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Egyptian Journal of Medical Human Genetics
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Toll-like receptor 10 gene polymorphism and risk of multiple sclerosis among Iraqi patients

Egyptian Journal of Medical Human Genetics volume 23, Article number: 88 (2022) Cite this article

Abstract

Background

Toll-like receptors (TLRs) are a family of 10 pattern recognition receptors (TLR1–TLR10) involved in the regulation of inflammatory and immune responses besides their role in the pathogenesis of autoimmune diseases including multiple sclerosis (MS). TLR10 is the least studied TLR in MS, and data for single nucleotide polymorphisms (SNPs) of the TLR10 gene are limited. Therefore, a case–control study was performed on 85 patients with relapsing–remitting MS and 86 healthy controls (HC) to explore SNPs in the promoter region of TLR10 gene. A 927-bp region was amplified, and Sanger sequencing identified 10 SNPs with a minor allele frequency ≥ 10% (rs200395112 T/A, rs201802754 A/T, rs201228097 T/A, rs113588825 G/A, rs10004195 T/A, rs10034903 C/G, rs10012016 G/A/C, rs10012017 G/T, rs33994884 T/Deletion [Del] and rs28393318 A/G).

Results

Del allele and T/Del genotype of rs33994884, as well as AG genotype of rs28393318, showed significantly lower frequencies in MS patients compared to HC. Allele and genotype frequencies of the 10 SNPs showed no significant differences between MS patients classified according to the Expanded Disability Status Scale. Haplotype analysis revealed that haplotype A-T-A-G-A-G-G-T-A showed a significantly increased frequency in MS patients compared to HC (odds ratio [OR] = 9.70; 95% confidence interval [CI] = 1.28–73.31; corrected probability [pc] = 0.03), while frequency of A-T-A-G-T-C-A-T-G haplotype was significantly decreased (OR = 0.10; 95% CI = 0.01–0.85; pc = 0.05).

Conclusions

The study indicated that two SNPs may influence susceptibility to MS (rs33994884 and rs28393318), but haplotype analysis of TLR10 gene SNPs was more informative.

Background

Multiple sclerosis (MS) is the most common type of neurological disability with a global prevalence of 35.9/100,000 in 2020 [1]. In Iraq, although a lower prevalence was estimated (11.73/100,000), the incidence of MS has increased from 0.05 in 2000 to 1.5 in 2017 [2]. It is a chronic autoimmune disorder affecting the central nervous system (CNS) primarily characterized by inflammation, demyelination, gliosis, and neuronal loss [3]. Clinically, multiple sclerosis is primarily classified into four phenotypes including relapsing–remitting multiple sclerosis (RRMS), primary progressive MS, secondary progressive MS and progressive relapsing MS, with RRMS being the most common phenotype accounting for 85% of cases [4].

The underlying cause of MS and the pathogenic mechanisms involved are still not fully understood, although complex interactions between genetic and environmental factors almost certainly play an important role [5]. Viral infections, particularly Epstein–Barr virus (EBV), are among the important environmental risk factors that have been linked to the etiology of MS [6]. Immunological and epidemiological studies support a causal role of EBV in risk of MS and disease progression. In this context, several immune-based mechanisms have been proposed to explain the etiological role of EBV in MS including molecular mimicry or cross-reactivity between EBV epitopes and self-epitopes and bystander damage due to aberrant immune response primarily directed to EBV [7]. Among the immune components that control antiviral responses in infected cells or in effector immune cells are Toll-like receptors (TLRs), which are indicated to play an important role in mediating immune responses against various viruses including EBV [8]. Further, TLRs expressed by astrocytes and microglia are important sensors of tissue damage and infection in the CNS and thus are involved in the initiation of innate immune response and inflammation at these sites [9].

TLRs are pattern recognition receptors (PRRs) involved in recognizing pathogen-associated molecular patterns (PAMPs) in viruses and bacteria. They are essential components of the innate immune system whose activation regulates inflammatory responses and establishes antigen-specific adaptive immunity. In humans, 10 functional TLRs (TLR1–TLR10) have been described to date [10]. Dysregulated expression of TLRs has been shown to correlate with immune imbalances, suggesting an increased risk of inflammatory and autoimmune diseases, and similar evidence has been indicated in neuroimmune diseases. Besides, the potential of TLRs as therapeutic targets in these diseases has been suggested [11, 12]. In MS, studies have indicated that TLRs play a critical role in pathogenesis of disease; for instance, TLR3 and TLR4 expression was shown to be significantly up-regulated in MS lesions, and both TLRs were positively correlated with MS pathogenesis, particularly in patients with RRMS [13]. Moreover, T helper (h) 17 cells expressing TLR2, TLR4 and TLR9 were also positively correlated with neuronal dysfunction, as well as number of active brain lesions in MS patients [14]. For TLR10, the data available in MS are limited, but it has been indicated that interferon-β may regulate the expression of distinct TLRs, including TLR3, TLR7, TLR9 and possibly TLR10, in plasmablastic dendritic cells of MS patients [15, 16].

