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Article

Interleukin 6 SNP rs1818879 Regulates Radiological and Inflammatory Activity in Multiple Sclerosis

by
Antonio Bruno
1,
Ettore Dolcetti
1,
Federica Azzolini
1,
Alessandro Moscatelli
2,3,
Stefano Gambardella
1,4,
Rosangela Ferese
1,
Francesca Romana Rizzo
1,
Luana Gilio
1,
Ennio Iezzi
1,
Giovanni Galifi
1,
Angela Borrelli
1,
Fabio Buttari
1,
Roberto Furlan
5,
Annamaria Finardi
5,
Francesca De Vito
1,
Alessandra Musella
6,7,
Livia Guadalupi
2,6,
Georgia Mandolesi
6,7,
Diego Centonze
1,2,* and
Mario Stampanoni Bassi
1
1
IRCSS Neuromed, 86077 Pozzilli, Italy
2
Department of Systems Medicine, Tor Vergata University, 00133 Rome, Italy
3
Laboratory of Neuromotor Physiology, IRCSS Fondazione Santa Lucia, 00179 Rome, Italy
4
Department of Biomolecular Sciences, University of Urbino “Carlo Bo”, 61029 Urbino, Italy
5
Clinical Neuroimmunology Unit, Institute of Experimental Neurology (INSpe), Division of Neuroscience, San Raffaele Scientific Institute, 20121 Milan, Italy
6
Synaptic Immunopathology Lab, IRCCS San Raffaele Rome, 00163 Rome, Italy
7
Department of Human Sciences and Quality of Life Promotion, University of Rome San Raffaele, 00163 Rome, Italy
*
Author to whom correspondence should be addressed.
Genes 2022, 13(5), 897; https://0-doi-org.brum.beds.ac.uk/10.3390/genes13050897
Submission received: 31 March 2022 / Revised: 9 May 2022 / Accepted: 14 May 2022 / Published: 17 May 2022
(This article belongs to the Special Issue Genetics and Biomarkers of Neurodegeneration)
Browse Figure
Review Reports Versions Notes

Abstract

:
(1) Background: The clinical course of multiple sclerosis (MS) is critically influenced by the expression of different pro-inflammatory and anti-inflammatory cytokines. Interleukin 6 (IL-6) represents a major inflammatory molecule previously associated with exacerbated disease activity in relapsing remitting MS (RR-MS); however, the role of single-nucleotide polymorphisms (SNPs) in the IL-6 gene has not been fully elucidated in MS. (2) Methods: We explored in a cohort of 171 RR-MS patients, at the time of diagnosis, the associations between four IL-6 SNPs (rs1818879, rs1554606, rs1800797, and rs1474347), CSF inflammation, and clinical presentation. (3) Results: Using principal component analysis and logistic regression analysis we identified an association between rs1818879, radiological activity, and a set of cytokines, including the IL-1β, IL-9, IL-10, and IL-13. No significant associations were found between other SNPs and clinical or inflammatory parameters. (4) Conclusions: The association between the rs1818879 polymorphism and subclinical neuroinflammatory activity suggests that interindividual differences in the IL-6 gene might influence the immune activation profile in MS.

1. Introduction

Multiple sclerosis (MS) is a central nervous system (CNS) disease caused by an autoimmune chronic inflammation [1]. Inflammatory mediators play a key role in the pathophysiology of MS by promoting blood–brain barrier (BBB) damage, migration of innate and adaptive immune cells, and activation of neuroinflammatory cascade in the CNS [2]. Cytokines are a heterogeneous group of polypeptides that includes chemokines, lymphokines, interferons (IFNs), and growth factors involved in both pro-inflammatory and anti-inflammatory processes [2]. Cytokines, released by both peripheral and CNS resident immune cells, interact with a large number of receptors expressed by various cell types, including infiltrating lymphocytes and monocytes, microglia, astrocytes, endothelial cells, and neurons [2,3,4]. The complex functions played by these molecules under physiological and pathological conditions are regulated by key organizing principles [5]. These molecules constitute complex networks where the same cytokine can perform different activities depending on the inflammatory milieu, and the same effect can be mediated by several cooperating cytokines, according to a principle of redundancy [5]. For these reasons, it is extremely difficult to elucidate the role of each specific cytokine even though some molecules have been consistently associated with increased inflammation and a worse clinical course in MS. More specifically, interleukin (IL)-6 represents one of the most important pro-inflammatory cytokines in the pathophysiology of MS [2,6,7]. Preclinical studies in animal models of MS (i.e., experimental autoimmune encephalomyelitis, EAE), have shown that IL-6 deficient mice were fully resistant to the disease induction [8]. Similarly, blocking the IL-6 receptor (IL6R) led to a significant reduction of EAE symptoms [9]. Clinical studies in patients with relapsing remitting-MS (RR-MS) confirmed an association between higher levels of IL-6 in the cerebrospinal fluid (CSF) and a worse disease course characterized by an increased relapse rate and greater disability [6,7]. Notably, previous studies have shown that single nucleotide polymorphisms (SNPs) of the IL-6 gene can affect MS susceptibility [10,11]. These data suggest that interindividual IL-6 gene variability may influence the CSF inflammatory milieu and clinical presentation of MS. To explore this, we investigated whether four SNPs of the IL-6 gene (rs1818879, rs1554606, rs1800797, and rs1474347) are associated with different levels of CSF pro-inflammatory and anti-inflammatory molecules and clinical presentation in a group of RR-MS patients at the time of diagnosis.

