-treatment RRMS patients at diagnosis and we compared these values with those of 20 age-, gender-, and BMI-matched healthy control subjects (Supplemental Table 1 and Supplemental Table 2). We repeated these analyses in the same RRMS patients after 6 and 12 months starting GA treatment (Supplemental Table 1 and Supplemental Table 2). We observed that naïve-to-treatment RRMS patients showed a significantly lower number and percentage of CD16+ CD56+ NK cells ( Fig. 1 B and Supplemental Fig. 1 D) and a lower number of CD4+ T cells co-expressing the activation marker HLA-DR (CD4+HLA-DR+) as compared to healthy control subjects ( Fig. 1 E). In addition, naïve-to-treatment RRMS patients had a significantly higher percentage of CD3+ and CD4+ T cells with respect to healthy controls ( Supplemental Fig. 1 A and B respectively). In particular we observed that at diagnosis RRMS patients showed a higher number of memory CD4+CD45RO+ cells ( Fig. 1 G) and a higher percentage of both CD3+ and CD4+ T cells with a memory phenotype (both CD3+CD45RO+ and CD4+CD45RO+ cells, respectively) ( Supplemental Fig. 1 E and F) when compared with healthy controls. Treatment with GA did not alter these differences as the percentage of CD3+, CD4+ and percentage and the number of CD3+CD45RO+ and CD4+CD45RO+ T cells were always higher in patients than in controls over time after starting pharmacological treatment ( Supplemental Fig. 1 A, B, E and F and Fig. 1 D and G). Concomitantly, naïve-to-treatment RRMS patients showed a significantly lower number and percentage of naïve CD4+ (CD4+CD45RA+) T cells at diagnosis ( Fig. 1 F and Supplemental Fig. 1 G). It was found that RRMS patients showed a significantly lower number of CD19+ B cells ( Fig. 1 C) and, during GA treatment, there was a significant further reduction of number and percentage of B cells over time ( Fig. 1 C and Supplemental Fig. 1 C). Finally, GA treatment increased the percentage of CD4+ T cells expressing the co-stimulation molecule CD28 (CD4+CD28+) ( Supplemental Fig. 1 H).

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Fig. 1 Peripheral blood immunophenotype of RRMS patients and healthy controls. Absolute number of immune cell subpopulations analyzed in 20 healthy controls and 20 RRMS patients at diagnosis (naïve-to-treatment) and after 6 (time point 1) and 12 (time point 2) months of GA treatment. Comparisons between RRMS patients and healthy controls were evaluated using the unpaired t-test (two tails), while comparisons within RRMS subjects were evaluated using repeated-measures ANOVA model with post hoc paired t test with the Sidak adjustment for multiple comparison (mean + S.E.M.).

3.2. Serum Immunometabolic Parameters of Healthy Controls and RRMS Subjects at Diagnosis and After GA Treatment

To characterize the immunometabolic profile of RRMS patients, the levels of several markers involved in the pathogenesis of obesity and inflammation such as leptin, soluble leptin-receptor (sLep-R), myeloperoxidase (MPO), soluble CD40 ligand (sCD40-L), soluble tumor necrosis factor-receptor (sTNF-R), MCP-1, soluble Inter-Cellular Adhesion Molecule-1 (sICAM-1) and osteoprotegerin (OPG), were analyzed ( Fig. 2 ). We confirmed that naïve-to-treatment RRMS patients at diagnosis showed significantly higher levels of circulating leptin ( Fig. 2 A) as compared with those in control subjects. In addition, we observed that MPO and sICAM-1 were lower in RRMS patients when compared with healthy controls and GA treatment did not change these concentrations over time ( Fig. 2 G and H). We also observed that after GA treatment, there was a decrease in the plasma levels of both sCD40-L and sTNF-R over time ( Fig. 2 C and D). Finally, there was no difference between patients and controls in serum levels of sLepR, OPG and MCP-1 ( Fig. 2 B, E and F).