TLR10 shows a high level of expression in immune cells (eosinophils, dendritic cells, neutrophils and B cells) and non-immune cells such as trophoblasts, but functionally, it is considered an orphan receptor with poorly understood ligands and functions, which remains an area in need of elucidation [17]. However, it has been found that TLR10 may function as a broad negative regulator of TLR-mediated signaling, and thus, its role in controlling immune responses has been suggested [18]. Additional data highlighted that TLR10 can be considered as a novel regulator of innate immune responses with a role in the differentiation of primary monocytes into functional dendritic cells [19]. Recently, it has been discussed that TLR10 can suppress other TLRs by competing with other stimulatory TLRs, while its role in promoting or reducing inflammation is subject to speculation [20]. In rheumatoid arthritis, an autoimmune disorder, increased expression of TLR10, has been found in B-cell subsets and was positively correlated with disease activity [21]. Based on these findings, it is reasonable to hypothesize that TLR10 may have a role in the pathogenesis of MS.

In humans, the TLR10 gene (ID: 81,793) is located on the short arm of chromosome 4 (4p14) and harbors large numbers of single nucleotide polymorphisms (SNPs) [20]. These SNPs have been associated with the risk of autoimmune thyroid disease, tuberculosis and infection-related gastritis, along with their role in altering the balance between pro-inflammatory and anti-inflammatory responses [22]. In MS, to the researcher’s best knowledge, the TLR10 gene polymorphism was the subject of only one study and no association with the disease was found [23]. Therefore, this study sought to give a view of the association between SNPs located in a noncoding region of TLR10 gene (promoter region) and risk of MS, as these polymorphisms may affect the expression of TLR10 gene and thus may influence susceptibility to MS. In addition, we examined whether these polymorphisms are associated with the degree of disability in MS patients (Extended Disability Status Scale; EDSS).

Methods

MS patients and controls

During January 2020—June 2021, a case–control study was performed on 171 subjects, including 85 patients with RRMS and 86 healthy controls (HC). Patients were recruited from the MS clinic at the General Hospital for Neurosciences in Baghdad, and the 2010 revised McDonald criteria for RRMS diagnosis were followed [24]. Physical disability was assessed using EDSS, and as previously recommended, MS patients were divided into two groups: < 3.0 (57.6%) and ≥ 3.0 (42.4%) [25]. The HC group included blood donors and health service personnel, who did not have neurological or autoimmune disorders.

Detection of TLR10 gene SNPs

Venous blood (3 mL) was collected from patients (during their visit to the clinic) and HC in an ethylene-diamine-tetra-acetic acid (EDTA) tube. DNA was isolated from EDTA blood using a gSYNC DNA extraction kit following the manufacturer's instructions (Geneaid Biotech Ltd, Taiwan).

The complete DNA sequence of the TLR10 gene and SNP data was downloaded first (http://asia.ensembl.org), and then, forward (5’-CCTGTAGTCCCAGACATTTG-3’) and reverse (5’-GGTTGTTAACCATCCTCTTCT-3’) primers were designed to amplify a promoter region (927 bp) using Geneious software version 11.1.2 [26]. Primers were tested online using in silico PCR analysis (https://genome.ucsc.edu/cgi-bin/hgPcr), which confirmed specificity of primers and molecular size of amplified region (chr4:38,782,390–38,783,316). The target region was screened for SNPs with a minor allele frequency ≥ 10%, and in view of this, 10 SNPs were identified (rs200395112 T/A, rs201802754 A/T, rs201228097 T/A, rs113588825 G/A, rs10004195 T/A, rs10034903 C/G, rs10012016 G/A/C, rs10012017 G/T, rs33994884 T/Deletion [Del] and rs28393318 A/G).