2. Materials and Methods

2.1. MS Patients

In this study, we enrolled a group of 171 consecutive RR-MS patients at the time of diagnosis. We admitted patients to the neurological clinic of the Neuromed Research Institute in Pozzilli, Italy, between 2017 and 2019. The diagnosis of MS was made on the basis of clinical, laboratory, and MRI parameters. The Ethics Committee of the Neuromed Research Institute in Pozzilli, Italy approved the study according to the Declaration of Helsinki (cod. 06-17). All patients gave written informed consent to participate in the study. At the time of diagnosis, patients underwent a clinical evaluation, a brain and spine MRI, and a lumbar puncture. Clinical characteristics recorded were age, sex, an expanded disability status score (EDSS), the presence of clinical/radiological disease activity, and disease duration. Clinical activity was defined as the presence of a concomitant clinical relapse. Disease duration was defined as the interval elapsing between the first clinical episode compatible with MS and confirmed diagnosis.

2.2. IL-6 SNPs Analysis

Genotyping for IL-6 SNPs rs1818879, rs1554606, rs1800797, and rs1474347 was performed in all enrolled patients. A blood sample (200 mL) was collected at the time of diagnosis. Genomic DNA was isolated from peripheral blood leukocytes according to standard procedures (QIAamp DNA Blood Mini Kit–QIAGEN, Hilden, Germany). IL-6 SNPs were analyzed with a TaqMan Validate SNP Genotyping Assay (Applied Biosystems, Foster City, CA, USA) using the ABI-Prism 7900HT Sequence Detection System (Applied Biosystems, Foster City, CA, USA) from 25 ng of genomic DNA in a final volume of 15 mL according to the manufacturer’s instructions.

2.3. CSF Collection and Analysis

In all RR-MS patients, CSF concentrations of inflammatory cytokines were analyzed. CSF was collected at the time of diagnosis, during hospitalization, and by lumbar puncture (LP). No corticosteroids were administered before LP. Disease modifying therapies were initiated after the confirmed diagnosis when indicated. CSF was stored at −80 °C and then analyzed using a Bio-Plex multiplex cytokine assay (Bio-Rad Laboratories, Hercules, CA, USA). CSF cytokines levels were determined according to a standard curve generated for the specific target and expressed as picograms/milliliter (pg/mL). Samples were analyzed in triplicate. The CSF cytokines analyzed included IL-1β, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL-12, IL-13, IL-15, IL-17, the tumor necrosis factor α (TNF-α), IFN-γ, the macrophage inflammatory protein 1α (MIP-1α), the macrophage inflammatory protein 1β (MIP-1β), the monocyte chemoattractant protein 1 (MCP-1), the granulocyte colony-stimulating factor (G-CSF), the granulocyte–monocyte colony stimulating factor (GM-CSF), the interleukin-1 receptor antagonist (IL-1ra), eotaxin, the fibroblast growing factor (FGF), the IFN-γ induced protein 10 (IP-10), the platelet-derived growth factor (PDGF), when regulated upon activation, normal T cells that are expressed and secreted (RANTES), and the vascular endothelial growth factor (VEGF).

2.4. MRI

All the patients underwent a 1.5T MRI scan of brain and spinal cord, which included the following sequences: dual-echo proton density, fluid-attenuated inversion recovery (FLAIR), T1-weighted spin-echo (SE), T2-weighted fast SE, and a contrast-enhanced T1-weighted SE before and after intravenous gadolinium (Gd) infusion (0.2 mL/kg). Radiological disease activity at the time of diagnosis was defined as the presence of Gd-enhancing (Gd+) lesions at the time of hospitalization in brain and spinal cord.

2.5. Statistical Analysis

A Shapiro–Wilk test was used to evaluate normality distribution of continuous variables. Data were shown as mean (standard deviation, SD) or median (interquartile range, IQR). Categorical variables were presented as absolute (n) and relative frequency (%). A chi-square, or when necessary, a Fisher’s exact test, were employed to explore the associations between categorical variables. The difference in continuous variables between the IL-6 SNP groups was evaluated using a nonparametric Mann–Whitney test. A p value < 0.05 was considered statistically significant. When exploring the impact of SNPs on the CSF cytokine profile, we used a method based on dimensionality reduction (principal component regression) to first select a subset of cytokines for the second level analysis. Principal Component Analysis (PCA) was applied to the sample of the 27 CSF cytokines. Logistic regression was used to explore the association between principal components (PCs), each SNP, and to assess the association between SNPs and individual cytokines. All analyses were performed using IBM SPSS Statistics for Windows (IBM Corp., Armonk, NY, USA) and R (R Core Team).

3. Results

3.1. Clinical Characteristics in MS Patients

The clinical and demographic characteristics of RR-MS patients involved in the study are shown in Table 1. The first clinical event was characterized by: pyramidal symptoms (29.8%), visual symptoms (19.9%), brainstem symptoms (19.9%), cerebellar symptoms (8.2%), sphincteric symptoms (2.9%), and cognitive symptoms (1.2%). Missing data: 7 patients.

3.2. Analysis of IL-6 SNP

We assessed the frequencies of the alleles and genotypes for all IL-6 SNPs. In our cohort, the allele frequencies of the four SNPs were in the Hardy–Weinberg equilibrium considering the general Caucasian population (Gnomad database) (Table 2). To obtain two comparable groups, for each SNP, patients were divided into two groups, one homozygous for the major allele, and one carrying the minor allele in homozygosity or heterozygosity (Table 2).