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Fig. 2 Immunometabolic parameters in RRMS patients and healthy controls. Serum level of different immune/metabolic parameters analyzed in 15 healthy controls and 14 RRMS patients at diagnosis (naïve-to-treatmnet) and after 6 (time point 1) and 12 (time point 2) months of GA treatment. Comparisons between RRMS patients and healthy controls were evaluated using the unpaired t-test (two tails), while comparisons within RRMS subjects were evaluated using repeated-measures ANOVA model with post hoc paired t test with the Sidak adjustment for multiple comparison (mean + S.E.M.).

3.3. Multiple Correlation Analysis Among Immunometabolic Markers in Healthy Controls and RRMS Patients

We next performed a multiple correlation analyses (Spearman rho non parametric correlation) among the different cellular subpopulations and the immunologic and metabolic parameters analyzed in the study in controls group and in the group of RRMS patients at diagnosis and after 6 and 12 months of GA treatment ( Fig. 3 ). With regard to cell subpopulations, a different profile of correlations was observed between control subjects ( Fig. 3 A and Supplemental Table 3 ) and naïve-to-treatment patients at diagnosis ( Fig. 3 B and Supplemental Table 4 ). In addition several correlations were found only in patients at diagnosis ( Fig. 3 B and Supplemental Table 4 ) and not after 6 ( Fig. 3 C and Supplemental Table 5 ) and 12 months ( Fig. 3 D and Supplemental Table 6 ) of GA treatment. For example only in the group of healthy controls did we observe a positive correlation between the number of CD19+ B cells and the number of total lymphocytes (r = 0.91, p < 0.001) and between CD19+ B cells and CD3+ T cells (r = 0.85, p < 0.001). These correlations were not present in patients either before or after GA treatment.

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Fig. 3 Graphical representation of the Spearman rho non parametric correlation matrix among the analyzed variables in RRMS patients and healthy controls. Correlations in (A) control subjects (B) RRMS patients at diagnosis (naïve-to-treatment) (C) RRMS patients after 6 months of GA treatment, (D) RRMS patients after 12 months of GA treatment. The presence of a significant correlation between two variables is expressed by means of a blue (positive correlation) line. The colour intensity and the thickness of each line are proportional to the level of correlation.

In addition, in RRMS patients at diagnosis the number of CD4+ T cells with a memory phenotype (CD4+CD45RO+) positively correlated with number of leukocytes (r = 0.85, p < 0.001), lymphocytes (r = 0.91, p < 0.001), CD3+ (r = 0.81, p < 0.001) and CD4+ T cells (r = 0.90, p < 0.001) ( Fig. 3 B). These specific correlations were not observed in healthy controls ( Fig. 3 A) and disappeared in patients after GA treatment ( Fig. 3 C and D). We also observed that in healthy controls there was a positive correlation between CD4+HLA-DR+ and CD4+ T cells (r = 0.78, p < 0.001) ( Fig. 3 A). This correlation was not observed in naïve-to-treatment RRMS patients ( Fig. 3 B), probably because there was a decrease in the number of CD4+HLA-DR+ T cells in MS patients with respect to healthy controls ( Fig. 1 E). Finally, we observed, after 12 months of GA treatment, a positive correlation between CD4+CD25+ Treg cells and total number of lymphocytes (r = 0.81, p < 0.001) in RRMS patients ( Fig. 3 D).

In terms of immunologic and metabolic parameters, we found that in the control group the levels of these parameters were not correlated with the number of immune cell populations and, in addition, the metabolic markers levels are not correlated one with the other ( Fig. 3 A). In naïve-to-treatment RRMS patients circulating MCP-1 levels positively correlated with sTNF-R levels (r = 0.84, p < 0.001). This correlation was not present in patients after GA treatment. In addition, in naïve-to-treatment RRMS patients MPO positively correlated with sICAM-1 (r = 0.85, p < 0.001). This correlation was present even after 6 months of GA treatment (r = 0.86, p < 0.001) but disappeared after 12 months. The number of CD3+ T cells with a memory phenotype (CD3+CD45RO+) positively correlated with the levels of sCD40-L in 6 months treated RRMS patients (r = 0.89, p < 0.001) ( Fig. 3 C) and, finally the levels of sCD40-L correlated with OPG (r = 0.86, p < 0.001) in patients after 12 months of GA treatment ( Fig. 3 D).