The PCR mix included 2 µL template DNA, 1µL of each primer (10 pmol),12.5 µL GoTaq Green Master Mix (2X: Promega, USA) and 8.5 µL nuclease-free water (total volume = 25 µL). The thermal cycler (BioRad, USA) was set for the following optimized conditions: one cycle of initial denaturation (95 °C for 5 min), followed by 30 cycles of denaturation at 95 °C for 30 s, annealing at 60 °C for 30 s and extension at 72 °C for 60 s, and a final extension cycle at 72 °C for 7 min. Amplified DNA products were subjected to forward and reverse sequencing (Sanger sequencing, Macrogen Corporation, South Korea) and analyzed to identify 10 SNPs of TLR10 gene by aligning with reference sequences (http://asia.ensembl.org). Genotypes of TLR10 gene SNPs were determined using Geneious software version 11.1.2 [26]. A representative chromatogram of DNA sequences of three SNPs (rs201228097, rs201802754 and rs200395112) are shown in Fig. 1.

Fig. 1
figure 1

DNA sequence chromatogram of three TLR10 gene SNPs (rs201228097, rs201802754 and rs200395112) showing genotypes of four samples (S1, S2, S3 and S4). Besides, reference sequence (RS) is also given. The chromatogram was generated by Geneious software analysis (version 11.1.2)

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Statistical analysis

Alleles and genotypes of TLR10 gene SNPs were presented by a number and a percentage, and significant differences were assessed using Fisher's exact test or Pearson's Chi-square test. Pearson's Chi-square goodness-of-fit test was used to test for Hardy–Weinberg equilibrium (HWE). Logistic regression analysis was applied to calculate odds ratio (OR) and 95% confidence interval (CI) after adjusting for age and gender. A probability (p) value ≤ 0.05 was considered statistically significant. Due to multiple comparisons, Bonferroni correction was applied to correct p-value (pc). The statistical package IBM SPSS Statistics 25.0 (Armonk, NY: IBM Corp.) was employed to perform statistical analysis. Haploview software version 4.2 was used to assess linkage disequilibrium (LD) between SNPs and to estimate haplotype frequencies. Logarithm of odds (LOD) and LD coefficient (D') were used to define LD.

Results

Age and gender distribution of participants

The mean age of RRMS patients was 35.0 ± 8.8 years (age range = 18–59 years), which corresponds to the mean age of HC (mean age = 36.4 ± 9.5 years; age range = 22–50 years). In addition, males showed similar frequencies in patients and HC (45.9 and 46.5%, respectively).

TLR10 gene SNPs

Genotype frequencies of the 10 TLR10 gene SNPs (rs200395112 T/A, rs201802754 A/T, rs201228097 T/A, rs113588825 G/A, rs10004195 T/A, rs10034903 C/G, rs10012016 G/A/C, rs10012017 G/T, rs33994884 T/Deletion [Del] and rs28393318 A/G) were in good agreement with HWE in HC. Comparisons between MS patients and HC revealed that only two SNPs showed significant differences (rs33994884 and rs28393318). Del allele (21.8 vs. 36.0%; OR = 0.49; 95% CI = 0.30—0.81; p = 0.006; pc = 0.018) and T/Del genotype (17.6 vs. 37.3%; OR = 0.31; 95% CI = 0.15—0.65; p = 0.003; pc = 0.009) of rs33994884 showed significantly lower frequencies in MS patients compared to HC. rs28393318 is the second SNP that showed a significant difference between MS patients and HC. The heterozygous genotype AG of this SNP was observed to have a lower frequency in patients compared to HC (21.2 vs. 45.3; OR = 0.29; 95% CI = 0.14—0.62; p = 0.002; pc = 0.006). Allele and genotype frequencies of the remaining eight SNPs showed no significant differences between MS patients and HC (Table 1). Further, allele and genotype frequencies of the 10 SNPs showed no significant differences between the two EDSS groups of MS patients (< 3.0 and ≥ 3.0) (Table 2).

Table 1 Logistic regression and Hardy–Weinberg analyses of TLR10 gene SNPs in multiple sclerosis patients and healthy controls
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Table 2 Allele and genotype frequencies of TLR10 gene SNPs stratified by Expanded Disability Status Scale groups in multiple sclerosis patients
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LD and haplotype analysis

Haploview software was used to determine LD between 9 SNPs of TLR10 gene (rs200395112, rs201802754, rs201228097, rs113588825, rs10004195, rs10034903, rs10012016, rs10012017 and rs28393318). SNPrs33994884 was excluded due to the presence of Del allele. These SNPs showed different profiles of LD; strong LD (LOD > 2 and D' = 1), weak LD (LOD > 2 and 0.21 < D' < 1) or no LD (LOD < 2 and D' < 1). For instance, rs201228097, rs201802754 and rs200395112 SNPs were in a strong LD (Figs. 1 and 2).