3.3. Association between CSF Inflammation and IL-6 SNPs

To explore whether individual genetic variability in the IL-6 gene could influence central inflammation in MS, we analyzed the possible association between IL-6 SNPs rs1818879, rs1554606, rs1800797, and rs1474347 and the CSF cytokine profile.
PCA, a dimension reduction technique which generates latent variables (PCs), was applied to our set of 27 cytokines [12]. The first six PCs explained 70.364% of the variance (Supplementary Table S1) and were retained for further analysis. In Figure 1 (Panel a and b), we show the association of specific cytokines with the first 4 PCs. We used logistic regression to assess the association between each SNP (rs1818879, rs1554606, rs1800797, and rs1474347) and the first six PCs. We found a significant association between rs1818879 and PC1 (β-coefficient = 0.27; SE = 0.11; p = 0.018) (see also Supplementary Table S2). Conversely, we found no significant associations between the other SPNs explored.
As shown in Figure 1a, the following cytokines have high positive loading (cut-off value > 3) on PC1: IL-1β, IL-4, IL-5, IL-7, IL-9, IL-10, IL-13, G-CSF, PDGF, and VEGF. When analyzing the impact of rs1818879 on the CSF levels of these cytokines, we found significantly higher levels of IL-1β (p = 0.0385); IL-9 (p = 0. 0231); IL-10 (p = 0. 0345); and IL-13 (p = 0.0319) in the A carrier. Conversely, the association between other cytokines was not significant (Table 3).

3.4. rs1818879 Influences Radiological Activity in RRMS Patients

A significant association emerged between the SNP rs1818879 and radiological activity at diagnosis (Table 4). In particular, the presence of the A allele was associated with a higher prevalence of gadolinium-enhancing lesions at the time of diagnosis (GG patients = 32.60%; GA/AA = 57.50; p = 0.001). We found no other significant differences between the two groups in the demographic and clinical characteristics examined.

4. Discussion

In the present study we investigated in a group of newly diagnosed RR-MS patients and the association between four SNPs of the IL-6 gene (rs1818879, rs1554606, rs1800797, and rs1474347) and a large set of CSF inflammatory molecules. PCA was applied to our set of 27 CSF cytokines to identify, in an unsupervised manner, specific components explaining the synergistic effect of different molecules [12]. We found a significant association between rs1818879 and the first component (PC1) which represents the greatest source of variation, explaining 24.23% of the variance within our CSF cytokine set. PC1 reflects the combined effect of a large set of pro- and anti-inflammatory molecules including IL-1β, IL-4, IL-5, IL-7, IL-9, IL-10, IL-13, G-CSF, PDGF, and VEGF. In particular, the CSF levels of IL-1β, IL-9, IL-10, and IL-13 were significantly higher in A minor allele carriers of rs1818879. These data suggest that individual variability of IL-6 rs1818879 may influence the CSF inflammatory milieu in RR-MS. In particular, A minor allele carriers may present with higher levels of central inflammation at the time of diagnosis.
When exploring the association between rs1818879 and clinical characteristics, we found a higher prevalence of radiological disease activity among patients carrying the A minor allele. Conversely, no significant associations emerged with other clinical features, including EDSS and clinical activity. Altogether, these apparently contrasting findings may possibly suggest an increased susceptibility to new inflammatory subclinical episodes in these patients.
In rs1818879, A minor allele carriers significantly increasing CSF levels of both pro-inflammatory and anti-inflammatory cytokines have been observed. Notably, IL-1β is a prototypical pro-inflammatory molecule involved in the migration of activated inflammatory cells into the CNS by altering BBB permeability [13]. This cytokine is produced by several immune cells including monocytes, macrophages, dendritic cells, neutrophils, T lymphocytes, and glial cells, in response to inflammatory signals [13]. Previous studies have clearly demonstrated the role of IL-1β in the pathogenesis of EAE and MS [14]. In particular, IL-1β detectability in the CSF of stable RR-MS patients has been associated with increased prospective disability and higher neurodegeneration [15]. The other cytokines associated with rs1818879, particularly IL-9, IL-10 and IL-13, have been classically linked to anti-inflammatory functions in MS [16,17,18,19]. In particular, IL-9 is secreted by T helper cells and regulates the balance between Th17 and T regulator (Treg) cells favoring the latter [20]. Similarly, IL-10 and IL-13 are pleiotropic, and immunoregulatory cytokines associated with T helper 2 and Treg cell responses and functions, promote immune homeostasis and anti-inflammatory responses [18,19]. A concurrent elevation of both pro- and anti-inflammatory cytokines may therefore suggest heterogeneous activation of the immune response in these patients.
Previous studies have investigated the role of SNPs in the IL-6 gene in MS [10,11,21,22,23,24,25,26] and other autoimmune diseases (e.g., rheumatoid arthritis and erythematous systemic lupus [27,28]). Particularly for MS, the SNP rs1800795, located in the promoter region of the IL-6 gene, has been associated with MS risk and severity [11,21]. rs1800795 has also been implicated in the development of optic neuritis risk [22,23], and in the modulation of flu-like symptoms in patients treated with interferon β1a [22]; however, the role of most IL-6 gene SNPs in MS is still unknown.
To the best of our knowledge, this is the first study demonstrating a direct effect of a SNP of the IL-6 gene on CSF cytokine milieu in RR-MS, and the first study investigating the role of rs1818879 in MS. Previous studies have shown a possible a role of this polymorphism in different inflammatory conditions. In rs1818879, A minor allele carriers have a greater risk of developing inflammatory diseases such as chronic obstructive bronco pneumopathy (COPD) has been reported, which is associated with smoking [29]. In addition, a study showed a higher risk of developing major depressive disorder in patients with childhood maltreatment carrying the A minor allele of rs1818879 [30].
Although our results indicate that rs1818879 may significantly influence an immune response in MS, and may represent a possible marker associated with higher risk of neuroinflammation and disease activity, the lack of correlation between this SNP and IL-6 CSF concentrations represents an unexpected result. rs1818879 is localized in the 3′ untranslated region (3′UTR) near the promotor of the IL-6 gene and the CCCTC-Binding factor (CTCF) binding site that can be involved in the modulation of gene expression [31]. As reported by the Genotype-Tissue Expression (GTEx) project, this SNP is localized in another gene placed in the opposite strand of IL-6 gene encoding for AC073072, a novel antisense long non-coding RNA, about which, little is known [31,32]. Although the mechanism remains unclear, localization in sites involved in the direct or indirect regulation of gene expression, suggests that rs1818879 may be a functional polymorphism [31]. In this regard, one hypothesis could be that rs1818879 is not involved directly in the synthesis of IL-6 but it could be able to indirectly influence the levels of other CSF cytokines; however, the idea that the lack of association with IL-6 levels could be due to statistical/technical limitations cannot be overlooked. Therefore, it is necessary to study a larger cohort of MS patients with homogeneous clinical characteristics, such as disease duration and activity, which have been previously associated with increased IL-6 expression [6,33]. Equally important, are studies with longer follow-ups, which are needed to clarify possible associations between A minor allele presence and clinical activity, as the effects of chronic increased levels of IL-1β, IL-9, IL-10, and IL-13, mediated by rs1818879, may influence the disease course in the long run. Other important limitations are represented by the lack of detailed MRI data, such as quantification of the T2 lesions’ load and volume.
In conclusion, the association between IL-6 rs1818879 SNP and central inflammation suggests a role for this polymorphism in regulating disease activity in MS.