4. Discussion

Over the last few years a great interest has been directed towards the study of the immunomodulating capacity of GA during MS course. This is an exploratory study, where we investigated the impact of GA treatment over time not only on the modulation of immune-phenotype but also on the levels of different parameters involved in metabolic control of multiple biologic processes. In addition the correlation matrix provided information on the interaction between immune and metabolic system and on how these interactions change overtime after GA treatment.

We observed that naïve-to-treatment RRMS patients showed a significantly lower number of B cells in peripheral blood. This could be related to a redistribution of this population and its migration into the CNS. However, GA treatment further reduced the number and the percentage of B cells over time. It remains to be established which B-cell pools, proinflammatory or immunosuppressive, are targeted by GA treatment over time. In addition, our results further support the concept of RRMS as an autoimmune disease that is mediated not only by myelin-reactive T cells but also by B cells [25] . This discloses a possible scenario that links abnormal B-cell activation to T-cell-mediated immunopathology. However, there are some contrasting results since B cells can either promote or inhibit CNS inflammation. Indeed on one hand a hallmark of RRMS patients is the presence of cerebrospinal fluid (CSF) specific oligoclonal IgG and in addition it has been shown that clonally expanded B cells are present in the CNS and CSF of MS patients’ brain. On the other hand different subsets of B cells with immunosuppressive/regulatory functions have been described [26] . We also found that RRMS patients showed a significantly higher number of T cells with a memory phenotype and a lower number of naïve T cells. These data are in agreement with previously published results. Indeed, it has been reported that in RRMS patients there is a change in memory T cell populations [27] since autoantigen stimulation leads to an increase of memory population and a decreased pool of naïve T cells. However, no significant differences were observed in the analysis of these populations in RRMS patients over time after GA treatment indicating that this treatment has no effect on the modulation of these cellular subpopulations.

It has been shown that CD40-L is a T helper (Th)1/inflammatory marker [28] and CD40-L is expressed on the surface of activated CD4+ T cells, basophils, and mast cells. The activation of CD40 by its ligand, leads to B cell activation and proliferation. In addition the soluble form of CD40-L is able to rescue B cells from apoptosis. Several data indicated that the level of sCD40-L is increased in overweight and obese people [29] further suggesting that there is a link between metabolism and immune system functions. We observed that after GA treatment there is a significant reduction of sCD40-L levels in RRMS patients; this reduction is in line with the notion that this treatment has an impact also on inflammation. The analysis of the other immune-metabolic parameters revealed that in RRMS patients there was a significantly lower level of MPO with respect to healthy controls and their values remained unchanged after GA treatment. This phenomenon could be possibly related to the limit of our analysis that is restricted to the evaluation of the levels of this cytokine only in the serum and not in the CSF. Indeed it has been previously reported that the levels of active MPO are elevated in the MS patients’ white matter – in demyelinated more so than in non-demyelinated – when compared with healthy controls’ white matter. In light of these data, future investigations are required to verify whether there could be different levels of MPO in the CSF of controls and MS patients.

Metabolically, it has been reported that leptin could represent one of the major link between immune and metabolic system probably through its action on the proliferation of Teffector and Treg cells [30] and [31]. In this context, several studies in human and mouse revealed that leptin levels associated with autoimmune diseases, thus suggesting a potential role of leptin in immune homeostasis and in disorders characterized by a loss of immune tolerance such as MS, inflammatory bowel disease (IBD), rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), type 1 diabetes (T1D), psoriasis and autoimmune thyroiditis [32] . We confirmed that naïve-to-treatment RRMS patients had higher serum level of leptin when compared to healthy controls. After GA treatment we did not observe a significant variation of this adipokine level over time.