Fig. 2
figure 2

Linkage disequilibrium (LD) map of nine SNPs within the TLR10 gene. LD blocks were generated using the default algorithm in Haploview software (version 4.2). The coloring of the boxes is based on the scores of two estimates, logarithm of odds (LOD) and LD coefficient (Dʹ). Strong LD (LOD > 2 and Dʹ = 1) is indicated in red. A decrease in the intensity of red indicates lower LOD and Dʹ values. Regions with weak LD (LOD > 2 and 0.21 < Dʹ < 1) are shown in blue. Regions with no LD (LOD < 2 and Dʹ < 1) are shown in white. The number within each box indicates the Dʹ statistic value multiplied by 100

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Haplotype analysis revealed two important findings. The haplotype A-T-A-G-A-G-G-T-A (in the order: rs200395112, rs201802754, rs201228097, rs113588825, rs10004195, rs10034903, rs10012016, rs10012017 and rs28393318) showed a significantly increased frequency in MS patients compared to HC (0.102 vs. 0.003; OR = 9.70; 95% CI = 1.28—73.31; p = 0.003; pc = 0.03), while frequency of A-T-A-G-T-C-A-T-G haplotype was significantly decreased (0.003 vs. 0.058; OR = 0.10; 95% CI = 0.01—0.85; p = 0.005; pc = 0.05) (Table 3).

Table 3 Estimated frequencies of nine-locus haplotypes of TLR10 gene SNPs (in the order: rs200395112, rs201802754, rs201228097, rs113588825, rs10004195, rs10034903, rs10012016, rs10012017 and rs28393318) among multiple sclerosis patients and healthy controls
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Discussion

Although the etiology of MS is not well known and the pathogenesis is not fully understood, recent compelling epidemiological and molecular data have indicated the significance of the combined effects of multiple risk factors in determining an individual's susceptibility to disease progression [3]. It is notably recognized that the initiation of MS involves a chronic dysregulation of immune homeostasis resulting from complex interactions between infectious exposures, genetic predispositions and factors associated with increased pro-inflammatory conditions, including obesity, low sun exposure/low vitamin D levels and smoking [6]. This is reinforced by epigenetic alteration studies suggesting that epigenetic alternations may be involved in the initiation and progression of MS, possibly by correlating the effects of environmental risk factors and host genetics [27]. Accordingly, genetic predisposition is the cornerstone of MS etiology, and in fact, genome-wide association studies (GWAS) revealed more than 200 loci implicated in susceptibility to disease [28]. GWAS also uncovered that up to 10.5% of the genetic variance underlying MS risk is linked to human leukocyte antigen (HLA) class II alleles among all ethnic groups studied [29]. However, studies have also revealed that non-HLA loci and their SNPs may influence susceptibility to MS, especially those involved in controlling components involved in innate and adaptive immune responses; for instance, cytokines and TLRs [12, 16, 30, 31].

Recently, TLR gene SNPs have been discussed as important determinants of susceptibility to several autoimmune diseases including MS, and TLR genetic variations may shed light on the role of complex interaction between immune and environmental factors in influencing the susceptibility to MS in various populations [32]. In this context, several SNPs within TLR genes have been linked to altered susceptibility to MS [23, 33, 34]. The less researched member of TLR genes is TLR10 gene, and only one study identified five SNPs (rs4129008, rs4129009, rs11466657, rs11096955 and rs11096957) in Caucasian MS patients from Denmark but no association with the disease has been demonstrated [23]. None of these SNPs was located in the promoter region. Although promoter SNPs do not alter protein structure, they are involved in the regulation of transcription events and can alter the affinity for normal transcription factors [35]. Therefore, the current study was conducted to examine SNPs in the promoter region of TLR10 gene. Of the 10 SNPs identified, two showed a significant association with MS. The first was rs33994884, and the Del allele and T/Del genotype of this SNP were associated with a reduced risk of developing MS (OR = 0.49 and 0.31, respectively). There is no direct evidence to support these findings but indicated that Del alleles of some SNPs within TLR genes may have functional effects and thus may alter susceptibility to diseases. An example is two deletion polymorphisms within TLR2 gene. The Del allele of a 22-bp nucleotide deletion polymorphism in the promoter region of TLR2 gene has been linked to increased susceptibility to breast cancer [36], while the heterozygous genotype of this allele has been associated with a protection from cerebral malaria [37]. Another Del allele of a polymorphism in TLR2 gene (-196 to -174 Ins/Del; rs111200466) has been associated with increased susceptibility to aggressive periodontitis, type 2 diabetes mellitus and prostate cancer [38,39,40], while in primary gouty arthritis, no association was found with this allele [41]. A recent study demonstrated that the heterozygous genotype of rs111200466 showed a lower frequency in patients with schizophrenia than in HC, and thus, a protective tendency against this psychiatric disorder has been suggested, particularly in women [42]. The presented studies disclosed interesting findings, which indicated that the heterozygous genotype of the Del allele may be associated with a protective trend against some diseases rather than conferring susceptibility. This view is shared by the current study, and therefore, it is possible to suggest that the T/Del genotype of rs33994884 may have a protective effect against the development of MS.