Supplementary Materials

The following supporting information can be downloaded at: https://0-www-mdpi-com.brum.beds.ac.uk/article/10.3390/genes13050897/s1, Table S1. Variance of the first six principal components in PCA. Table S2. Logistic regression analysis between PCs and SNP rs1818879 group.

Author Contributions

Conceptualization, A.B. (Antonio Bruno), D.C. and M.S.B.; writing—original draft preparation, A.B. (Antonio Bruno) and M.S.B.; writing—review and editing, A.B. (Antonio Bruno), D.C. and M.S.B.; data collection/curation, E.D., F.A., S.G., R.F. (Rosangela Ferese), F.R.R., L.G. (Luana Gilio), E.I., G.G., A.B. (Angela Borrelli), F.B., R.F. (Roberto Furlan), A.F., F.D.V., A.M. (Alessandra Musella), L.G. (Livia Guadalupi), G.M.; funding acquisition, D.C., G.M.; statistical analysis, A.M. (Alessandro Moscatelli), A.B. and M.S.B. All authors have read and agreed to the published version of the manuscript.

Funding

The study was supported by: FISM grants (Fondazione Italiana Sclerosi Multipla-cod. 2019/S/1 to D.C. and F.R.R. and FISM-Fondazione Italiana Sclerosi Multipla-cod. 2020/R-Multi/018 and financed or cofinanced with the 5 per mille public funding to M.S.B. and F.R.R.); Progetto di Rete RCR-2020-23670067 to IRCCS Neuromed; Ricerca corrente to IRCCS San Raffaele Roma; Progetto Nuovi biomarker diagnostici e terapeutici delle malattie neurodegenerative to D.C.; private donation in memory of Chiara Sardi to D.C.; F.D.V. was supported by a research fellowship FISM (cod. 2020/BS/003) and financed and co-financed with the 5 per mille public funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board (or Ethics Committee) of Neuromed Research Institute in 2017 (cod. 06-17).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from the patient(s) to publish this paper.

Data Availability Statement

Anonymized datasets are available upon reasonable request to the corresponding author.

Conflicts of Interest

The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: F.B. acted as an Advisory Board member of Teva and Roche and received honoraria for speaking or consultation fees from Merck Serono, Teva, Biogen Idec, Sanofi, and Novartis, and received non-financial support from Merck Serono, Teva, Biogen Idec, and Sanofi. R.F. received honoraria for serving on scientific advisory boards or as a speaker from Biogen, Novartis, Roche, and Merck, and funding for research came from Merck. D.C. is an Advisory Board member of Almirall, Bayer Schering, Biogen, GW Pharmaceuticals, Merck Serono, Novartis, Roche, Sanofi-Genzyme, and Teva, and received honoraria for speaking or consultation fees from Almirall, Bayer Schering, Biogen, GW Pharmaceuticals, Merck Serono, Novartis, Roche, Sanofi-Genzyme, and Teva. He is also the principal investigator in clinical trials for Bayer Schering, Biogen, Merck Serono, Mitsubishi, Novartis, Roche, Sanofi-Genzyme, and Teva. His preclinical and clinical research was supported by grants from Bayer Schering, Biogen Idec, Celgene, Merck Serono, Novartis, Roche, Sanofi-Genzyme, and Teva. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. A.B. (Antonio Bruno), E.D., F.A., A.M. (Alessandro Moscatelli), S.G., R.F. (Rosangela Ferese), F.R.R., L.G. (Luana Gilio), E.I., G.G., A.B. (Angela Borrelli), F.B., R.F. (Roberto Furlan), A.F., F.D.V., A.M. (Alessandra Musella), L.G. (Livia Guadalupi), G.M., M.S.B.: nothing to report.