We also evaluated peripheral levels of sLepR, the main leptin-binding protein in human blood. Biologically sLepR can modulate leptin effects on cells target either by inhibiting the binding of leptin to its membrane receptors or by increasing the availability of circulating leptin, delaying its clearance [33] . We observed no significant difference in sLepR levels either between controls and RRMS patients or in RRMS patients before and after GA treatment. These data indicate that the higher leptin levels observed in RRMS patients were associated with a different concentration of sLepR that could control leptin functions and half-life.

In addition, GA treatment reduced serum levels sTNF-R over time. The role of this molecule in the pathogenesis of RRMS remains still unclear so far, but our results are consistent with data showing that sTNF-R might prevent TNF-α inactivation [34] , thus suggesting that GA treatment might improve RRMS course, by downmodulation of this pro-inflammatory cytokine. Finally, in order to evaluate the alterations occurring during RRMS and the differences characterizing RRMS patients after GA treatment, we performed a multiple Spearman rho non-parametric correlation matrix among the immunologic and metabolic molecular variables analyzed in the study in both healthy controls and patients. We found a marked difference between controls and patients indicating that a series of mechanisms of control is altered during disease compromising the correlations between immune and metabolic system that are physiologically present at “equilibrium” in healthy individuals. Moreover, the analysis revealed that GA treatment associated with further changes of these correlations, one of which is with regard to Treg cells. Indeed, the correlation analysis showed, in RRMS patients, a positive correlation between Treg cells and total number of lymphocytes, only after 12 months of GA treatment, indicating that, after pharmacological treatment, the modulation of lymphocytes number was directly correlated to the number of Treg cells.

In addition, we observed that while in healthy controls correlations between immune cellular populations and metabolic parameters were not present, we found that in RRMS patients after 6 months of GA treatment there was a positive correlation between memory CD3+ T cells and the levels of sCD40-L. While in naïve-to-treatment RRMS patients circulating MCP-1 positively correlated with sTNF-R and MPO positively correlated with sICAM-1, in patients after GA treatment these correlations were lost. Although we found a number of statistically significant differences between MS patients and healthy controls in terms of immune cell subpopulations and immune/metabolic markers, some data such as OPG or MCP-1 levels might be significant in a larger cohort of subjects. However, given the exploratory nature of this study, it should contribute to generate novel investigations linking metabolism to the pathogenesis of MS.

5. Conclusions

Our data indicate that there is a marked alteration of mechanisms of cross-talk between immune and metabolic system during RRMS. GA treatment is able to affect these parameters and their interactions. Unfortunately, despite the efficacy of the treatment overtime and its capacity to reduce the number of relapses, it is not able to fully restore the “equilibrium” of the interactions among these immunometabolic parameters at a level similar to that observed in healthy control subjects.

Author Contributions

P. B. Carrieri, F. Carbone, S. Montella, M. Petracca, C. La Rocca and M. Galgani performed the study. F. Perna performed Flow cytometry experiments and analyses. D. Bruzzese performed statistical analysis. C. Florio, G.T. Maniscalco, D.Spitalieri, G.Iuliano, S. Bonavita and G. Tedeschi enrolled the patients and performed the follow up. G. Matarese supervised the study. P.B Carrieri, F. Carbone and G. Matarese wrote the paper and analyzed the data.

Funding

This study was supported by grants from the Fondazione Italiana Sclerosi Multipla (FISM) no. 2012/R/11, EU Ideas Programme, ERC-StG “menTORingTregs” no. 310496, FIRB MERIT Grant no. RBNE08HWLZ_015, and a CNR Medicina Personalizzata Grant. M. P. is supported by a research fellowship from Fondazione Italiana Sclerosi Multipla (FISM) no. 2013/B/7.

Disclosure Statement

All authors have approved the final article.

The following are the supplementary data related to this article.