The study also showed that the rs28393318 AG genotype was associated with a lower risk of developing MS (OR = 0.29). No previous study could confirm or refute these findings, but it has been demonstrated that rs28393318 was among 38 variants that showed an association with asthma and allergic diseases [43]. Another study showed that rs28393318 was associated with the presence of precancerous gastric lesions [44]. Genome-wide genetic studies also showed that rs28393318 was among 17 genetic loci that had an effect on the abundance of transcripts of immune genes especially those related to cytokines [45]. Taken together, these data may indicate that rs28393318 can affect innate immune signaling and thus may contribute to the inflammatory response in MS.

Although single-SNP-based association studies have great potential for identifying genetic influences on diseases of complex traits, a haplotype-based analysis that incorporates LD information from several SNPs may be more powerful and informative than the traditional analysis that focuses on individual SNPs [46]. In view of this, a multi-locus haplotype analysis that included nine TLR10 gene SNPs (in the order: rs200395112, rs201802754, rs201228097, rs113588825, rs10004195, rs10034903, rs10012016, rs10012017 and rs28393318) was performed. The analysis revealed two important findings; A-T-A-G-A-G-T-A haplotype was significantly associated with a 9.70-fold increased risk of MS, while A-T-A-G-T-C-A-T-G haplotype was associated with a reduced risk of MS. As these blocks included both risk and protective haplotypes, a critical role for the TLR10 promoter towards MS susceptibility could be envisaged, but more studies are certainly warranted to understand this role in MS pathogenesis.

Regardless of the significance of this study being the first to explore the association between genetic variants in the TLR10 promoter region and MS in Iraqis, some limitations should be noted. First, the sample size of patients and HC is relatively small compared to other case–control studies. Second, EBV status (prevalence and viral load) was not determined and it would be of interest to correlate it with the TLR10 polymorphism. Third, the impact of TLR10 polymorphism on serum level and/or gene expression of TLR10 was not studied.

Conclusions

The study indicated that two SNPs in the promoter region of TLR10 gene may influence susceptibility to MS. The T/Del genotype of rs33994884 and AG genotype of rs28393318 are suggested to have protective effects against the development of MS. Haplotype analysis of TLR10 gene SNPs was more informative in highlighting the association with MS susceptibility.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

CI:

Confidence interval

CNS:

Central nervous system

D':

Linkage disequilibrium coefficient

Del:

Deletion

EBV:

Epstein–Barr virus

EDSS:

Expanded Disability Status Scale

LD:

Linkage disequilibrium

LOD:

Logarithm of odds

MS:

Multiple sclerosis

OR:

Odds ratio

p :

Probability

PAMP:

Pathogen-associated molecular pattern

pc :

Corrected probability

PRR:

Pattern recognition receptors

RRMS:

Relapsing–remitting multiple sclerosis

SNP:

Single nucleotide polymorphism

TLR:

Toll-like receptor

References

  1. Walton C, King R, Rechtman L, Kaye W, Leray E, Marrie RA et al (2020) Rising prevalence of multiple sclerosis worldwide: insights from the Atlas of MS. Mult Scler J 26:1816–21. https://doi.org/10.1177/1352458520970841

    Article  Google Scholar 

  2. Hassoun HK, Al-Mahadawi A, Sheaheed NM, Sami SM, Jamal A, Allebban Z (2021) Epidemiology of multiple sclerosis in Iraq: retrospective review of 4355 cases and literature review. Neurol Res 44:14–23. https://doi.org/10.1080/01616412.2021.1952511

    Article  CAS  PubMed  Google Scholar 

  3. Ghasemi N, Razavi S, Nikzad E (2017) Multiple sclerosis: pathogenesis, symptoms, diagnoses and cell-based therapy. Cell J. 19:1–10. https://doi.org/10.22074/cellj.2016.4867

    Article  PubMed  Google Scholar 

  4. Klineova S, Lublin FD (2018) Clinical course of multiple sclerosis. Cold Spring Harb Perspect Med 8:a028928. https://doi.org/10.1101/cshperspect.a028928

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Dobson R, Giovannoni G (2019) Multiple sclerosis–a review. Eur J Neurol 26:27–40. https://doi.org/10.1111/ene.13819