References

  1. Thompson, A.J.; Baranzini, S.E.; Geurts, J.; Hemmer, B.; Ciccarelli, O. Multiple sclerosis. Lancet 2018, 391, 1622–1636. [Google Scholar] [CrossRef]
  2. Göbel, K.; Ruck, T.; Meuth, S.G. Cytokine signaling in multiple sclerosis: Lost in translation. Mult. Scler. J. 2018, 24, 432–439. [Google Scholar] [CrossRef] [PubMed]
  3. Codarri, L.; Fontana, A.; Becher, B. Cytokine networks in multiple sclerosis: Lost in translation. Curr. Opin. Neurol. 2010, 23, 205–211. [Google Scholar] [CrossRef]
  4. Yadav, S.K.; Mindur, J.E.; Ito, K.; Dhib-Jalbut, S. Advances in the immunopathogenesis of multiple sclerosis. Curr. Opin. Neurol. 2015, 28, 206–219. [Google Scholar] [CrossRef] [PubMed]
  5. Becher, B.; Spath, S.; Goverman, J. Cytokine networks in neuroinflammation. Nat. Rev. Immunol. 2017, 17, 49–59. [Google Scholar] [CrossRef] [PubMed]
  6. Stampanoni Bassi, M.; Iezzi, E.; Mori, F.; Simonelli, I.; Gilio, L.; Buttari, F.; Sica, F.; De Paolis, N.; Mandolesi, G.; Musella, A.; et al. Interleukin-6 Disrupts Synaptic Plasticity and Impairs Tissue Damage Compensation in Multiple Sclerosis. Neurorehabil. Neural Repair 2019, 33, 825–835. [Google Scholar] [CrossRef]
  7. Stampanoni Bassi, M.; Iezzi, E.; Drulovic, J.; Pekmezovic, T.; Gilio, L.; Furlan, R.; Finardi, A.; Marfia, G.A.; Sica, F.; Centonze, D.; et al. IL-6 in the Cerebrospinal Fluid Signals Disease Activity in Multiple Sclerosis. Front. Cell. Neurosci. 2020, 14, 120. [Google Scholar] [CrossRef]
  8. Kleiter, I.; Ayzenberg, I.; Araki, M.; Yamamura, T.; Gold, R. Tocilizumab, MS, and NMOSD. Mult. Scler. J. 2016, 22, 1891–1892. [Google Scholar] [CrossRef]
  9. Rothaug, M.; Becker-Pauly, C.; Rose-John, S. The role of interleukin-6 signaling in nervous tissue. Biochim. Biophys. Acta-Mol. Cell Res. 2016, 1863, 1218–1227. [Google Scholar] [CrossRef]
  10. Mirowska-Guzel, D.; Gromadzka, G.; Mach, A.; Czlonkowski, A.; Czlonkowska, A. Association of IL1A, IL1B, ILRN, IL6, IL10 and TNF-α polymorphisms with risk and clinical course of multiple sclerosis in a Polish population. J. Neuroimmunol. 2011, 236, 87–92. [Google Scholar] [CrossRef]
  11. Benešová, Y.; Vašků, A.; Bienertová-Vašků, J. Association of interleukin 6, interleukin 7 receptor alpha, and interleukin 12B gene polymorphisms with multiple sclerosis. Acta Neurol. Belg. 2018, 118, 493–501. [Google Scholar] [CrossRef] [PubMed]
  12. Helmy, A.; Antoniades, C.A.; Guilfoyle, M.R.; Carpenter, K.L.H.; Hutchinson, P.J. Principal Component Analysis of the Cytokine and Chemokine Response to Human Traumatic Brain Injury. PLoS ONE 2012, 7, e39677. [Google Scholar] [CrossRef]
  13. Musella, A.; Fresegna, D.; Rizzo, F.R.; Gentile, A.; De Vito, F.; Caioli, S.; Guadalupi, L.; Bruno, A.; Dolcetti, E.; Buttari, F.; et al. ‘Prototypical’ proinflammatory cytokine (IL-1) in multiple sclerosis: Role in pathogenesis and therapeutic targeting. Expert Opin. Ther. Targets 2020, 24, 37–46. [Google Scholar] [CrossRef] [PubMed]
  14. Mandolesi, G.; Gentile, A.; Musella, A.; Fresegna, D.; De Vito, F.; Bullitta, S.; Sepman, H.; Marfia, G.A.; Centonze, D. Synaptopathy connects inflammation and neurodegeneration in multiple sclerosis. Nat. Rev. Neurol. 2015, 11, 711–724. [Google Scholar] [CrossRef] [PubMed]
  15. Rossi, S.; Studer, V.; Motta, C.; Germani, G.; Macchiarulo, G.; Buttari, F.; Mancino, R.; Castelli, M.; De Chiara, V.; Weiss, S.; et al. Cerebrospinal fluid detection of interleukin-1β in phase of remission predicts disease progression in multiple sclerosis. J. Neuroinflamm. 2014, 11, 32. [Google Scholar] [CrossRef] [Green Version]
  16. Ruocco, G.; Rossi, S.; Motta, C.; Macchiarulo, G.; Barbieri, F.; De Bardi, M.; Borsellino, G.; Finardi, A.; Grasso, M.G.; Ruggieri, S.; et al. T helper 9 cells induced by plasmacytoid dendritic cells regulate interleukin-17 in multiple sclerosis. Clin. Sci. 2015, 129, 291–303. [Google Scholar] [CrossRef] [Green Version]