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Supplemental Fig. 1 Peripheral blood immunophenotype of healthy controls and RRMS patients. Percentage of immune cell subpopulations analyzed in 20 healthy control subjects and 20 RRMS patients at diagnosis (naïve-to-treatment) and after 6 and 12 months of GA treatment. *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001. All values are expressed as percentage of total lymphocytes.

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Supplemental Table 1 Number of immune cell populations (cells per m3) analyzed in 20 healthy controls and 20 RRMS patients at diagnosis (naïve-to-treatment) and after 6 and 12 months of glatiramer acetate (GA) treatment. Data are expressed as mean value ± SD.

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Supplemental Table 2 Percentage of Immune cell populations analyzed in 20 healthy controls and 20 RRMS patients at diagnosis (naïve-to-treatment) and after 6 and 12 months of glatiramer acetate (GA) treatment. Data are expressed as mean value ± SD. All percentage values are expressed as % of total lymphocytes.

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Supplemental Table 3 List of significant Spearman rho non parametric correlation observed in healthy controls.

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Supplemental Table 4 List of significant Spearman rho non parametric correlation observed in RRMS patients at diagnosis (naïve-to-treatment).

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Supplemental Table 5 List of significant Spearman rho non parametric correlation observed in RRMS patients after 6 months of GA treatment.

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Supplemental Table 6 List of significant Spearman rho non parametric correlation observed in RRMS patients after 12 months of GA treatment.

Acknowledgments

The authors wish to thank Mr. Salvatore De Simone and Mrs. Maria Rosaria Montagna for technical assistance; the paper is dedicated to the memory of Serafino Zappacosta and Eugenia Papa.

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Footnotes

a Dipartimento di Neuroscienze, Scienze Riproduttive ed Odontostomatologiche, Università di Napoli “Federico II”, Napoli, Italy

b Laboratorio di Immunologia, Istituto di Endocrinologia e Oncologia Sperimentale, Consiglio Nazionale delle Ricerche (IEOS-CNR), c/o Dipartimento di Medicina Molecolare e Biotecnologie Mediche, Università di Napoli “Federico II”, Napoli, Italy

c Dipartimento di Medicina Clinica e Chirurgia, Università di Napoli “Federico II”, Napoli, Italy

d Dipartimento di Sanità Pubblica, Università di Napoli “Federico II”, Napoli, Italy

e Department of Neurology, Icahn School of Medicine at Mount Sinai, NY, USA

f Dipartimento di Neurologia, Azienda Ospedaliera di Rilievo Nazionale Cardarelli, Napoli, Italy

g Unità Operativa Complessa di Neurologia, Azienda Ospedaliera di Rilevo Nazionale S. Giuseppe Moscati, Avellino, Italy

h Dipartimento di Neuroscienze, Unità di Malattie Demielinizzanti, Azienda Ospedaliera Universitaria S. Giovanni di Dio e Ruggi d'Aragona, Salerno, Italy

i Dipartimento di Scienze Mediche, Chirurgiche, Neurologiche, Metaboliche e dell’Invecchiamento, Seconda Università di Napoli, Napoli, Italy

j Dipartimento di Medicina e Chirurgia, Facoltà di Medicina e Chirurgia, Università di Salerno, Baronissi Campus, Baronissi, Salerno, Italy

k IRCCS MultiMedica, Milano, Italy

Corresponding author at: Dipartimento di Medicina e Chirurgia, Facoltà di Medicina e Chirurgia, Università di Salerno, Baronissi Campus, via S. Allende, Baronissi 84081, Salerno, Italy. Tel.: + 39 0817464580; fax: + 39 0817463252.

1 These authors contributed equally to this work.

The authors have no conflicts of interest.


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    Timothy Vartanian, Professor at the Brain and Mind Research Institute and the Department of Neurology, Weill Cornell Medical College, Cornell...
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    Claire S. Riley, MD is an assistant attending neurologist and assistant professor of neurology in the Neurological Institute, Columbia University,...
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    Rebecca Farber, MD is an attending neurologist and assistant professor of neurology at the Neurological Institute, Columbia University, in New...

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