    Article  CAS  PubMed  Google Scholar 

  6. Waubant E, Lucas R, Mowry E, Graves J, Olsson T, Alfredsson L et al (2019) Environmental and genetic risk factors for MS: an integrated review. Ann Clin Transl Neurol 6:1905–1922. https://doi.org/10.1002/acn3.50862

    Article  PubMed  PubMed Central  Google Scholar 

  7. Drosu N, Giovannoni G, Pohl D, Hawkes C, Lechner-Scott J, Levy M (2021) Probing the association between Multiple Sclerosis and Epstein Barr Virus from a therapeutic perspective. Mult Scler Relat Disord 52:103087. https://doi.org/10.1016/j.msard.2021.103087

    Article  PubMed  Google Scholar 

  8. Gaglia MM (2021) Anti-viral and pro-inflammatory functions of Toll-like receptors during gamma-herpesvirus infections. Virol J 18:218. https://doi.org/10.1186/s12985-021-01678-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Li L, Acioglu C, Heary RF, Elkabes S (2021) Role of astroglial toll-like receptors (TLRs) in central nervous system infections, injury and neurodegenerative diseases. Brain Behav Immun 91:740–755. https://doi.org/10.1016/j.bbi.2020.10.007

    Article  CAS  PubMed  Google Scholar 

  10. Li D, Wu M (2021) Pattern recognition receptors in health and diseases. Signal Transduct Target Ther 6:1–24. https://doi.org/10.1038/s41392-021-00687-0

    Article  CAS  Google Scholar 

  11. El-Zayat SR, Sibaii H, Mannaa FA (2019) Toll-like receptors activation, signaling, and targeting: an overview. Bull Natl Res Cent 43:1–12. https://doi.org/10.1186/s42269-019-0227-2

    Article  Google Scholar 

  12. Li H, Liu S, Han J, Li S, Gao X, Wang M et al (2021) Role of toll-like receptors in neuroimmune diseases: therapeutic targets and problems. Front Immunol 12:777606. https://doi.org/10.3389/fimmu.2021.777606

    Article  PubMed  PubMed Central  Google Scholar 

  13. Gooshe M, Abdolghaffari AH, Gambuzza ME, Rezaei N (2014) The role of toll-like receptors in multiple sclerosis and possible targeting for therapeutic purposes. Rev Neurosci 25:713–739. https://doi.org/10.1515/revneuro-2014-0026

    Article  CAS  PubMed  Google Scholar 

  14. Ferreira TB, Hygino J, Wing AC, Kasahara TM, Sacramento PM, Camargo S et al (2018) Different interleukin-17-secreting Toll-like receptor + T-cell subsets are associated with disease activity in multiple sclerosis. Immunology 154:239–252. https://doi.org/10.1111/imm.12872

    Article  CAS  PubMed  Google Scholar 

  15. Derkow K, Bauer JMJ, Hecker M, Paap BK, Thamilarasan M, Koczan D et al (2013) Multiple sclerosis: modulation of toll-like receptor (TLR) expression by interferon-β includes upregulation of TLR7 in plasmacytoid dendritic cells. PLoS ONE 8:e70626. https://doi.org/10.1371/journal.pone.0070626

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Miranda-Hernandez S, Baxter AG (2013) Role of toll-like receptors in multiple sclerosis. Am J Clin Exp Immunol 2:75–93

    PubMed  PubMed Central  Google Scholar 

  17. Fore F, Indriputri C, Mamutse J, Nugraha J (2020) TLR10 and its unique anti-inflammatory properties and potential use as a target in therapeutics. Immune Netw 20:e21. https://doi.org/10.4110/in.2020.20.e21

    Article  PubMed  PubMed Central  Google Scholar 

  18. Jiang S, Li X, Hess NJ, Guan Y, Tapping RI (2016) TLR10 is a negative regulator of both MyD88-dependent and -independent TLR signaling. J Immunol 196:3834–3841. https://doi.org/10.4049/jimmunol.1502599

    Article  CAS  PubMed  Google Scholar 

  19. Hess NJ, Felicelli C, Grage J, Tapping RI (2017) TLR10 suppresses the activation and differentiation of monocytes with effects on DC-mediated adaptive immune responses. J Leukoc Biol 101:1245–1252. https://doi.org/10.1189/jlb.3a1116-492r

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Fore F, Budipranama M, Destiawan RA (2021) TLR10 and its role in immunity. Handb Exp Pharmacol. https://doi.org/10.1007/164_2021_541

    Article  PubMed  Google Scholar 

  21. Zhang Y, Cao R, Ying H, Du J, Chen S, Wang N et al (2018) Increased expression of TLR10 in B cell subsets correlates with disease activity in rheumatoid arthritis. Mediators Inflamm. https://doi.org/10.1155/2018/9372436