  17. Donninelli, G.; Studer, V.; Brambilla, L.; Zecca, C.; Peluso, D.; Laroni, A.; Michelis, D.; Mantegazza, R.; Confalonieri, P.; Volpe, E. Immune Soluble Factors in the Cerebrospinal Fluid of Progressive Multiple Sclerosis Patients Segregate into Two Groups. Front. Immunol. 2021, 12, 633167. [Google Scholar] [CrossRef]
  18. Sedeeq, M.S.; El-Nahrery, E.M.A.; Shalaby, N.; Hussein, M.; Shehata, H.; El Aal, R.A.; Abdel Ghaffar, N.F.; Mohamed, M.M. Micro-RNA-96 and interleukin-10 are independent biomarkers for multiple sclerosis activity. J. Neurol. Sci. 2019, 403, 92–96. [Google Scholar] [CrossRef]
  19. Rossi, S.; Mancino, R.; Bergami, A.; Mori, F.; Castelli, M.; De Chiara, V.; Studer, V.; Mataluni, G.; Sancesario, G.; Parisi, V.; et al. Potential role of IL-13 in neuroprotection and cortical excitability regulation in multiple sclerosis. Mult. Scler. J. 2011, 17, 1301–1312. [Google Scholar] [CrossRef]
  20. Elyaman, W.; Khoury, S.J. Th9 cells in the pathogenesis of EAE and multiple sclerosis. Semin. Immunopathol. 2017, 39, 79–87. [Google Scholar] [CrossRef]
  21. Yan, J.; Liu, J.; Lin, C.Y.; Csurhes, P.A.; Pender, M.P.; McCombe, P.A.; Greer, J.M. Interleukin-6 Gene Promoter-572 C Allele May Play a Role in Rate of Disease Progression in Multiple Sclerosis. Int. J. Mol. Sci. 2012, 13, 13667–13679. [Google Scholar] [CrossRef] [Green Version]
  22. Stonys, V.; Lindžiūtė, M.; Vilkevičiūtė, A.; Gedvilaitė, G.; Kriaučiūnienė, L.; Banevičius, M.; Žemaitienė, R.; Liutkevičienė, R. Associations between IL1RAP rs4624606, IL1RL1 rs1041973, IL-6 rs1800795, and HTRA1 rs11200638 gene polymorphisms and development of optic neuritis with or without multiple sclerosis. Ophthalmic Genet. 2020, 41, 325–330. [Google Scholar] [CrossRef] [PubMed]
  23. Gedvilaite, G.; Vilkeviciute, A.; Kriauciuniene, L.; Asmoniene, V.; Liutkeviciene, R. Does CETP rs5882, rs708272, SIRT1 rs12778366, FGFR2 rs2981582, STAT3 rs744166, VEGFA rs833068, IL6 rs1800795 polymorphisms play a role in optic neuritis development? Ophthalmic Genet. 2019, 40, 219–226. [Google Scholar] [CrossRef] [PubMed]
  24. Bertoli, D.; Serana, F.; Sottini, A.; Cordioli, C.; Maimone, D.; Amato, M.P.; Centonze, D.; Florio, C.; Puma, E.; Capra, R.; et al. Less Frequent and Less Severe Flu-Like Syndrome in Interferon β-1a Treated Multiple Sclerosis Patients with at Least One Allele Bearing the G > C Polymorphism at Position -174 of the IL-6 Promoter Gene. PLoS ONE 2015, 10, e0135441. [Google Scholar] [CrossRef]
  25. Hu, S.; Chen, Y.; Sun, X.-D.; Li, F.-J.; Shu, Q.-F.; Liu, X.-L.; Jiang, S.-F. Association between IL-6 -174G/C Polymorphism and Risk of Multiple Sclerosis: A Meta-Analysis. Genet. Test. Mol. Biomark. 2014, 18, 127–130. [Google Scholar] [CrossRef] [PubMed]
  26. Amirzargar, A.; Khosravi, F.; Dianat, S.; Hushmand, F.; Maryousef, P.; Foroushani, A.; Lotfi, J.; Nikbin, B. Profile of cytokine gene polymorphisms in Iranian multiple sclerosis patients. Mult. Scler. J. 2007, 13, 253–255. [Google Scholar] [CrossRef]
  27. Paradowska-Gorycka, A.; Roszak, M.; Stypinska, B.; Lutkowska, A.; Walczyk, M.; Olesinska, M.; Wajda, A.; Piotrowski, P.; Puszczewicz, M.; Majewski, D.; et al. IL-6 and TGF-β gene polymorphisms, their serum levels, as well as HLA profile, in patients with systemic lupus erythematosus. Clin. Exp. Rheumatol. 2019, 37, 963–975. [Google Scholar]
  28. Dar, S.A.; Haque, S.; Mandal, R.K.; Singh, T.; Wahid, M.; Jawed, A.; Panda, A.K.; Akhter, N.; Lohani, M.; Areeshi, M.Y.; et al. Interleukin-6-174G > C (rs1800795) polymorphism distribution and its association with rheumatoid arthritis: A case-control study and meta-analysis. Autoimmunity 2017, 50, 158–169. [Google Scholar] [CrossRef]
  29. Ambrocio-Ortiz, E.; Pérez-Rubio, G.; Abarca-Rojano, E.; Montaño, M.; Ramos, C.; Hernández-Zenteno, R.D.J.; Del Angel-Pablo, A.D.; Reséndiz-Hernández, J.M.; Ramírez-Venegas, A.; Falfán-Valencia, R. Influence of proinflammatory cytokine gene polymorphisms on the risk of COPD and the levels of plasma protein. Cytokine 2018, 111, 364–370. [Google Scholar] [CrossRef]