    Article  PubMed  PubMed Central  Google Scholar 

  22. Su SB, Tao L, Deng ZP, Chen W, Qin SY, Jiang HX (2021) TLR10: insights, controversies and potential utility as a therapeutic target. Scand J Immunol 93:e12988. https://doi.org/10.1111/sji.12988

    Article  CAS  PubMed  Google Scholar 

  23. Enevold C, Oturai AB, Sørensen PS, Ryder LP, Koch-Henriksen N, Bendtzen K (2009) Multiple sclerosis and polymorphisms of innate pattern recognition receptors TLR1-10, NOD1-2, DDX58, and IFIH1. J Neuroimmunol 212:125–131. https://doi.org/10.1016/j.jneuroim.2009.04.008

    Article  CAS  PubMed  Google Scholar 

  24. Milo R, Miller A (2014) Revised diagnostic criteria of multiple sclerosis. Autoimmun Rev 13:518–524. https://doi.org/10.1016/j.autrev.2014.01.012

    Article  PubMed  Google Scholar 

  25. Piri CB, Guven YY (2018) What we learned from the history of multiple sclerosis measurement: expanded disease status scale. Arch Neuropsychiatr 55:S69-75. https://doi.org/10.29399/npa.23343

    Article  Google Scholar 

  26. Kearse M, Moir R, Wilson A, Stones-Havas S, Cheung M, Sturrock S et al (2012) Geneious basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28:1647–1649. https://doi.org/10.1093/bioinformatics/bts199

    Article  PubMed  PubMed Central  Google Scholar 

  27. Aslani S, Jafari N, Javan MR, Karami J, Ahmadi M, Jafarnejad M (2017) Epigenetic modifications and therapy in multiple sclerosis. NeuroMolecular Med 19:11–23. https://doi.org/10.1007/s12017-016-8422-x

    Article  CAS  PubMed  Google Scholar 

  28. Baranzini SE, Oksenberg JR (2017) The genetics of multiple sclerosis: from 0 to 200 in 50 years. Trends Genet 33:960–970. https://doi.org/10.1016/j.tig.2017.09.004

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Hollenbach JA, Oksenberg JR (2015) The immunogenetics of multiple sclerosis: a comprehensive review. J Autoimmun 64:13–25. https://doi.org/10.1016/j.jaut.2015.06.010

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Bakr NM, Hashim NA, El-Baz HAED, Khalaf EM, Elharoun AS (2021) Polymorphisms in proinflammatory cytokines genes and susceptibility to Multiple Sclerosis. Mult Scler Relat Disord 47:102654. https://doi.org/10.1016/j.msard.2020.102654

    Article  PubMed  Google Scholar 

  31. Al-Naseri MA, Salman ED, Ad’hiah AH (2019) Association between interleukin-4 and interleukin-10 single nucleotide polymorphisms and multiple sclerosis among Iraqi patients. Neurol Sci 40:2383–2089. https://doi.org/10.1007/s10072-019-04000-4

    Article  PubMed  Google Scholar 

  32. Zhang Y, Liu J, Wang C, Liu J, Lu W (2021) Toll-like receptors gene polymorphisms in autoimmune disease. Front Immunol 12:672346. https://doi.org/10.3389/fimmu.2021.672346

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Deeba E, Koptides D, Lambrianides A, Pantzaris M, Krashias G, Christodoulou C (2019) Complete sequence analysis of human toll-like receptor 3 gene in natural killer cells of multiple sclerosis patients. Mult Scler Relat Disord 33:100–106. https://doi.org/10.1016/j.msard.2019.05.027

    Article  PubMed  Google Scholar 

  34. Dominguez-Mozo MI, Garcia-Montojo M, López-Cavanillas M, De Las HV, Garcia-Martinez A, Arias-Leal AM et al (2014) Toll-like receptor-9 in Spanish multiple sclerosis patients: an association with the gender. Eur J Neurol 21:537–540. https://doi.org/10.1111/ene.12209

    Article  CAS  PubMed  Google Scholar 

  35. Marques H, Freitas J, Medeiros R, Longatto-Filho A (2016) Methodology for single nucleotide polymorphism selection in promoter regions for clinical use. An example of its applicability. Int J Mol Epidemiol Genet 7:126–36

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Theodoropoulos GE, Saridakis V, Karantanos T, Michalopoulos NV, Zagouri F, Kontogianni P et al (2012) Toll-like receptors gene polymorphisms may confer increased susceptibility to breast cancer development. Breast 21:534–538. https://doi.org/10.1016/j.breast.2012.04.001

    Article  PubMed  Google Scholar 

  37. Greene JA, Sam-Agudu N, John CC, Opoka RO, Zimmerman PA, Kazura JW (2012) Toll-like receptor polymorphisms and cerebral malaria: TLR2 Δ22 polymorphism is associated with protection from cerebral malaria in a case control study. Malar J 11:47. https://doi.org/10.1186/1475-2875-11-47

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Takahashi M, Chen Z, Watanabe K, Kobayashi H, Nakajima T, Kimura A et al (2011) Toll-like receptor 2 gene polymorphisms associated with aggressive periodontitis in japanese. Open Dent J 5:190–194. https://doi.org/10.2174/1874210601105010190

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Karaali ZE, Candan G, Aktuğlu MB, Velet M, Ergen A (2019) Toll-like receptor 2 (TLR-2) gene polymorphisms in type 2 diabetes mellitus. Cell J 20:559–63. https://doi.org/10.22074/cellj.2019.5540

    Article  Google Scholar 

  40. Mandal RK, George GP, Mittal RD (2012) Association of Toll-like receptor (TLR) 2, 3 and 9 genes polymorphism with prostate cancer risk in North Indian population. Mol Biol Rep 39:7263–7269. https://doi.org/10.1007/s11033-012-1556-5

    Article  CAS  PubMed  Google Scholar 

  41. Cai Y, Peng Y-H, Tang Z, Guo X-L, Qing Y-F, Liang S-H et al (2014) Association of Toll-like receptor 2 polymorphisms with gout. Biomed Reports 2:292–296. https://doi.org/10.3892/br.2014.224

    Article  CAS  Google Scholar 

  42. Aflouk Y, Inoubli O, Saoud H, Zaafrane F, Gaha L, Bel BHJ (2021) Association between TLR2 polymorphisms (− 196–174 Ins/Del, R677W, R753Q, and P631H) and schizophrenia in a Tunisian population. Immunol Res 69:541–52. https://doi.org/10.1007/s12026-021-09238-9

    Article  CAS  PubMed  Google Scholar 

  43. Zhu Z, Lee PH, Chaffin MD, Chung W, Loh PR, Lu Q et al (2018) A genome-wide cross-trait analysis from UK Biobank highlights the shared genetic architecture of asthma and allergic diseases. Nat Genet 50:857–864. https://doi.org/10.1038/s41588-018-0121-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Nieuwenburg SAV, Mommersteeg MC, Eikenboom EL, Yu B, den Hollander WJ, Holster IL et al (2021) Factors associated with the progression of gastric intestinal metaplasia: a multicenter, prospective cohort study. Endosc Int Open 09:E297-305. https://doi.org/10.1055/a-1314-6626

    Article  CAS  Google Scholar 

  45. Li Y, Oosting M, Smeekens SP, Jaeger M, Aguirre-Gamboa R, Le KTT et al (2016) A functional genomics approach to understand variation in cytokine production in humans. Cell 167:1099-1110.e14. https://doi.org/10.1016/j.cell.2016.10.017

    Article  CAS  PubMed  Google Scholar 

  46. Allen AS, Satten GA (2005) Robust testing of haplotype/disease association. BMC Genet 6:S69. https://doi.org/10.1186/1471-2156-6-S1-S69

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The kind cooperation of the medical staff in the MS clinic of the General Hospital for Neurosciences (Baghdad) is greatly appreciated by the authors.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Biology, College of Science, University of Baghdad, Baghdad, Iraq

    Noor S. Atiyah & Hula Y. Fadhil

  2. Tropical-Biological Research Unit, College of Science, University of Baghdad, Baghdad, Iraq

    Ali H. Ad’hiah

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  1. Noor S. Atiyah
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  2. Hula Y. Fadhil
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  3. Ali H. Ad’hiah
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Contributions

NSA and HYF contributed to laboratory work, data handling, writing and revising the manuscript. AHA managed data, carried out statistical analyses and wrote the manuscript. All authors read and approved the final manuscript.

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Correspondence to Ali H. Ad’hiah.

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The participants provided their written informed consent to be included in the study. The study protocol was approved by The Ethics Committee of the Department of Biology (College of Science, University of Baghdad) and the Iraqi Ministry of Health and Environment.

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Atiyah, N.S., Fadhil, H.Y. & Ad’hiah, A.H. Toll-like receptor 10 gene polymorphism and risk of multiple sclerosis among Iraqi patients. Egypt J Med Hum Genet 23, 88 (2022). https://doi.org/10.1186/s43042-022-00301-0

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