  30. Cohen-Woods, S.; Fisher, H.L.; Ahmetspahic, D.; Douroudis, K.; Stacey, D.; Hosang, G.M.; Korszun, A.; Owen, M.; Craddock, N.; Arolt, V.; et al. Interaction between childhood maltreatment on immunogenetic risk in depression: Discovery and replication in clinical case-control samples. Brain. Behav. Immun. 2018, 67, 203–210. [Google Scholar] [CrossRef] [Green Version]
  31. Kaboré, J.W.; Ilboudo, H.; Noyes, H.; Camara, O.; Kaboré, J.; Camara, M.; Koffi, M.; Lejon, V.; Jamonneau, V.; MacLeod, A.; et al. Candidate gene polymorphisms study between human African trypanosomiasis clinical phenotypes in Guinea. PLoS Negl. Trop. Dis. 2017, 11, e0005833. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  32. Kim, S.; Yu, N.-K.; Kaang, B.-K. CTCF as a multifunctional protein in genome regulation and gene expression. Exp. Mol. Med. 2015, 47, e166. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Stampanoni Bassi, M.; Gilio, L.; Maffei, P.; Dolcetti, E.; Bruno, A.; Buttari, F.; Centonze, D.; Iezzi, E. Exploiting the Multifaceted Effects of Cannabinoids on Mood to Boost Their Therapeutic Use Against Anxiety and Depression. Front. Mol. Neurosci. 2018, 11, 424. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Genes 13 00897 g001 550
Figure 1. The association of specific cytokines with the first four PCs. Figure Legend: The biplot shows the orientation of the different cytokines with respect to the first and the second component (a), and the third and the fourth component (b), respectively. The biplots showing the orientation of the cytokines concerning the first four PCs. Abbreviations: PC (principal component); IL (interleukin); TNF (tumor necrosis factor); IFN (interferon); MIP (macrophage inflammatory protein); MCP (monocyte chemoattractant protein); G-CSF (granulocyte colony-stimulating factor); IL-1ra (interleukin-1 receptor antagonist); FGF (fibroblast growing factor); IP-10 (interferon γ induced protein 10); PDGF (platelet-derived growth factor); RANTES (regulated upon activation, normal T cell expressed and secreted); VEGF (vascular endothelial growth factor).
Figure 1. The association of specific cytokines with the first four PCs. Figure Legend: The biplot shows the orientation of the different cytokines with respect to the first and the second component (a), and the third and the fourth component (b), respectively. The biplots showing the orientation of the cytokines concerning the first four PCs. Abbreviations: PC (principal component); IL (interleukin); TNF (tumor necrosis factor); IFN (interferon); MIP (macrophage inflammatory protein); MCP (monocyte chemoattractant protein); G-CSF (granulocyte colony-stimulating factor); IL-1ra (interleukin-1 receptor antagonist); FGF (fibroblast growing factor); IP-10 (interferon γ induced protein 10); PDGF (platelet-derived growth factor); RANTES (regulated upon activation, normal T cell expressed and secreted); VEGF (vascular endothelial growth factor).
Genes 13 00897 g001
Table
Table 1. Demographic and clinical characteristics of RR-MS patients.
Table 1. Demographic and clinical characteristics of RR-MS patients.
MS Patients
n = 171
Sex, FN (%)113 (66.10)
Age, yearsMean, (SD)35.78 (12.27)
Disease duration, monthsMedian, (IQR)5.10 (1.05–24.89)
EDSS at diagnosisMedian, (IQR)2 (1–2.5)
OCB presence, yesN (%)132/166 (79.50)
Radiological activity at diagnosisN (%)74/166 (44.60)
Clinical activity at diagnosisN (%)68 (39.76)
Abbreviations: female (F), multiple sclerosis (MS), relapsing remitting (RR), expanded disability status scale (EDSS), interquartile range (IQR). Missing data: OCB (5 patients, 2.9%), Radiological activity (5 patients, 2.9%).
Table
Table 2. SNP distribution and allele frequency for the Hardy–Weinberg equilibrium of RR-MS patients calculated to single SNP and SNP groups for analysis.
Table 2. SNP distribution and allele frequency for the Hardy–Weinberg equilibrium of RR-MS patients calculated to single SNP and SNP groups for analysis.
SNPSNP DistributionAllele Frequency (%)Chi-SquareGroup Analysis (n)
rs1818879GG (n = 88; 51.46%)
AG (n = 79; 46.19%)
AA (n = 4; 2.33%)		G = 75.44
A = 24.55		p = 0.917GG (88)
AG/AA (83)
rs1554606GG (n = 78; 70.38%)
TG (n = 46; 32.11%)
TT (n = 11; 6.58%)		G = 74.81
T = 25.18		p = 0.842GG (78)
TG/TT (57)
rs1800797GG (n = 86; 50.58%)
AG (n = 74; 41.17%)
AA (n = 10; 5.88%)		G = 72.35
A = 27.64		p = 0.886GG (86)
AG/AA (84)
rs1474347AA (n = 84; 49.70%)
CA (n = 75; 44.37%)
CC (n = 10; 5.91%)		A = 71.81
C = 28.19		p = 0.870AA (84)
CA/CC (85)
Abbreviations: single nucleotide polymorphism (SNP). Missing data: rs1554606 (4 patients, 2.34%), rs1800797 (1 patient, 0.58%), rs1474347 (2 patients, 1.17%).
Table
Table 3. Median (IQR) of cytokine levels with high loading in PC1 according to the SNP rs1818879 group.
Table 3. Median (IQR) of cytokine levels with high loading in PC1 according to the SNP rs1818879 group.
GGAG/AAp Valueβ-CoefficientSE
IL-1β0.01 (0.01–0.05)0.025 (0.00–0.07)p = 0. 0385 *7.003.38
IL-40.08 (0.01–0.15)0.08 (0.00–0.22)p = 0.1041.250.770
IL-50.34 (0.00–2.16)1.15 (0.00–3.34)p = 0.05430.1420.0740
IL-70.41 (0.00–0.92)0.20 (0.00–1.41)p = 0.09910.1800.109
IL-91.86 (1.11–2.77)2.36 (1.45–5.44)p = 0.0231 *0.1050.0462
IL-101.78 (0.97–2.60)2.11 (1.27–2.70)p = 0.0345 *0.2780.132
IL-131.63 (1.04–3.32)2.06 (1.11–4.53)p = 0.0319 *0.1280.0597
G-CSF15.29 (4.41–25.92)16.51 (3.62–28.34)p = 0.1980.01310.0102
PDGF0.00 (0.00–0.37)0.00 (0.00–0.52)p = 0.2540.1310.115
VEGF4.03 (0.00–13.97)5.79 (0.00–50.29)p = 0.07150.009880.00548
Table Legend: Subjects carrying the homozygous major allele of SNP (GG), subjects carrying A minor allele (AG/AA) for SNP rs18188792. (*) denotes statistical significance (p < 0.05) using logistic regression analysis. Abbreviations: interquartile range (IQR); standard error (SE); IL (interleukin); G-CSF (granulocyte colony stimulating factor); PDGF (platelet-derived growth factor); VEGF (vascular endothelial growth factor). CSF analysis missing data: GG group (3 patients, 3.4%), AG/AA group (7 patients, 8.4%).
Table
Table 4. Demographic and clinical characteristics of RR-MS patients according to the SNP rs1818879 group.
Table 4. Demographic and clinical characteristics of RR-MS patients according to the SNP rs1818879 group.
GG
n = 88 (51.46%)		AG/AA
n = 83 (48.53%)		p Value
Sex, FN (%)57 (64.80)56 (67.50)p = 0.710
Age, yearsMean, (SD)37.20 (12.38)34.27 (12.04)p = 0.111
Disease duration, monthsMedian (IQR)6.66 (1.3–26.13)3.1 (0.90–24.60)p = 0.227
EDSSMedian (IQR)2 (1–2.5)2 (1–2.25)p = 0.647
OCB presence, yesN (%)70/87 (80.50)62/79 (78.50)p = 0.752
Radiological activity at diagnosisN (%)28/86 (32.60)46/80 (57.50)p = 0.001 *
Clinical activity at diagnosisN (%)34 (38.63)34 (40.96) p = 0.707
Table legend: Subjects carrying homozygous major allele of SNP (GG), subjects carrying A minor allele (AG/AA) for SNP rs18188792. (*) denotes statistical significance (p < 0.05) using a nonparametric Mann–Whitney test for continuous variables and a chi-square test for categorial variables. Abbreviations: multiple sclerosis (MS), relapsing remitting (RR), expanded disability status scale (EDSS), interquartile range (IQR).
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Bruno, A.; Dolcetti, E.; Azzolini, F.; Moscatelli, A.; Gambardella, S.; Ferese, R.; Rizzo, F.R.; Gilio, L.; Iezzi, E.; Galifi, G.; et al. Interleukin 6 SNP rs1818879 Regulates Radiological and Inflammatory Activity in Multiple Sclerosis. Genes 2022, 13, 897. https://0-doi-org.brum.beds.ac.uk/10.3390/genes13050897

AMA Style

Bruno A, Dolcetti E, Azzolini F, Moscatelli A, Gambardella S, Ferese R, Rizzo FR, Gilio L, Iezzi E, Galifi G, et al. Interleukin 6 SNP rs1818879 Regulates Radiological and Inflammatory Activity in Multiple Sclerosis. Genes. 2022; 13(5):897. https://0-doi-org.brum.beds.ac.uk/10.3390/genes13050897

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Bruno, Antonio, Ettore Dolcetti, Federica Azzolini, Alessandro Moscatelli, Stefano Gambardella, Rosangela Ferese, Francesca Romana Rizzo, Luana Gilio, Ennio Iezzi, Giovanni Galifi, and et al. 2022. "Interleukin 6 SNP rs1818879 Regulates Radiological and Inflammatory Activity in Multiple Sclerosis" Genes 13, no. 5: 897. https://0-doi-org.brum.beds.ac.uk/10.3390/genes13050897

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